1109 results about "Low pass filtering" patented technology

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 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 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 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.

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 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 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 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.

The invention provides a thin-wall structural part milling chatter suppression method and device based on main shaft rotating speed parameter adaptive regulation and control, electronic equipment and a storage medium. According to a stability lobe graph, a rotating speed area is divided, and a continuous variable rotating speed dominant area and a discrete variable rotating speed dominant area are determined; if the milling chatter is in a low-speed area, sine continuous variable-speed modulation is adopted, modulation parameters are optimized based on an energy minimization criterion, and a continuous variable-speed curve is generated; if the target is in the high-speed area, calculating a stable target rotating speed according to a phase difference matching condition, and generating a discrete rotating speed sequence through low-pass filtering and saturation constraint processing; and when the stability improvement amplitude is lower than a threshold value, the rotating speed is divided into a discrete variable rotating speed dominant area, and a discrete variable rotating speed strategy is adopted. An additional vibration suppression device or hardware transformation is not needed, and the method has the advantages of being easy and convenient to implement and low in cost and has the remarkable effect in the aspect of improving machining stability and efficiency.

The invention belongs to the technical field of power systems, and particularly discloses a fault prediction method for a thyristor and a BOD (Biochemical Oxygen Demand) in an SVC (Static Var Compensator), which comprises the following steps: an optimized data acquisition stage: continuously acquiring and storing data by utilizing a long short-term memory network, and dividing fault types into thyristor open circuit, thyristor short circuit, trigger angle deviation and normal state; establishing a prediction model: analyzing historical operation data by using a time sequence method, generating the prediction model by using a convolutional neural network and a long-short-term memory method, predicting the operation trend of the equipment in real time, and generating an early warning notice; a BOD protection circuit is optimized, wherein a BOD overvoltage detection branch and low-pass filtering branches R1 and C1 are established on a thyristor main circuit.

The invention discloses a no-load state monitoring method and system for an aircraft power supply system, and relates to the technical field of electrical parameter measurement and aircraft power supply system monitoring. Initializing a monitoring system, calibrating a voltage sensor, a current sensor, a frequency sensor and an impedance sensor, setting a no-load state judgment threshold system, and importing historical no-load data as a comparison reference; no-load voltage, current, frequency and impedance signals are synchronously acquired through sampling frequency of the sensor group, and the signals are transmitted to the data processing unit through the shielding cable; median filtering and low-pass filtering are adopted to remove noise interference, normalization processing is carried out on the signals, and basic electrical parameters are calculated; calculating core characteristic values of the voltage stability, the frequency fluctuation coefficient, the impedance stability and the harmonic distortion rate; comparing the characteristic value with a preset threshold value, judging whether the state is normal or not by combining historical data, and identifying an abnormal type; and outputting a judgment result in real time, and starting acousto-optic early warning in case of abnormality. Abnormal traceability and trend prediction are realized, and safe and stable operation of an aircraft power supply system is guaranteed.

The invention relates to the technical field of wind power generation, and discloses a numerical simulation inflow turbulence scale conversion method and device.The method comprises the steps that a turbulence inflow system is decomposed into an analysis part and a simulation part through wind spectrum time scale analysis and target time scale division, direct calculation of full-scale turbulence in a traditional method is avoided, and the calculation efficiency is improved; and the calculation cost is obviously reduced. The analysis part adopts instantaneous wind speed time history direct representation, the simulation part is solved through a turbulent flow transport equation, and calculation precision and efficiency are both considered. Smooth connection of an analysis scale and a simulation scale is realized by adopting a low-pass filtering technology, manual truncation errors are reduced, and the simulation precision of turbulence pulsation characteristics is improved.

The present invention discloses a method for optimizing and controlling target boost pressure, comprising: entering boost closed-loop enablement when boost closed-loop enablement conditions are met, obtaining a filtered EGR rate characteristic coefficient after first-order low-pass filtering based on a filtering time and an EGR rate characteristic coefficient; calculating a target boost pressure that has not been dynamically optimized based on the currently disclosed target boost pressure, the EGR rate characteristic coefficient, and the filtered EGR rate characteristic coefficient; dynamically optimizing the target boost pressure that has not been dynamically optimized based on the actual boost pressure read in real time to obtain a dynamically optimized target boost pressure; and controlling a boost actuator based on the dynamically optimized target boost pressure. The method provided by the present invention can achieve optimized control of the target boost pressure, further improving boost closed-loop control stability and EGR response accuracy.

The invention belongs to the technical field of integrated circuits, and particularly discloses a numerical control programmable gain amplifier chip, which adopts an SIP (Session Initiation Protocol) packaged operational amplifier, a digital potentiometer, an input end Vin and an output end Vout, and is characterized in that an alternating current signal is input from the Vin end and is connected to an inverted input end of the operational amplifier through a capacitor C1 and an input resistor R1; a positive power supply pin of the operational amplifier is connected with a power supply positive electrode VDD, a negative power supply pin is grounded, and a VDD / 2 direct current bias voltage is obtained by an in-phase input end through a precision resistance voltage dividing network and is accessed after low-pass filtering; the high end of the digital potentiometer is connected with the Vout end, the low end is connected with the inverting input end of the operational amplifier, and a feedback path is formed through a sliding end. The digital potentiometer and the operational amplifier are integrated, high integration and modularization of the programmable gain amplification circuit are achieved, and the problems that in a traditional discrete scheme, due to analog switch conduction resistance, gain precision is insufficient, reliability of a multi-element system is lowered, and the circuit size is large are solved.

The invention provides an eddy current sensor signal noise reduction method based on frequency division detection low-pass filtering and a monitoring system, belongs to the technical field of sensor signal processing, and can at least partially solve the problems of unstable signals and inaccurate measurement in the prior art. The signal noise reduction method for the eddy current sensor comprises the following steps: processing an original output signal of the eddy current sensor by adopting a synchronous detection module, demodulating a dynamic vibration alternating current component of the original output signal into a direct current voltage signal by utilizing a reference signal, and separating a static displacement direct current component signal; an independent dynamic signal channel and an independent static signal channel are formed; filtering high-frequency noise in the dynamic signal channel through a dynamic channel low-pass filter with a first preset cut-off frequency, and filtering power frequency interference in the static signal channel through a static channel low-pass filter with a second cut-off frequency; and reversely injecting compensation voltage at the output end of the static channel low-pass filter so as to eliminate the direct-current offset error of the static displacement component signal.

The invention discloses a self-adaptive virtual inertia control method and device for an energy storage DAB converter, relates to the field of converter control, and comprises three parts of current feedforward control based on low-pass filtering, voltage compensation control and virtual capacitor Cv parameter self-adaption. Wherein current feed-forward compensation in a low-pass filtering mode is added in a loop causing initial voltage abrupt change so as to improve initial drop of bus voltage, steady-state voltage compensation is added in a droop link so as to realize zero-difference adjustment of the bus voltage, and a function is constructed according to bus voltage deviation and change rate so as to realize self-adaptive adjustment of the virtual capacitor Cv. According to the control method provided by the invention, the bus voltage fluctuation can be effectively inhibited, the voltage steady-state error is reduced, and the supporting capability of the energy storage unit on the bus voltage is improved.

The invention relates to the technical field of impact resistance evaluation of underwater vehicles, in particular to an impact resistance evaluation method for internal equipment of an underwater vehicle. The method comprises the following steps: setting a flow field size of an underwater test environment and carrying out flow field modeling; based on the modeled flow field, performing numerical simulation on the dynamic response of the underwater vehicle under the impact load, obtaining the dynamic response value of internal equipment in each key cabin section of the underwater vehicle, and performing low-pass filtering processing; fourier transform is carried out on the dynamic response value of the internal equipment in each key cabin section after low-pass filtering processing, and an impact spectrum of the underwater vehicle is constructed; and converting the impact spectrum into a design spectrum for impact resistance evaluation, and comparing the design spectrum with an impact resistance standard system to judge the impact resistance of the internal equipment of the underwater vehicle. According to the method, the influence of the impact environment on the underwater vehicle can be intuitively and accurately quantified, so that the structural design of an internal cabin section of the vehicle is helped to be optimized.

The invention provides a virtual synchronous machine grid-connected control method and device and a readable storage medium, and relates to the technical field of electric power. The method comprises the following steps: calculating grid-connected active power and actual reactive power according to inverter parameters and power grid parameters of previous grid-connected control; determining the output voltage of the inverter according to the actual reactive power and the reference reactive power; inputting the grid-connected active power, the reference active power and the rated angular frequency into a first-order low-pass filtering module to obtain a frequency deviation and a frequency change rate, and adaptively adjusting the virtual inertia according to the power difference and the frequency deviation; updating the output angular frequency of the inverter according to the sum of the frequency deviation and the rated angular frequency; and performing voltage and current double-closed-loop control on the output voltage and the output angular frequency to obtain a modulated wave signal, and performing sine pulse width modulation based on the modulated wave signal to obtain a switching control signal of the inverter. Therefore, the active power oscillation is eliminated, the active steady-state deviation is reduced, and the frequency deviation and the frequency change rate are effectively inhibited.

The invention belongs to the technical field of power electronics, and discloses a carrier tuning nanoscale laser micro-vibration signal harmonic distortion suppression method and system, and the method comprises the steps: collecting an original carrier signal and a Doppler signal, and extracting the center frequency of the original carrier signal; calculating a frequency tuning amount based on the sampling duration, and shifting the center frequency to a low-frequency direction according to the tuning amount to obtain a tuned carrier wave; mixing the tuned carrier and the Doppler signal, performing low-pass filtering on the mixed frequency to obtain an I / Q baseband signal after phase compensation, drawing a Lissajous curve for the I / Q baseband signal, and correcting the Lissajous curve into a unit circle to obtain an orthogonal I / Q baseband signal; and performing arc tangent and phase unwrapping on the orthogonal I / Q baseband signal, and subtracting the compensation phase from a result to obtain a final signal. According to the invention, effective compensation of the phase of the I / Q baseband signal is realized, so that ellipse fitting maintains stability and robustness under a nanoscale vibration signal, non-orthogonal errors are effectively eliminated, and harmonic distortion in a nanoscale vibration demodulation result is inhibited.