1420 results about "Low pass filtering" patented technology

The invention relates to the field of target detection, in particular to a UMFNet-YOLO-based joint detection algorithm under a low-light condition, which adopts a multi-scale decomposition and high-frequency-low-frequency feature collaborative enhancement strategy, separates image details from global features through high / low-pass filtering, realizes multi-resolution feature complementation in combination with dynamic weight distribution, and improves the detection accuracy. Target contour and texture information degraded in severe weather can be effectively recovered; a parallel channel compression and spatial semantic enhancement module is designed, rain and fog noise interference is suppressed through cross-dimensional adaptive feature screening, and significance expression of a key target is enhanced; an image enhancement network UMFNet is cascaded with YOLOv8, and strong correlation between feature expression and a detection task is ensured by synchronously optimizing an enhancement module and detecting network parameters through back propagation. Compared with a baseline YOLOv8 detector, the method has the advantages that the average precision (mAP50) is obviously improved, and the method is obviously superior to a traditional enhancement-detection separation scheme. The method can be applied to robust target detection scenes in complex environments such as automatic driving and security monitoring.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention provides a PPG big data driven biological feature recognition method, which comprises the following steps: acquiring PPG signal data of trainees, and performing high-pass filtering and low-pass filtering preprocessing on the PPG signal data to obtain preprocessed PPG signals; framing is carried out on the preprocessed PPG signal, a Mel frequency cepstral coefficient feature and a Gammatone frequency cepstral coefficient feature are extracted respectively, the Mel frequency cepstral coefficient feature and the Gammatone frequency cepstral coefficient feature are fused, then principal component analysis dimension reduction is carried out, and a fusion feature is obtained; a universal background model UBM of a Gaussian mixture model is constructed based on the fusion features, zero-order, first-order and second-order Baum-Welch statistics are calculated, a global difference space matrix is estimated from the Baum-Welch statistics, and i-vector identity authentication vectors are extracted; and inputting the i-vector identity authentication vector into a long short-term memory network for training and classification to obtain an identity recognition result of the trainees. According to the invention, the influence of motion artifacts can be eliminated, the overall variability of PPG signals is captured, and an identity authentication scheme is provided for wearable equipment.

The invention relates to an underwater cleaning robot positioning and tracking method. Positioning comprises the steps that data of multiple sensors on an underwater cleaning robot are fused through an extended Kalman filter, and coordinates of the underwater cleaning robot on the surface of a ship body are obtained after sensor measurement noise is filtered through a first-order low-pass filtering algorithm. The tracking method comprises the step of realizing the path tracking of the underwater cleaning robot based on the positioning method in combination with an iterative linear quadratic regulator (ILQR). According to the invention, a multi-sensor fusion scheme of the encoder, the IMU and the depth meter is innovatively provided, and the positioning problem of the underwater cleaning robot in a wall attaching state is solved. And double-plane differential treatment is adopted, so that the working state of the robot on the ship wall or the ship bottom is better fitted, and positioning is more accurate. A target point updating mechanism of geometric distance constraint is provided, the nearest neighbor point of the reference trajectory is adjusted in real time, and the problem of tracking lag caused by updating rate mismatch in a traditional method is solved.

The invention discloses an arm movement monitoring and health recognition method and system based on a wearable sensor. The method comprises the steps that original multi-axis signals in the arm movement process of a user are collected; carrying out analysis, calibration and low-pass filtering on the original multi-axis signal, and calculating a three-dimensional attitude angle and a motion track of an arm in real time by adopting an improved Kalman filtering algorithm; segmenting the continuous motion data into a standardized motion sequence by adopting a hybrid motion segmentation algorithm; performing time-frequency domain analysis on the standardized action sequence to obtain a time domain feature and a frequency domain feature; extracting attitude track features; the time domain features, the frequency domain features and the attitude track features are combined into a high-dimensional feature matrix, and low-dimensional feature vectors are generated after dimension reduction; and inputting the low-dimensional feature vector into a pre-trained health assessment model, and outputting an arm motion function score, abnormal type early warning and rehabilitation guidance. The system can realize remote guidance of doctors and household rehabilitation monitoring of patients, and has the advantages of high precision, low cost and high real-time performance.

The invention relates to the technical field of power electronics, in particular to an inverter load feed-forward compensation method and system based on dynamic hysteresis regulation. The method comprises the following steps: acquiring an instantaneous load current signal of an inverter system; wherein when the load current suddenly changes, the bus voltage on the direct current bus fluctuates; performing low-pass filtering on the instantaneous load current signal to extract a direct current component of the load current; determining a dynamic hysteresis limit value according to the instantaneous load current signal, the direct current component and the amplitude adjustment coefficient; comparing the instantaneous load current signal with a historical current feed-forward signal, updating the historical current feed-forward signal according to a comparison result and a dynamic hysteresis limit value, taking the updated historical feed-forward current signal as an updated feed-forward current signal of the current control period, generating a compensation current based on the updated feed-forward current signal, and outputting the compensation current to the current control period. And the compensation current is applied to the corresponding direct-current bus so as to suppress bus voltage fluctuation caused by load abrupt change.

The invention relates to the technical field of inverter power control, and discloses a given current-based inverter power control method and system, and the method comprises the steps: calculating a fluctuation compensation parameter based on a bus voltage, and carrying out the real-time compensation of a q-axis basic current, and obtaining a first given q-axis current; predicting the bus voltage of the next period and correcting the first given q-axis current in advance to obtain a second given q-axis current; on the basis of the stator voltage saturation state, weak magnetic d-axis current given is generated, low-pass filtering is carried out on the second q-axis current given and the weak magnetic d-axis current given, and a d-axis current instruction and a q-axis current instruction are obtained; a PWM driving signal corresponding to a dq-axis current instruction is generated, an inverter switch is controlled, meanwhile, a power deviation signal is collected to update q-axis basic current, accurate prediction of bus voltage and advanced correction of given current are achieved, compared with traditional lag compensation, the dynamic response is faster, and the power control precision and the operation stability are improved.

The invention provides an anti-interference sound signal data extraction method based on energy data perception, and belongs to the technical field of underwater sound signal processing, and the method comprises the steps: carrying out the A / D sampling and digital orthogonal demodulation of a received sound signal, and obtaining a baseband signal; performing low-pass filtering and downsampling on the baseband signal to obtain a preprocessed signal; performing matched filtering on the pre-processed signal by using a reference signal; the key improvement lies in that a local energy estimation function of a preprocessed signal is calculated, and normalization processing is carried out on a matched filtering result and the energy estimation function to obtain a normalized detection signal. The method effectively suppresses the shielding effect of a strong interference signal on a weak target signal under a non-orthogonal reference signal and a strong near-end and weak far-end scene, remarkably reduces the misrecognition probability, and improves the accuracy and reliability of the extraction of a long-distance and low-signal-to-noise-ratio target beacon signal in a complex underwater acoustic environment. The method is suitable for underwater navigation, positioning and communication systems.

The invention discloses a real-time power generation load estimation method and system for photovoltaic power generation, relates to the technical field of photovoltaic power generation load estimation, and aims to solve the problems of state judgment delay and model mismatching and misestimation caused by short-time cloud shadow rapid movement. The method comprises the following steps: collecting power, current, voltage, irradiance and temperature signals through a multi-array sensor, separating a high-frequency characteristic signal from a low-frequency characteristic signal through high-pass / low-pass filtering, and generating a static baseline multi-dimensional characteristic vector; based on the power spectrum entropy, adaptively adjusting a time window and extracting dynamic characteristics; fusing the static and dynamic characteristics to generate discrimination input; performing state judgment by adopting a logistic regression model, and starting extended Kalman filtering online correction when an energy residual exceeds a threshold; and model drift is detected based on CUSUM and Shewhart algorithms, and an incremental training set is automatically constructed to update model parameters. And high-precision and low-delay real-time power generation load estimation and operation and maintenance support under a short-time cloud shadow condition are realized.

The invention discloses a permanent magnet synchronous motor current dynamic coupling correction method, which comprises the following steps of collecting motor three-phase stator current, performing Clark transformation to obtain a current value under an alpha-beta static coordinate system, and meanwhile, collecting through a position sensor to obtain an electrical angle true value; performing low-pass filtering calculation on the current value under the alpha-beta static coordinate system to obtain a filtering current value, and performing operation through a sliding-mode observer and a PLL phase-locked loop to obtain an electrical angle observation value; calculating to obtain a compensation adjustment coefficient according to the true value and the observed value of the electrical angle, and further calculating to obtain a current value after phase offset compensation; and performing complementary filtering calculation on the filtering current value and the current value after phase offset compensation to obtain a coupling correction current value for current loop calculation, inputting the coupling correction current value into a motor control current loop PID algorithm, and generating a PWM driving signal to drive a motor in combination with vector control. According to the method, the problems of high-frequency interference and phase deviation of current sampling can be solved, so that the motor control efficiency and the dynamic performance are improved.

The invention relates to the technical field of impurity gas detection control, and discloses an impurity detection control method and system for high-purity oxygen, and the method comprises the steps: arranging a plurality of groups of TDLAS detection probes and temperature vibration monitoring units, and obtaining a spectrum harmonic signal and temperature and vibration data; the concentration is inverted through wavelength modulation spectrum demodulation and temperature and pressure disturbance compensation; pCA fusion denoising processing is executed through the edge calculation unit, and correction concentration is output; and abnormity is judged through a multi-scale dynamic threshold mechanism and linkage control is carried out. Compared with a common single-point probe or traditional low-pass filtering in the prior art, the technical problem that interference between impurity concentration change and environmental disturbance cannot be effectively handled especially in a high-vibration and high-temperature-difference environment is solved. According to the invention, through multi-channel cooperative weighting, PCA fusion compensation and a dynamic threshold determination mechanism, accurate detection control of the high-purity oxygen conveying pipeline in an industrial environment is realized, and the automation level of pipeline impurity gas detection control is improved.

The invention discloses an AC excitation system rotor initial position on-line identification method and system, and the method comprises the steps: collecting a stator three-phase voltage signal when a stator is no-load, carrying out the Clarke conversion of the signal, obtaining a voltage component under a static coordinate system, and carrying out the low-pass filtering processing; calculating a stator flux linkage based on the stator current and the processed voltage component, and calculating a rotor flux linkage based on the rotor current; the rotor initial position offset is continuously accumulated through closed-loop adjustment by calculating the stator and rotor flux linkage angle and the phase difference until the phase difference approaches zero, and the accumulated rotor initial position offset is output; and combining the mechanical angle output by the encoder with the accumulated rotor initial position offset to obtain a corrected rotor position angle, and updating the corrected rotor position angle to an excitation system. According to the method, the in-phase characteristics of stator and rotor flux linkages are utilized, and the optimally designed low-pass filter is combined, so that the identification precision of the initial position of the rotor is remarkably improved, and the zero offset of the encoder is effectively compensated.

The invention discloses a dither signal filtering and sampling method based on a TFLN laser. The method comprises the following steps: step 1, loading a fundamental wave / second harmonic of a low-frequency dither signal into a photo-generated current of an optical module; 2, the photo-generated current signal loaded with the fundamental wave / second harmonic of the dither signal is amplified through an operational amplifier circuit and then is divided into two paths to be output; 3, after the first path of output in the two paths of output passes through a low-pass RC filter circuit, optical power sampling is carried out to monitor optical power, and the second path of output in the two paths of output is divided into a fundamental wave branch and a second harmonic wave branch; 4, enabling the fundamental wave branch and the second harmonic wave branch to pass through a corresponding frequency-selecting filtering and amplifying circuit and a rear-end amplifying and biasing circuit respectively to obtain a fundamental wave signal and a second harmonic wave signal, and outputting the fundamental wave signal and the second harmonic wave signal to an ADC sampling module for sampling. According to the invention, effective sampling of the fundamental wave and the second harmonic wave of the dither is realized.

The invention discloses a millimeter wave detector suitable for a short-distance moving target. The millimeter wave detector comprises a millimeter wave front end and an intermediate frequency signal processing unit, wherein the intermediate frequency signal processing unit comprises a low-pass filtering and amplifying circuit which is used for realizing low-pass filtering of a first preset frequency; comprising a detection circuit which is used for outputting an envelope signal of a second preset frequency and filtering a triangular wave signal of a third preset frequency; the band-pass filtering and amplifying circuit is used for realizing band-pass filtering between a fourth preset frequency and a fifth preset frequency; and the signal peak threshold judgment circuit is used for judging whether a detection target appears in a distance range needing to be detected or not according to the signal output by the band-pass filtering and amplifying circuit. The millimeter wave detector suitable for the short-distance moving target has the beneficial effect that the short-distance target detection sensitivity can be effectively improved.

The invention discloses a three-dimensional contour reconstruction method based on phase smoothing preprocessing in a phase shift profilometry, a storage medium and equipment. The method comprises the following steps: establishing a mathematical model between a phase shift fringe image and a phase in the phase shift profilometry to obtain an analytic expression for calculating the phase, a fringe background component and a fringe modulation amplitude; separating a modulation component from the phase shift fringe image, calculating a modulation degree of the phase shift fringe image, normalizing the modulation component according to the modulation degree, and performing low-pass filtering on the normalized modulation component; and finally, synthesizing the original background component and the modulation component subjected to low-pass filtering into a new phase shift fringe image, completing preprocessing, and calculating a phase by using the preprocessed image. According to the method, the influence of background component noise, the surface reflectivity of the object to be measured and other factors on the phase smoothing effect is avoided, the high-frequency characteristics in the phase can be reserved, the good phase smoothing effect is achieved, and the precision of the reconstructed three-dimensional contour is improved.

The invention provides a fall detection algorithm model for a wearable device of a stroke patient, and relates to the technical field of health monitoring and posture detection, and the technical key points are as follows: a construction method of the model is as follows: S1: obtaining posture information of a wearer by using an inertial sensor IMU in the wearable device, after data collection is completed, performing low-pass filtering processing on original data to obtain sensor data; s2, decomposing the inertial sensor data obtained at the t moment into a trend component, a season component and a residual component by using LOESS according to an STL (Standard Template Library) cyclic trend decomposition method, and then carrying out normalization processing on the data; and S3, performing time sequence modeling on each inertial sensor component by using LSTM (Long Short Term Memory). According to the fall detection algorithm model for the wearable device of the stroke patient, collaborative optimization of multi-physical-quantity coupling feature decoupling and time sequence dynamic modeling is realized, and medical-level fall monitoring performance is realized under resource constraints of the wearable device.

A gear fault diagnosis method based on the joint weighted envelope anti-noise correlation of sub-signals. The method comprises: first, converting an original vibration signal sequence into envelope signals by means of a squaring-low pass filtering-square root calculation process of a signal sequence element by element; then, reconstructing the envelope signals on the basis of different time intervals, so as to obtain a series of sub-signals, and calculating a fault information representation metric of each sub-signal on the basis of L-moment theory indexes; next, assigning a weight to each sub-signal by combining the fault information representation metric and Sigmoid function transformation; then, on the basis of the envelope signals, and reconstructed sub-signals and corresponding weights thereof, calculating a joint weighted envelope anti-noise correlation function of an envelope signal sequence and the reconstructed sub-signals; and finally, determining a characteristic frequency on the basis of the reciprocal of a time interval value corresponding to a characteristic peak in a drawn graph showing the change of the joint weighted envelope anti-noise correlation function along with the time intervals, and finally recognizing a gear fault. By means of the method, a gear fault can be reliably recognized simply on the basis of time domain analysis when a signal length is limited and there is complex noise interference.

The invention belongs to the technical field of current source control, and particularly relates to a self-adaptive intelligent current source output control system, and the system works cooperatively through a plurality of modules: a load detection module monitors the load access state in real time, converts a current signal into a voltage signal Udetect, and outputs the voltage signal Udetect; the state stabilization filtering module receives the signal, filters high-frequency noise through an RC low-pass filtering unit, amplifies the signal through an in-phase proportion amplification unit, and is internally provided with a signal monitoring and judging unit to extract a load characteristic signal INPUTIDE in a stable working state; the feedback calculation module generates a feedback control parameter through preset threshold comparison and logical operation according to the INPUTIDE; the analog switch control module selects a resistance potential-divider network input channel according to the feedback control parameters, and outputs a current source regulation and control signal VC; and the adjustable current source module outputs a working current adaptive to the load according to the VC. The self-adaptive intelligent current source output control system is superior to a traditional system in dynamic adaptation, protection mechanism, multi-scene adaptation, precision and stability.

The invention discloses a reversible information hiding method and system for enhancing image smoothness, and relates to the technical field of information security, and the system comprises a preprocessing module, an information embedding module and an optimization recovery module. According to the method, the Sobel operator is utilized to calculate the image gradient map, the region is divided based on the threshold T, the smooth region and the texture region can be accurately distinguished, an accurate basis is provided for differential processing of different regions, region characteristics are fitted, large-size blocks and sub-blocks of the smooth region are divided to facilitate subsequent refinement processing based on the gradient mean value, and the processing efficiency is improved. The small-size blocks of the texture area are beneficial to operation aiming at texture features, the rationality and effectiveness of overall processing are improved, meanwhile, the pixel mean value in the large-size blocks of the smooth area is calculated, the pixel and mean value difference value is recorded, reversible low-pass filtering is carried out, and the reversibility of the image in the whole information hiding and recovering process is ensured.

The embodiment of the invention provides a collision detection method and equipment for a humanoid robot. The method comprises the steps that resultant force, collected by a force sensor, of the tail end of the mechanical arm is obtained, derivation is conducted on the resultant force in time, first force change rate data of the resultant force in a time domain are obtained, low-pass filtering is conducted on the first force change rate data based on a preset frequency threshold value, and second force change rate data of the resultant force in the time domain are obtained; the preset frequency threshold value is determined by converting historical force change rate data into data on a frequency domain, the historical force change rate data is obtained by deriving resultant force obtained in the operation process with collision on time, and a collision detection result is determined according to the second force change rate data and the preset threshold value. According to the method provided by the embodiment of the invention, the calculation process is simple and efficient, and the target object screening efficiency and accuracy are improved.

The invention discloses a hysteresis compensation system and method for low-frequency compensation of a magnetoelectric vibration sensor. The compensation system comprises a difference to single-end circuit, a second-order lag compensation circuit, a single-chip microcomputer, an ADC conversion module, a first-order high-pass filtering module and a second-order low-pass filtering module which are sequentially connected with the magnetoelectric vibration sensor. The differential-to-single-end conversion circuit receives voltage signals output by the magnetoelectric vibration sensor and converts the voltage signals into single-end electric signals, the second-order lag compensation circuit receives the single-end electric signals of the differential-to-single-end conversion circuit for low-frequency compensation, the single-chip microcomputer receives low-frequency compensation signals, the ADC conversion module converts the low-frequency compensation electric signals into digital signals, and the digital signals are transmitted to the magnetoelectric vibration sensor. The first-order high-pass filtering module filters noise with the frequency lower than f1 out of the digital signals, and the second-order low-pass filtering module filters noise with the frequency higher than f2 out of the digital signals output by the first-order high-pass filtering module. According to the invention, the amplitude response of the magnetoelectric vibration sensor during low-frequency signal measurement can be improved.

The invention belongs to the field of sensors and signal processing, and particularly relates to a wavelet kernel scale sensitivity oriented abrasive particle induced voltage signal noise reduction method, which comprises the following steps: acquiring an abrasive particle induced voltage signal, and carrying out harmonic elimination on the abrasive particle induced voltage signal to obtain a preprocessed signal; constructing a wavelet kernel function, and calculating the wavelet kernel function and the preprocessed signal to obtain a kernel scale guiding spectrum; constructing a sparse joint noise reduction model based on the kernel scale guiding spectrum; processing the sparse joint noise reduction model to obtain a convex optimization objective function; solving the convex optimization objective function by combining a self-adaptive step gradient descent method and a self-adaptive iterative shrinkage threshold method to obtain a weight vector representing the distribution of the abrasive particle characteristic signals; carrying out binarization processing on the weight vector representing the distribution of the abrasive particle characteristic signals to obtain a characteristic indication vector; carrying out Hadamard product on the feature indication vector and the preprocessed signal, and then carrying out low-pass filtering to obtain a noise reduction signal; according to the method, the abrasive particle characteristic signals can be self-adaptively subjected to non-destructive enhancement and noise reduction processing in a strong interference environment.

The invention discloses a balance bridge arm control method and device of a three-phase inverter. The balance bridge arm control method comprises the following steps: acquiring an upper bus voltage, a lower bus voltage and an actual current of an inductor in real time; calculating a voltage difference value between the upper bus voltage and the lower bus voltage, and performing low-pass filtering processing on the voltage difference value; determining capacitance current of an upper bus capacitor and / or a lower bus capacitor according to the upper bus voltage and / or the lower bus voltage, and performing low-pass filtering processing on the capacitance current; calculating the reference current of the inductor according to the voltage difference value after the low-pass filtering processing and the capacitance current after the low-pass filtering processing; and controlling two switch tubes of the balance bridge arm according to the actual current and the reference current of the inductor. According to the technical scheme, the balance bridge arm can control potential balance of the neutral point of the direct-current bus and can restrain voltage ripples of a bus capacitor.

The invention discloses a sub-Nyquist joint broadband spectrum sensing and automatic modulation classification method based on deep learning. The method comprises the following steps: generating a generation data set for joint broadband spectrum sensing and automatic modulation classification; the method comprises the following steps: modeling a cognitive wireless network combined broadband spectrum sensing and automatic modulation classification system, and carrying out multi-coset preprocessing and analog low-pass filtering on a signal received by a cognitive wireless end; constructing a broadband spectrum sensing and automatic modulation classification neural network and training a model; and the local client combines the off-line broadband spectrum sensing training model and the off-line automatic modulation classification training model to online predict the spectrum occupation condition of the broadband signal and the modulation type of each occupied sub-band signal. According to the method, organic fusion of spectrum detection and modulation identification is realized, the processing efficiency and the intelligent degree of the system are remarkably improved, and the method has good robustness and high accuracy and is particularly suitable for a joint identification task of a multi-user broadband communication signal in a dynamic electromagnetic environment.

The invention relates to the technical field of signal processing, in particular to a high-speed unmanned aerial vehicle structure servo elastic coupling vibration suppression control method, system and device, a medium and a program product. Obtaining a target control loop causing resonance; spectral analysis is carried out, and double-center frequencies causing resonance are obtained; a cascade wave trap with double center frequencies is designed; and the servo elastic coupling vibration of the unmanned aerial vehicle structure is suppressed. According to the method, the problems of difficulty in structure modification, narrow notch frequency range and large low-pass filtering delay of an existing structure servo elastic coupling resonance solving method are solved, the performance of a control system cannot be reduced, and the flight safety is ensured.

The invention belongs to the technical field of image processing, and particularly relates to an MBR flat membrane surface physical damage visual identification method and system, and the method comprises the steps: carrying out the low-pass filtering of a microscopic gray image, so as to melt a micropore background; calculating a texture variation index based on the outlier degree of the gray values of the neighborhood pixel points and the neighborhood gray dynamic contrast gain; determining a damage aggregation weight by using a space attenuation accumulation value, and eliminating isolated noise points; the texture variation index and the damage aggregation weight are fused through a self-adaptive gating function, and damage confidence is generated; and finally marking a connected damage region based on statistical threshold binarization and morphological processing. According to the method, dense micropore interference can be effectively inhibited, and the physical damage identification precision is improved.

The invention discloses a multi-frequency digital phase-locked amplification method and device based on coherent sampling, and the method comprises the steps: applying multi-frequency sine reference signal excitation containing K reference frequencies to a measured object, and obtaining an impedance response signal of the measured object; performing coherent sampling on the impedance response signal; performing synchronous digital phase locking of K reference frequencies on the discrete impedance response signal sequence to obtain amplitudes and phases under the K reference frequencies; the device comprises a front-end unit and a rear-end unit, the front-end unit comprises a positive and negative current source and a current amplifier, and the rear-end unit comprises a control processing module, a DA conversion and low-pass filtering module and a low-pass filtering and AD conversion module. The invention aims to improve the digital lock-in amplifier so that the digital lock-in amplifier can synchronously process multi-frequency signals, reduce the design difficulty and resource consumption of a pass filter and improve the precision and speed of the lock-in amplifier, so that the digital lock-in amplifier is stable and reliable and has wide adaptability.

The invention provides a control method and system of a permanent magnet synchronous motor, computer equipment and a medium, and belongs to the field of motor control, and the method comprises the steps: obtaining stator current and voltage signals of the permanent magnet synchronous motor; constructing and operating a second-order sliding mode observer to output a stator current observation value; based on the current observation error and the change rate thereof, constructing a mixed sliding mode surface containing a linear term and a nonsingular terminal term, and designing a sliding mode control law; the control law is fed back to an observer to correct the observation process, and meanwhile two-phase observation counter electromotive force which is free of buffeting and does not need low-pass filtering is directly exported; closed-loop demodulation is carried out on the back electromotive force through a phase-locked loop, estimated values of the rotor position and the rotating speed are extracted, and finally position-sensorless vector control of the permanent magnet synchronous motor is achieved. According to the method, the buffeting phenomenon of a traditional sliding-mode observer is inhibited, a low-pass filter is avoided, phase lag is eliminated, and the estimation precision and the dynamic response performance of the system are improved.

The invention provides a sleep breathing condition detection method and system and electronic equipment, and relates to the technical field of human body sign monitoring, and the method comprises the following steps: obtaining a first breathing signal sampled by an optical fiber micro-vibration sensor; performing low-pass filtering processing on the first respiratory signal to obtain a second respiratory signal; envelope analysis and frequency analysis are carried out on the second respiration signal, and a target envelope width sequence and a target respiration dominant frequency sequence are determined; and according to the target envelope width sequence, the target respiration dominant frequency sequence, the reference envelope width baseline and the reference respiration frequency baseline, determining the sleep respiration condition of the user. According to the sleep breathing condition detection method, a large number of electrodes and wires do not need to be pasted on the human body, the use convenience is high, and the sleep comfort of the user is improved. In addition, the envelope width is combined with time-frequency analysis, the accuracy is improved through a multi-dimensional feature cross validation mode, and the accuracy of abnormal breathing event detection is high.

The invention discloses a bidirectional flow measurement ultrasonic flowmeter, and relates to the technical field of bidirectional flow measurement. The ultrasonic flowmeter comprises an amplifying circuit, a balance and unbalance conversion circuit, a signal frequency mixing circuit, a low-pass filtering amplifying circuit, an automatic gain tracking adjusting circuit and an ADC (Analog to Digital Converter). And after the signal reflected and diffracted by the liquid is amplified by the amplifying circuit, the balanced and unbalanced conversion circuit converts the amplified single-ended signal into a differential signal. After a differential signal and an input local oscillator signal are mixed by the signal mixing circuit, gain adjustment is carried out on the differential signal and the input local oscillator signal sequentially through the automatic gain tracking adjustment circuit, upper frequency is filtered out through the low-pass filtering amplification circuit, and finally signal acquisition is carried out through the ADC. According to the ultrasonic flowmeter, the effective signal collection effect is improved, and the ultrasonic flowmeter is smaller in size space, lower in cost and more excellent in performance.