200 results about "Noise spectrum" patented technology

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.

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 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 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 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 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 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.

The invention relates to the technical field of grounding detection, and discloses a frequency modulation one-way grounding detection system based on frequency adjustment, which comprises a signal generator module used for generating frequency-adjustable sine wave or square wave detection signals, and the frequency adjustment range is 1 Hz to 1 kHz; the self-adaptive frequency modulation control module is internally provided with an LSTM neural network algorithm, collects power grid background noise in real time, generates a 0-500Hz noise spectrogram through fast Fourier transform, and dynamically outputs an interference avoidance frequency band and an optimal detection frequency set; a multi-frequency orthogonal coding module; a dynamic power distribution module; a signal acquisition and processing module; and a fault positioning module. Through a dynamic adaptive frequency modulation technology of an LSTM neural network, by analyzing a power grid noise spectrum in real time, an optimal detection set of a main frequency and an auxiliary frequency is dynamically generated, frequency decision logic is optimized by using closed-loop feedback, sudden interference frequency bands such as lightning stroke and load abrupt change are actively avoided, and thus the real-time performance and reliability of fault detection are improved.

The invention provides a hearing aid intelligent protection device and method integrated on a headgear, and the method comprises the steps: converting an environment sound wave signal into a frequency domain signal through a signal processor in an interlayer of the headgear, and separating a noise dominant frame and a voice dominant frame in a hearing aid; determining a voice masking index between the environmental noise and the voice in the environmental sound wave signal according to a distribution association relationship between the noise dominant frame and the voice dominant frame; according to the energy distribution characteristics of the noise spectrum in the frequency domain signal, determining the noise interference suffered by the hearing aid in the current wearing scene, and determining the transient noise dynamic characteristics of the environment sound wave signal under the current noise interference; and performing disturbance trend prediction on voice signal noise in the hearing aid according to the transient noise dynamic characteristics and the voice masking index, and further updating an adaptive filtering coefficient of the hearing aid according to a prediction result. By adopting the scheme of the invention, the prediction of the noise disturbance trend in a complex and changeable environment can be realized so as to adaptively adjust the noise reduction filter coefficient of the hearing aid.

The application relates to the technical field of system-in-package, and discloses a functional chip SIP (System in Package) system-in-package method and system. The method constructs a reduced-order discrete thermal state predictor through multi-order exponential attenuation fitting, generates a DVFS gear-noise hazard degree spectrum mapping table based on a synchronous switching noise spectrum and an ADC noise sensitivity curve, generates a multi-DVFS working condition robust grounding network topology by optimizing a narrow bridge connection structure parameter by using a sequential quadratic programming, quantizes equivalent noise interference amounts of each DVFS gear on an ADC chip, and jointly optimizes DVFS gear selection and power consumption upper limit distribution under a rolling time domain mixed integer quadratic programming framework, simultaneously performs online model correction through exponential weighted moving average and Kalman filtering, and realizes cooperative satisfaction of thermal constraints and noise constraints.

The invention relates to the technical field of mining machinery intelligent monitoring, and discloses a remote monitoring system and method based on a crusher. According to the system, an original current waveform and a bearing voiceprint signal in a starting stage are collected through an initial state sensing unit, a current waveform deviation spectrum is generated, sound energy of a specific frequency band is extracted, and an initial state data set is formed through fusion; the dynamic operation analysis unit monitors a power fluctuation period, a torque change slope and a material flow noise spectrum in a steady state, and generates a real-time operation state flow; the self-adaptive regulation and control unit identifies an abnormal correlation mode of power and torque, starts fine adjustment of the feeding speed when the torque slope exceeds a threshold, and dynamically adjusts an instruction according to real-time feedback of noise characteristic frequency band energy. According to the scheme, early diagnosis of hidden faults of the equipment and predictive smooth control of the material blocking risk are realized, and the equipment reliability and the production continuity are improved.

This application discloses a wind tunnel-based noise detection method, apparatus, storage medium, and electronic device. The method includes: when testing a test piece in a wind tunnel, acquiring a measured noise spectrum in the testing environment and obtaining the operating frequency of the wind tunnel's inverter; matching the background noise spectrum corresponding to the operating frequency in a background noise spectrum library based on the operating frequency; aligning and dividing the measured noise spectrum and the background noise spectrum using a frequency band division rule adapted to the inverter's operating frequency to obtain multiple frequency band units; based on the acoustic energy density of the measured noise spectrum and the background noise spectrum within each frequency band unit, extracting the energy component corresponding to the background noise spectrum from the measured noise spectrum through directional orthogonal projection, and reconstructing the spectrum of the residual energy after extraction to obtain the effective aerodynamic noise spectrum of the test piece; and calculating the noise detection result of the test piece based on the effective aerodynamic noise spectrum.

A method and a decoder for generating concealment audio frame of an audio signal. The method includes performing a frequency domain analysis of a sequence of previously decoded audio signal to obtain a frequency spectrum and identifying peaks in the spectrum. The method further includes estimating a relative energy between the noise spectrum and the complete spectrum, determining an attenuation of the noise spectrum based on the relative energy, and applying the attenuation to the noise spectrum. The method includes applying an inverse transform to time domain on an error concealment spectrum, that includes the peaks and the attenuated noise spectrum.

The application discloses a noise reduction unit, which comprises a magneto-rheological noise reduction layer, an enclosed shell filled with magneto-rheological fluid, an iron core wrapped outside the enclosed shell, and a conductive coil wound outside the iron core, and an upper substrate and a lower substrate arranged on the upper and lower sides of the magneto-rheological noise reduction layer respectively. The application also discloses a noise reduction unit and a control method. The noise reduction unit has simple structure, strong versatility, and is suitable for standardization and mass production. The noise reduction unit can be conveniently adjusted according to the shape of the noise reduction target, the environment and other factors. The magneto-rheological noise reduction layer has good sound insulation and noise reduction effect on the wide frequency band and the high noise of the medium and low frequency of the industrial complex environment noise spectrum, and can adjust the noise reduction performance in real time according to the change of the noise spectrum characteristics caused by the production conditions switching and the like, so as to effectively reduce the noise exposure level of workers and effectively reduce the occupational health damage caused by high noise.

A method and a decoder for generating concealment audio frame of an audio signal. The method includes performing a frequency domain analysis of a sequence of previously decoded audio signal to obtain a frequency spectrum and identifying peaks in the spectrum. The method further includes estimating a relative energy between the noise spectrum and the complete spectrum, determining an attenuation of the noise spectrum based on the relative energy, and applying the attenuation to the noise spectrum. The method includes applying an inverse transform to time domain on an error concealment spectrum, that includes the peaks and the attenuated noise spectrum.

The application discloses a low-noise tire pattern pitch optimization method and device based on image processing and a medium, and the method comprises the following steps: S1, processing initial pattern information and calculating pitch length; S2, extracting groove boundaries and tire mark boundaries of a pattern pitch graph; S3, inputting basic parameters such as pitch and mark; S4, introducing an A-weighting filter for acoustic weighting; S5, calculating an initial noise spectrum; S6, optimizing a spectrum peak value in a harmonic range; S7, iteratively adjusting a pitch ratio and quantity until noise energy meets a threshold value, and outputting an optimal pitch sequence. Through the fusion of image processing technology and acoustic analysis, the pattern pitch design is systematically optimized, tire noise is significantly reduced, design efficiency is improved, and a research and development cycle is shortened.

The invention discloses a noise logging sound source point signal extraction method. The method comprises the following steps: acquiring noise spectrum data; global background noise is calculated after global stable background well section noise data is selected from the noise spectrum data, and the global background noise in the noise spectrum data is eliminated to obtain spectrum data without background noise; selecting a sound source layer section and a processing layer section from the spectrum data without background noise by using a half amplitude point method, and processing the spectrum data of the processing layer section through a sound conduction model to obtain conduction noise of the processing layer section; and processing layer section conduction noise in the spectrum data without background noise is eliminated to obtain stratum noise spectrum data, and extraction is completed.

This invention provides a method, device, electronic equipment, and storage medium for active noise reduction in vehicles. During vehicle operation, the method searches for a target amplitude in a pre-defined mapping relationship between the current motor speed and the target amplitude. A noise reduction signal is generated based on the current motor frequency, the target amplitude, and the reverse motor phase to cancel out gear-induced noise, thus actively reducing vehicle noise. This method eliminates the need to adjust the torque distribution between the front and rear electric drive systems. Instead, it queries the corresponding noise spectrum based on the current motor speed and plays the inverse noise reduction signal for active noise reduction. This improves the suppression of whistling noise under heavy loads. Because this invention does not require adjusting the torque distribution between the front and rear electric drive systems, it does not affect driving safety.

The invention relates to the technical field of server hardware control, and discloses a server hardware acceleration control system based on noise temperature coupling. The system comprises a multi-source noise collector, a temperature control accelerator, a noise temperature coupling analyzer and an acceleration decision master control unit. The multi-source noise collector is deployed on the surface of the acceleration unit and an internal key node, and captures an electromagnetic radiation noise spectrum and a mechanical vibration noise waveform in real time; the temperature control accelerator integrates a thermoelectric refrigeration array and a voltage frequency modulation module, receives the temperature sensing data flow, and generates a pulse type refrigeration instruction and clock frequency correction according to a dynamic strategy; the coupling analyzer decomposes the multi-modal noise signal, extracts an energy feature vector, and constructs a coupling coefficient matrix; and after receiving the matrix operation, the acceleration decision-making unit outputs a frequency scaling factor and a voltage offset to the temperature control accelerator, feeds back a failure frequency band marking result update coefficient, and combines noise and temperature coupling characteristics to realize dynamic balance of hardware acceleration performance and stability.

This invention provides an electromagnetic noise analysis apparatus and method that achieves both reduced analysis time and improved analysis accuracy. [Solution] The electromagnetic noise analysis device 1 includes: a noise spectrum analysis unit that performs frequency-axis noise analysis on any observation point of a power conversion circuit in which at least two switches are connected between power supplies, representing each switch as a voltage source or current source on the frequency axis; a voltage source / current source noise source parameter generation unit that generates a parameter table for representing switching operation as a voltage source or current source on the frequency axis according to operating conditions including power supply voltage, gate resistance of each switch, or current; a worst-case condition extraction unit that extracts phase relationships and current amounts in which the noise amount can become the worst-case condition in the time-axis operation of each switch based on control; and a worst-case condition noise source model generation unit that sets the worst-case condition noise source parameters using the extraction results by the worst-case condition extraction unit.

The invention discloses an active sound production system and method for optimizing door closing sound quality, and relates to the field of simulation testing, a multi-dimensional sensing array module comprises a distance sensor, a three-axis acceleration sensor, a distributed microphone array and a temperature and humidity sensor, the sensors synchronously collect data, the data are transmitted through a CAN bus, and the timeliness and consistency of the data are ensured; the environmental acoustic modeling module is used for carrying out noise spectrum analysis on microphone signals, evaluating a vehicle door motion state, processing distance and acceleration data, calculating a temperature correction coefficient, dynamically adjusting sound pressure parameters, periodically updating an environmental parameter matrix and reflecting acoustic characteristics under different working conditions; and the PID-fuzzy hybrid algorithm module adopts a PID and fuzzy logic fusion framework, presets a target frequency, a maximum sound pressure level and a door closing speed threshold value, and calculates frequency, sound pressure and speed deviation, and the fuzzy logic module dynamically adjusts PID parameters according to the noise level, weights and superposes a proportional term, an integral term and a differential term, and is used for regulating and controlling the door closing sound quality.

The invention discloses an internal arteriovenous fistula portable intelligent auscultation instrument for hemodialysis, and relates to the technical field of medical equipment. An alarm, three auscultation result indicating lamps with different colors, an equipment switch and an electric quantity indicating lamp are arranged on the auscultation instrument main machine, an equipment power supply, a control module, a wireless data transmission module and an AI noise recognition chip are arranged in the auscultation instrument main machine, and an auscultation head and a conduction tube are arranged on the outer side of the auscultation instrument main machine. And an auxiliary fixing mechanism is arranged on the stethoscope head. A noise reduction acoustic filter screen is arranged in the auscultation head to accurately filter interference, a signal amplification module at the tail end of a conduction tube amplifies weak blood flow sound, an AI noise recognition chip with a clinical noise spectrum pre-recorded is matched, the internal fistula noise state can be automatically recognized without depending on professional auscultation experience, visual feedback is achieved through a three-color auscultation result indicator lamp, linkage early warning is matched with an alarm, and the internal fistula noise state can be automatically recognized. Common medical care can quickly complete clinical screening, and a patient can also perform self-examination at home, so that the risks of missed judgment and misjudgment are thoroughly avoided.