1436 results about "Frequency noise" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The invention provides a civil construction monitoring intelligent sensor information detection method and system under signal mismatch, and the method comprises the steps: carrying out the time-frequency analysis of temperature and strain fundamental frequency components, selecting a wavelet basis function adaptive to the concrete setting and strength increasing process, carrying out the self-adaptive threshold processing of a wavelet coefficient, removing high-frequency noise, and carrying out the detection of the temperature and strain fundamental frequency components. Low-frequency signal characteristics reflecting concrete strength increase are reserved; performing feature extraction on the denoised temperature and strain signals by adopting a singular value decomposition algorithm, detecting internal defects of the concrete through abrupt change of a singular value spectrum, and if crack and hole defects exist, causing strain signal abrupt change by stress concentration; if it is detected that defects exist in the concrete, that is, spatial heterogeneity of temperature and stress distribution exists, temperature and strain monitoring data of multi-point arrangement and a defect detection result serve as boundary conditions and constraints, and a temperature field and a stress field in the whole concrete structure are obtained through numerical simulation.

The invention relates to the technical field of computer vision and pattern recognition, in particular to a deep-forged face image detection method based on a double-flow CNN and ViT hybrid model. The method comprises the following steps: preprocessing a real video and a forged video to obtain an original face image; obtaining a high-frequency noise residual error feature image of the original face image based on an improved high-frequency noise residual error feature extraction module; performing first feature enhancement operation on the original face image and the high-frequency noise residual feature image based on a double-flow CNN; carrying out secondary feature enhancement operation on the original face image and the high-frequency noise residual feature image which are subjected to the primary feature enhancement operation on the basis of double-flow ViT; and fusing the features of the original face image and the high-frequency noise residual feature image after the secondary feature enhancement operation, performing true and false prediction on the fused features, and outputting a detection result. The objective of the invention is to solve the technical problems of feature splitting, local-global imbalance and insufficient cross-modal interaction in the prior art.

The invention relates to the technical field of gallium nitride gate drivers, and discloses a design method and system of a gallium nitride gate driver, and the method comprises the steps: carrying out the working signal collection of the gallium nitride gate driver, obtaining original waveform sampling data, and building a signal integrity model under the action of electromagnetic interference; separating the original waveform sampling data into an effective driving signal, a low-frequency interference signal and a high-frequency noise signal, and extracting characteristic parameters to obtain a signal characteristic parameter set; switching time and circuit power consumption are set as optimization variables, constraint conditions are set according to the signal integrity model, and an optimization objective function is obtained; performing online identification and optimization calculation on the driving parameters to obtain a driving control parameter sequence; according to the method, the adaptive capacity of the driver to the change of the working condition is improved, and meanwhile, the stability and the reliability of the driving waveform are ensured.

The invention relates to the technical field of fault detection of the Internet of Things, in particular to a valve path detection method and system based on the Internet of Things, and the method comprises the following steps: based on real-time data of a valve path, removing high-frequency noise and high-frequency components in temperature fluctuation through a low-pass filter, carrying out the mean value smoothing operation of pressure data, and carrying out the mean value smoothing operation; and performing data standardization conversion on the flow data to obtain valve operation state data. According to the method, through real-time collection and optimization processing of valve operation data, it is ensured that the data is accurate and stable, weighted calculation is carried out in combination with the data influence degree, the representativeness of state feature extraction and the perception ability of working condition changes are enhanced, and based on state feature trend analysis, the sensitivity of fault detection is improved; and health state evaluation is carried out in combination with the operation duration and the load change, so that health state scoring is realized, the predictability of the long-term operation state is improved, false alarms and missing alarms are reduced, and the accuracy of fault early warning and the equipment health monitoring capability are enhanced.

The invention relates to the technical field of numerical control machine tool cutter state monitoring, and discloses an intermittent turning cutter service life monitoring and evaluating system based on multi-sensor fusion. The detection and evaluation system is composed of a data acquisition module, a noise data preprocessing module, a noise feature analysis and mode recognition module, a tool life prediction module and a multi-source data fusion module. According to the method, noise, vibration and temperature data are integrated, a weighted average algorithm is adopted, noise interference of a single sensor is reduced, the accuracy of cutter state evaluation is improved, filtering, smoothing and signal enhancement technologies are used, high-frequency noise is removed, data fluctuation is smoothed, useful signals are enhanced, data quality is improved, and data of all the sensors are standardized; the weight is calculated according to the reliability and the precision, weighted average fusion is carried out, fused high-quality data is generated, and the beneficial effect that errors caused by interference caused by noise in service life monitoring during cutting of the cutter are avoided is achieved.

The invention discloses an intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency domain separation, and provides a synchronous parameter acquisition and frequency domain separation processing scheme for solving the problems of temperature detection lag, insufficient frequency domain interference processing and weak dynamic adaptation capability in the prior art. Transient current, voltage and change rate (a first parameter group) of an electromagnetic coil are synchronously acquired, and high-frequency fluctuation (greater than or equal to 10kHz) and low-frequency disturbance (1t, 1t) are converted into low-frequency fluctuation (greater than or equal to 10kHz) by combining environment temperature, cookware materials and user preferences (a second parameter group); 10kHz) is used for frequency spectrum separation. Phase difference features are extracted through band elimination filtering and FFT, heating priorities are corrected in combination with dynamic compensation factors, and a segmented heating curve is generated through time-frequency domain weighted fusion. And the system adjusts PWM parameters in real time, and triggers adaptive protection when detecting that the phase is abnormal or the power frequency exceeds the limit. According to the method, the limitation of single-frequency-band detection is broken through, high-frequency noise suppression and low-frequency disturbance compensation are realized, and accurate temperature control of ferromagnetic / non-ferromagnetic cookware is supported.

The invention relates to the technical field of filters, in particular to an internal shielding component of a power filter, which comprises a shell, a cover plate is mounted at the top of the shell through a cross recessed countersunk head screw, and a metal partition plate is arranged on one side of the inner wall of the shell; the wave-proof sleeve is welded on one side wall of the shell, and the material of the wave-proof sleeve is light nickel-plated copper fabric; when the power supply filter is used, the problem that crosstalk coupling is formed between interference signals can be prevented, the electromagnetic compatibility in the signal transmission process between systems is solved, the anti-wave sleeve is adopted for shielding power supply input of the power supply filter, interference of an input cable on other signals is greatly restrained, and the service life of the power supply filter is prolonged. The electromagnetic compatibility problem of the whole system caused by the fact that high-frequency noise is coupled to a power line is prevented, the shielding effectiveness is improved, the problem of filtering post-stage electromagnetic interference signals is greatly restrained, meanwhile, the heat dissipation area is increased, and the heat dissipation efficiency and effect 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.

The invention discloses a bimodal neural network and spectral subtraction combined underwater acoustic signal noise suppression method. Comprising the following steps: firstly, constructing a simulation underwater sound data set containing self-noise; thirdly, constructing a bimodal neural network, then training the bimodal neural network until training is completed, and obtaining a platform self-noise estimation model; inputting a to-be-processed noise-containing underwater acoustic signal into the platform self-noise estimation model, and outputting estimation platform self-noise by the model; and finally, based on the self noise of the estimation platform, noise reduction processing is carried out on the noise-containing underwater acoustic signal to be processed by using the improved spectral subtraction method, and then a noise-reduced underwater acoustic signal is obtained. The method combines an advanced deep learning method and a classical spectral subtraction algorithm, has the capabilities of autonomous learning and efficient noise reduction, remarkably improves the signal-to-noise ratio of the underwater acoustic signal, is suitable for noise reduction of the underwater acoustic signal in a non-stationary and complex marine environment, remarkably improves the low-frequency noise suppression capability and the target signal fidelity, and has a good application prospect. The method is suitable for underwater acoustic target detection, underwater acoustic communication and other scenes.

The invention discloses a method and system for estimating the two-dimensional attitude of an occlusion scene based on spatio-temporal information, and relates to the field of computer vision, and the method comprises the steps: S1, obtaining a video comprising information of a plurality of human bodies; s2, constructing a two-dimensional attitude estimation model; the model comprises an input module, a visibility prediction and shielding module, a key point reasoning module, a space-time dependency module and a regression layer. S3, inputting the obtained video into a two-dimensional attitude estimation model for training to obtain a trained two-dimensional attitude estimation model; and S4, using the trained two-dimensional attitude estimation model to carry out space-time information-based occlusion scene two-dimensional attitude estimation. According to the two-dimensional attitude estimation model constructed by the method, shielded key points can be accurately reasoned, the smoothness and accuracy of attitude estimation are improved, the interference of high-frequency noise is reduced, and the calculation complexity is reduced while the accuracy of the model is ensured, so that the real-time requirement is met.

The invention discloses an underwater thruster permanent magnet propeller capable of reducing cavitation noise, and relates to the technical field of propellers, the underwater thruster permanent magnet propeller comprises a mounting plate, a propeller body and a fairing; mounting holes are formed in the mounting plate, the mounting plate is fixedly mounted on the underwater propeller through matching of bolts and the mounting holes, a main shaft is rotationally connected to the middle of one end face of the mounting plate, the fairing is fixedly connected to one end face of the mounting plate, and the propeller body is fixedly mounted at the end, away from the mounting plate, of the main shaft. The vibration reeds are installed on the inner wall of the flow guide cover, the vibration reeds can vibrate at a high speed through vortexes generated by the cavitation bubbles, and therefore the cavitation bubbles large in size are scattered into a larger number of small cavitation bubbles, the noise decibel generated by the larger number of small cavitation bubbles is lower than the noise decibel generated by the large cavitation bubbles when the larger number of small cavitation bubbles are broken, and the frequency is higher than the noise decibel generated by the large cavitation bubbles when the larger number of small cavitation bubbles are broken. Due to the fact that the wavelength of the high-frequency noise is short, the high-frequency noise is easy to absorb and scatter, one part of the high-frequency noise is directly absorbed by the flow guide cover, and the underwater environment can restrain propagation of the high-frequency noise.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention relates to the technical field of atmospheric remote sensing, in particular to a satellite-borne laser radar-based multi-feature attention cloud top height estimation algorithm, which comprises the following steps of: full-waveform laser radar data acquisition and preprocessing: acquiring an original echo signal through a satellite-borne full-waveform laser radar system; performing time domain alignment, noise suppression and saturated signal elimination on the signals; a median filtering and wavelet threshold denoising algorithm is combined to eliminate high-frequency noise, and background radiation interference is eliminated through dynamic baseline correction; according to the method, a multi-stage noise reduction strategy combining sliding window median filtering and self-adaptive wavelet denoising is introduced in the waveform feature extraction stage, pulse interference and signal oscillation are effectively restrained, meanwhile, through a terrain matching error compensation mechanism, the detection accuracy is improved, and the detection accuracy is improved. The physical consistency of the elevation residual error under the real terrain background is enhanced, and the authenticity and the checkability of the point elevation return are improved.

The invention relates to the technical field of power systems, in particular to a transformer substation low-frequency noise source positioning method. The method comprises the following steps: obtaining a three-dimensional environment model of each structure in a transformer substation, and obtaining a geometric body topological relation matrix in the three-dimensional environment model; performing field measurement on the transformer substation to obtain noise measurement data; and performing outer convolution fusion on noise images between adjacent units according to the geometric topological relation matrix and the local noise image data, and iteratively spreading the noise images to the whole transformer substation area to generate initial sound source distribution estimation data of the whole transformer substation. The sound energy flow is utilized to analyze and identify the source position, the static source and the moving source are separated and tracked in combination with direction estimation and trajectory reconstruction, the static source can be accurately positioned, meanwhile, the motion trajectory of the moving source is obtained, and the separation efficiency is improved.

The invention provides a resonator phase noise suppression method based on Kalman filtering. The resonator phase noise suppression method comprises the following implementation steps: sampling displacement of a resonator; calculating a priori error matrix and a priori estimation value of each sampling; calculating the Kalman gain of each sampling; calculating an error matrix of each sampling; and obtaining a resonator phase noise suppression result. According to the method, suppression of the resonator phase noise in the mechanical resonant device is realized based on Kalman filtering, and the method is suitable for various noise types including high-frequency noise, can flexibly cope with different working environments and noise types, and has relatively good adaptability; based on the recursive calculation characteristic of Kalman filtering, state estimation can be updated every time new data arrives, processing of all historical data is not needed, meanwhile, dynamic estimation and optimization are carried out on input signals in combination with a prediction model and noise observation, a filter can rapidly respond to the change of signal frequency, and the accuracy and the reliability of the method are improved. And an efficient real-time adjustment capability is provided.

The invention relates to the technical field of signal adaptive noise reduction, and provides a PPG signal adaptive noise reduction method based on wavelet transform and an auto-encoder. The method combines adaptive variational mode decomposition, stationary wavelet denoising, multi-dimensional consistency optimization, a synchronous updating technology and a Lagrangian multiplier constraint adjustment technology to remove high-frequency noise of PPG signals, and can better adapt to individual differences of drivers and complexity of the signals; the total loss function of the auto-encoder is optimized by adopting contrast loss, weighted reconstruction error and sparsity regularization, so that the sensitivity and the judgment capability of an abnormal health state are enhanced; according to the method, the PPG signals can be accurately restored and evaluated in multiple dimensions, reliable technical support is provided for continuous monitoring of the health state of the driver, high stability and expandability are achieved, and important guarantee can be provided for safe driving of vehicles and health management of the driver.

The invention relates to the field of power distribution network ground fault accurate judgment, and discloses a power distribution network ground fault accurate positioning method based on traveling wave detection and artificial intelligence, which comprises the following steps: (a) collecting a power distribution network traveling wave voltage signal through a multi-node synchronous sensor array, and generating a preprocessing signal through baseline correction and wavelet threshold denoising; (b) constructing a Gaussian random field model of line resistance, inductance and capacitance, and simulating a traveling wave propagation process in combination with a nonlinear wave equation; and (c) defining Finsler measurement on the traveling wave signal manifold, and separating the aliasing mode by minimizing path integration. According to the invention, the technical scheme of multi-node synchronous signal acquisition and self-adaptive preprocessing is adopted, so that the effect of eliminating coupling of power frequency interference and high-frequency noise is achieved. Compared with a scheme depending on a single sensor and a fixed filtering threshold in the prior art, the method solves the defect that the wave head time difference extraction is not accurate due to asynchronous sampling.

The invention discloses a chromatographic signal data processing method and device, equipment, a medium and a product, and relates to the technical field of data processing.The method comprises the steps that an original chromatographic signal is obtained; a Savitzky-Golay filter is adopted to filter out high-frequency noise in the original chromatographic signal, and a de-noised signal is obtained; fitting a base line of the de-noised signal by adopting cubic spline interpolation; deducting the baseline from the de-noised signal to obtain a corrected signal; and performing peak identification on the corrected signal through a symmetric zero area method to generate an identification result. According to the invention, the accuracy of chromatographic signal identification can be 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 provides a distribution network type lightning arrester leakage current optimization measurement method. The method comprises the steps of signal acquisition, preliminary signal denoising and preprocessing, wavelet transform signal decomposition, high-frequency noise component elimination and processing, input convolutional neural network CNN descrambling, integrated learning model optimization, output of descrambled leakage current signals, performance evaluation and result feedback. Through a multi-level processing method combining wavelet transform and a convolutional neural network, high-frequency noise in a weak leakage current signal is effectively removed, effective characteristics of the signal are reserved in the denoising process, and compared with a traditional method depending on a single signal processing technology, signal descrambling can be more accurate and effective, and the signal descrambling efficiency is improved. According to the wavelet transform signal decomposition method, the signal can be decomposed into components with different frequencies, high-frequency noise components are effectively eliminated, low-frequency and intermediate-frequency effective signals are reserved, then the convolutional neural network further improves the definition and reliability of the signal through depth feature extraction, and accurate detection of the leakage current signal is ensured.

The invention belongs to the technical field of physiological state estimation, and discloses a blood oxygen estimation method based on combination of successive variational mode decomposition and adaptive filtering, which comprises the following steps of: removing high-frequency noise and baseline drift from a collected pulse wave signal; performing successive variational mode decomposition and adaptive filtering on the pulse wave signal to remove motion artifacts; accurate peak values and valley values are extracted according to the denoised pulse waves; the R value is calculated according to the denoised pulse wave and the extracted peak value and valley value, the blood oxygen saturation degree is solved according to the R value, and the R value refers to the absorbance ratio of the red light and the infrared light in the blood. According to the blood oxygen estimation method based on the combination of the successive variational mode decomposition and the self-adaptive filtering, the effect of removing motion artifact noise of photoelectric volume pulse wave signals is achieved, multi-peak-value joint detection is used, peak values and valley values are accurately extracted, and therefore the R value is determined, the blood oxygen saturation degree is obtained, and the accuracy of blood oxygen estimation is improved. And the precision of estimating the blood oxygen saturation is greatly improved.

The invention discloses a pipeline high-frequency silencer based on acoustic mode modulation and a perforated pipe. The pipeline high-frequency silencer comprises a shell, and the interior of the shell is connected with a cylindrical inner pipe through a plurality of partition plates; the partition plates are parallel to the axial direction of the inner pipe and evenly arranged in the circumferential direction of the inner pipe. The partition plates divide an annular cavity formed between the shell and the inner pipe into a plurality of sub-cavities of the same size. Flanges are connected to two ends of the shell. The invention further discloses a design method of the pipeline high-frequency silencer based on acoustic mode modulation and the perforated pipe. The design method comprises a perforated design method of the inner layer of the shell and / or the inner layer of the inner pipe and a design method of the partition plate. According to the pipeline high-frequency silencer based on acoustic mode modulation and the perforated pipe, the problem that in the prior art, a sound absorption material which is prone to losing efficacy and harms human health is used for achieving high-frequency noise suppression is solved. High-frequency noise elimination is achieved through the partition plate and the perforated pipe, the structure is simple, and disassembly and replacement are convenient.

The invention discloses a time stretching sweep frequency interference high-precision distance measurement method and device based on double auxiliary interferometers, and relates to the technical field of non-cooperative target absolute distance measurement. In order to solve the technical defects that in the prior art, due to initial wavelength fluctuation of a light source and frequency sweeping nonlinearity, drift of a ranging result is large, and interference signals are interfered by zero-frequency noise, the technical scheme provided by the invention comprises the following steps: acquiring linear frequency modulation optical signals output by a common-source frequency sweeping laser; the signals are respectively transmitted to a measurement interferometer, an auxiliary interferometer and a resampling auxiliary interferometer to form a homologous three-interference structure; a dispersive medium is introduced through the reference arm, so that the reference light generates time stretching and interferes with the measurement light to form a chirp beat frequency signal; k-domain resampling and cross-correlation processing are carried out on the beat frequency signals, and target distance information is extracted. The method is suitable for high-precision and wide-range non-cooperative target absolute distance measurement work, and is especially suitable for the fields of laser radar three-dimensional modeling, precision industrial detection, robot calibration and the like.

The invention discloses an IV-type hydrogen cylinder stress monitoring system, a signal processing method thereof and an IV-type hydrogen cylinder, and relates to the technical field of manufacturing of high-pressure hydrogen storage equipment. The carbon fiber winding layer stress monitoring structure and the liner stress monitoring structure are respectively arranged in a strain generation area of a carbon fiber winding layer and a strain generation area of the outer surface of a plastic liner in the IV-type hydrogen cylinder, so that the surface stress of the liner of the gas cylinder and the stress between carbon fiber winding layers can be dynamically monitored in real time; therefore, the IV-type hydrogen cylinder has the function of dynamically monitoring the stress between the carbon fiber winding layers and the stress of the inner container in real time. According to the signal processing method of the IV-type hydrogen cylinder stress monitoring system, high-frequency noise signals can be effectively filtered, low and medium-frequency signals can be reserved, and the surface stress of a carbon fiber winding layer and a plastic inner container can be accurately measured. The IV-type hydrogen cylinder is provided with the IV-type hydrogen cylinder stress monitoring system and has the function of dynamically monitoring the stress between carbon fiber winding layers and the stress of an inner container in real time.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of permanent magnet synchronous motors, in particular to a method, device and equipment for reducing vibration noise of a permanent magnet synchronous motor, and the method comprises the steps: building an electromagnetic field model, extracting radial electromagnetic force data, carrying out the spectrum and modal analysis, recognizing a main noise source, generating an acoustic power level waterfall plot through the combination of acoustic simulation, and defining an optimization target. The optimal scheme is finally obtained by designing the shape of a rotor hole, adjusting multi-parameter collaborative optimization of stator slot width and air gap length and the like, and iteratively optimizing key variables by adopting an intelligent algorithm. According to the method, the space 0-order quadruplicated frequency noise and 24-order octupled frequency noise are remarkably reduced, the torque fluctuation is inhibited, the output performance is ensured, the method is suitable for large-scale production, and the NVH quality of the motor is effectively improved.