797 results about "Frequency noise" patented technology

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 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 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 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.

The invention discloses a remote sensing image low-frequency noise correction method and system, relates to the field of remote sensing image processing, realizes accurate removal of stripe noise under the condition of reducing satellite image detail loss, and relieves the problems of unstable correction effect, easiness in losing image details, difficulty in accurately depicting prior characteristics and the like of the existing remote sensing image low-frequency noise correction technology. The remote sensing image low-frequency noise correction process takes a column mean value or a row mean value as a basic processing unit; fine noise intensity calibration is carried out; low-frequency noise of the image is corrected in a mean value compensation mode, and detail loss of the corrected image is avoided. The method is suitable for multispectral and panchromatic remote sensing images of a remote sensing satellite, and is not influenced by ground feature types.

According to the electroencephalogram signal collecting device and method, the common-mode rejection ratio is increased through the right leg driving module, high-frequency noise and low-frequency noise are filtered out through the band-pass filtering module, power frequency interference is eliminated through the notch module, and filtering processing is conducted on the power frequency interference and the power frequency multiple in a software mode through the power frequency filtering model; therefore, the radio frequency interference is improved, the anti-interference capability of electroencephalogram signal acquisition is greatly improved, and the accuracy and stability of electroencephalogram signal acquisition are improved.

The invention discloses a gas leakage infrared detection algorithm for self-adaptive environmental noise suppression. The gas leakage infrared detection algorithm comprises the following steps: S1, preprocessing input data; s2, adaptive filtering is carried out; s3, carrying out multi-frame fusion, carrying out time-space domain analysis on continuous infrared image frames, separating high-frequency noise and low-frequency gas signals by utilizing wavelet transform, calculating a difference image of adjacent frames by combining an inter-frame difference method, enhancing gas diffusion morphological characteristics, distributing a weight for each frame according to a signal-to-noise ratio, and synthesizing a final image; and S4, post-treatment: concentration quantification and leakage area marking treatment are respectively carried out. According to the gas leakage infrared detection algorithm for self-adaptive environmental noise suppression disclosed by the invention, high sensitivity is kept through a self-adaptive environmental noise suppression algorithm, meanwhile, environmental noise interference is effectively eliminated, and the detection stability and accuracy in a complex scene are improved.

The invention relates to the technical field of boiler temperature measurement, and discloses a boiler furnace temperature field measurement system based on multi-sensor fusion. According to a temperature acquisition module of the system, a plurality of groups of infrared temperature measurement sensors and thermocouple sensors are arranged in preset space grids of a boiler hearth, and real-time temperature data of each space grid are synchronously acquired; the data fusion module performs time alignment and space registration on the acquired data to generate a space-time synchronous original temperature data set; the noise suppression module performs multi-scale decomposition on an original data set based on wavelet transform, extracts a high-frequency noise component and performs adaptive filtering, and outputs a temperature data sequence after noise reduction; the temperature field reconstruction module reconstructs a hearth three-dimensional temperature field distribution model through a radial basis function interpolation algorithm according to the denoised data sequence; and the dynamic compensation module calculates the heat conduction lag compensation amount of the temperature field distribution model according to the flowing speed of the flue gas in the hearth and the working condition parameters of the combustor, and generates a compensated dynamic temperature field model.

The invention discloses a partial discharge signal multi-source cooperative detection method and system based on time-frequency fusion analysis, and relates to the technical field of multi-source cooperative detection, and the method comprises the following steps: obtaining partial discharge original signals of an electric signal channel and an ultrahigh frequency channel, respectively carrying out the time-frequency transformation, and generating a multi-source time-frequency characteristic spectrum; extracting high-frequency noise modes of different channels under a non-partial discharge condition based on the multi-source time-frequency characteristic spectrum, and constructing a cross-source interference fingerprint database; based on the cross-source interference fingerprint database, carrying out frequency-band-by-band difference mapping calculation on actually acquired multi-source time-frequency characteristics to generate a difference characteristic matrix; and introducing reverse consistency constraint into the differential feature matrix, carrying out weighted fusion to obtain multi-source coupling time-frequency feature mapping, and outputting a detection result of the partial discharge signal. According to the method, the multi-source time-frequency characteristic spectrum is constructed to carry out band-by-band differential mapping on the actual signal, so that cross-source noise interference is inhibited, and the accuracy and robustness of partial discharge detection in a strong noise environment are improved.

The invention relates to the technical field of clean energy fault positioning, and discloses a fault positioning method and system for abnormal operation of a clean energy station. A signal collecting and processing module of the system collects a multi-source operation state signal in real time, and a smooth signal is reconstructed after high-frequency noise is removed through self-adaptive filtering; the operation state analysis module extracts time-frequency characteristics of the smooth signal, identifies the operation state stage of the station, and generates a fault characteristic spectrum based on preset parameters and real-time frequency data when the station is abnormal; the three-dimensional station modeling module analyzes terrain detection signals in a multi-scale manner, and constructs a three-dimensional model containing equipment space coordinates, topological connection relations and environment characteristics; the fault prediction module maps the fault characteristic spectrum to a three-dimensional model, divides equipment area subspaces, calculates fault probability distribution and generates a fault thermodynamic diagram and a density gradient field; and the optimization decision module initializes particle swarm optimization algorithm parameters, constructs an operation stability objective function, and generates an optimal fault positioning path scheme in combination with equipment health state constraints.

The invention relates to the technical field of power supply management, in particular to a transient response optimization method based on a linear voltage regulator, and the method comprises the steps: obtaining transient response data of the linear voltage regulator when a load suddenly changes, and judging whether the linear voltage regulator enters a transient early warning state or not. And if entering the early warning state, carrying out high-frequency noise threshold segmentation on an output voltage waveform, extracting voltage change rate characteristics, constructing a compensation efficiency model based on dynamic response time and effective compensation bandwidth, calculating an optimal open-loop compensation coefficient, generating a compensation control instruction, and driving a compensation network to carry out dynamic compensation. And if the pre-warning state is not entered, ripple uniformity data of the output voltage is acquired, a deviation matrix of an actual ripple distribution curved surface and an ideal ripple distribution plane is determined, and whether a local ripple abnormal region exists is determined. According to the method, through dynamic monitoring and intelligent compensation, the transient response speed and stability of the linear voltage regulator are improved, and high-frequency noise processing and ripple uniformity are optimized.

The invention belongs to the technical field of aviation flight training, particularly relates to a flight training trajectory generation method fusing an attention mechanism, and solves the problems that in the prior art, diversified, personalized and high-quality flight training trajectories are difficult to generate, and the high-quality training requirement cannot be met. According to the method, flight training multi-source data is obtained and subjected to preprocessing and dynamic weight fusion to generate fusion data, an encoder in a prediction model processes a single track fragment in the fusion data through a random feature attention mechanism, real-time aerodynamic parameters are fused into a key matrix, an attention score is adjusted through an aerodynamic consistency coefficient, and then submerged space representation is obtained; the decoder generates an initial prediction flight training trajectory sequence in combination with the submerged space representation and the noise vector; and finally, suppressing high-frequency noise by using speed self-adaptive cut-off frequency and weighted sinc filtering, and carrying out nonlinear filtering to obtain a final predicted flight training trajectory sequence. According to the method, diversified, personalized and high-quality flight training trajectories are generated, and the high-quality training requirement is met.

A high-frequency resistant ground fault circuit interrupter (HF-GFCI) with self-test and end-of-life detection is disclosed. The invention incorporates a noise-resistant fault detection circuit, an auto-test module, and an end-of-life shutdown mechanism to ensure reliable protection. The device minimizes false tripping due to high-frequency noise and enhances safety through continuous self-monitoring. Experimental validation confirms improved immunity to leakage currents and transient voltage disturbances. The HF-GFCI also provides an LED circuit having dual functionality: an LED indicator circuit and neutral to ground detection on the load side of the HF-GFCI.

The invention belongs to the technical field of distributed optical fiber sensing, and relates to a coding difference phase noise and distortion cooperative suppression optical frequency domain reflection method, which comprises the following steps: acquiring upper and lower frequency sweeping and beat frequency signals; separating the upper and lower sweep-frequency beat-frequency signals to obtain an upper sweep-frequency beat-frequency signal and a lower sweep-frequency beat-frequency signal; acquiring an upper sweep frequency beat frequency electric signal and a lower sweep frequency beat frequency electric signal; and the upper frequency-sweeping beat-frequency electric signal is delayed by one spatial resolution and then is mixed with the lower frequency-sweeping beat-frequency electric signal, or the lower frequency-sweeping beat-frequency electric signal is delayed by one spatial resolution and then is mixed with the upper frequency-sweeping beat-frequency electric signal, a frequency-mixing signal after phase frequency noise and frequency domain distortion suppression is obtained, and the Fourier frequency domain spectrum of the frequency-mixing signal is analyzed. The invention also provides a coding difference phase noise and distortion cooperative suppression optical frequency domain reflection system. The system comprises a positive and negative double-sideband sweep frequency light acquisition device, an optical splitter, a spectrum spreading device, an interference device, a beat frequency signal acquisition device and an electric delay unit. According to the invention, phase noise and frequency domain distortion noise can be effectively suppressed.

This invention discloses a broadband composite sound insulation structure, including a first sound insulation mechanism and a second sound insulation mechanism. The first and second sound insulation mechanisms are disposed opposite to each other, with a closed air layer formed between them. Through the arrangement of the first, second, and air layers, the broadband composite sound insulation structure of this invention can effectively reduce and suppress broadband noise, including high-frequency, mid-frequency, and low-frequency noise. By incorporating the first and second sound insulation panels, noise reduction can be achieved while minimizing external environmental pollution to both the first and second sound insulation layers.

The present application relates to power management technical field, especially to a kind of transient response optimization method based on linear voltage regulator, method includes: obtaining the transient response data of linear voltage regulator when load mutation, judge whether into transient early warning state.If into early warning state, high-frequency noise threshold segmentation is carried out to output voltage waveform, extract voltage rate of change characteristic, construct compensation efficiency model based on dynamic response time and effective compensation bandwidth, calculate optimal open-loop compensation coefficient, and generate compensation control instruction, drive compensation network to carry out dynamic compensation.If not into early warning state, the ripple uniformity data of output voltage is obtained, determine the deviation matrix of actual ripple distribution surface and ideal ripple distribution plane, judge whether there is local ripple abnormal area.The method improves the transient response speed and stability of linear voltage regulator by dynamic monitoring and intelligent compensation, optimizes high-frequency noise processing and ripple uniformity.

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

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 silencer, an air conditioner and a design method of the silencer, the silencer comprises a shell and a plurality of acoustic units arranged in the shell, and the shell is mounted on the outer wall of an air duct and provided with a mounting surface facing the air duct; the acoustic unit comprises a plurality of half-wavelength pipes, each half-wavelength pipe is provided with an extended first acoustic channel, and a first inlet and a first outlet of each half-wavelength pipe are formed in the mounting surface; wherein the first acoustic channel lengths of the plurality of half-wavelength tubes are set based on different target noise reduction frequencies so as to cover a predetermined noise reduction frequency band. The silencer is installed outside the air duct through the shell, it is guaranteed that the original circulation area and shape of the air duct are not affected at all, and therefore the 100% ventilation rate is achieved, and the core contradictions that the air volume is sacrificed and the performance of an air conditioner is reduced when a traditional noise reduction device is additionally installed are solved. Secondly, according to the scheme, the resonance sound absorption principle of a series of half-wavelength tubes is utilized, and the principle has an excellent control effect on low-frequency noise.

The invention relates to an icing power transmission line galloping simulation method and simulation system based on a composite time integral. The method comprises the following steps: S1, establishing a nonlinear finite element model of an icing power transmission line; s2, carrying out time domain solution by adopting a composite time integration algorithm; calculating the solution of the moment in two sub-steps according to the solution of the moment, namely from the first sub-step to the second sub-step; the first sub-step adopts a trapezoidal rule for integration, and the second sub-step adopts a three-point backward difference format based on information of three moments; s3, coupling and solving a nonlinear aerodynamic load related to the speed; s4, obtaining the galloping response of the power transmission line; the problems that in a transmission line galloping simulation technology based on a traditional Newmark method, contradiction between calculation efficiency and numerical precision exists in the strong nonlinear coupling problem, accurate tracking of low-frequency galloping and efficient suppression of high-frequency noise are difficult to achieve at the same time under the large time step length, simulation calculation efficiency is low, and convergence is poor are solved.

The invention belongs to the technical field of distributed optical fiber sensing, and relates to a phase-frequency noise suppression optical frequency domain reflection measurement method and system without an auxiliary interferometer. The measurement method comprises the following steps: acquiring positive and negative double-sideband sweep-frequency light with the same sweep-frequency range and opposite sweep-frequency directions; dividing the positive and negative double-sideband sweep frequency light into double-sideband detection light and double-sideband local oscillation light according to a preset proportion; configuring a pseudo-random noise code, and modulating the pseudo-random noise code to the double-sideband probe light to obtain spread spectrum double-sideband probe light; the spread spectrum double-sideband detection light enters the sensing optical fiber and is reflected back to the backward Rayleigh scattering light, and the backward Rayleigh scattering light and the spread spectrum double-sideband local oscillation light interfere with each other to output up-down sweep frequency beat frequency signals; separating the upper and lower sweep-frequency beat-frequency signals to obtain an upper sweep-frequency beat-frequency signal and a lower sweep-frequency beat-frequency signal; and after the upper sweep frequency beat frequency signal and the lower sweep frequency beat frequency signal are respectively decoded, a beat frequency signal is separated through band-pass filtering, the beat frequency signal is subjected to frequency mixing processing to obtain a frequency mixing signal after phase frequency noise suppression, and the frequency spectrum of the frequency mixing signal is analyzed.

The invention discloses a ring-down light cavity precision pressure control method and device based on enhanced data-driven PID, and the method comprises the steps: 1) building a ring-down light cavity pressure control device, and integrating a pressure sensor, a proportional valve, a digital multimeter, a DC power supply, a vacuum pump and other key parts; 2) acquiring a proportional valve driving voltage and a pressure feedback signal based on LabVIEW, and constructing a discrete time ARX model of the system; (3) on the basis of the model residual error and the real-time error, an enhanced data driving algorithm is adopted to carry out online self-adaptive updating on PID parameters; 4) filtering high-frequency noise by using a second-order Bessel low-pass filter to improve the signal stability; and 5) realizing closed-loop control under the LabVIEW platform, and monitoring and maintaining the operation performance of the system in real time. The method has the advantages of model self-learning, parameter self-tuning, high robustness, high control precision and the like, is particularly suitable for scenes such as high-precision spectral measurement with extremely high requirements on pressure stability, and has remarkable engineering practical value and popularization and application prospects.

The invention relates to a method for obtaining credible blasting vibration dominant frequency, which solves the problem of dominant frequency prediction under complex geology through EMD-HHT multi-scale decomposition and statistical confidence interval analysis and is difficult to adapt to dynamically changing construction conditions. EMD decomposition adaptively eliminates low-frequency noise caused by nonlinear response of the rock mass, and the signal-to-noise ratio is increased; a main frequency determination strategy is automatically switched through confidence interval width (RW) to adapt to complex geological conditions; 95% confidence coefficient ensures that a dominant frequency predicted value accords with actual energy distribution; compared with a traditional Samsu formula, the main frequency prediction error is reduced by more than or equal to 40%.

The invention belongs to the technical field of deep sea vibration and noise reduction functional shells, and particularly relates to a vibration and noise reduction composite pipe and a preparation method thereof.The vibration and noise reduction composite pipe comprises an outer layer, a middle layer and an inner layer which are sequentially connected together in a sleeved mode; the outer layer is a carbon fiber resin composite material pipe; the inner layer is a 316L stainless steel pipe; the middle layer is arranged between the outer layer and the inner layer and comprises a micro-perforated aluminum pipe, a Vonass flower basket imitating structure and a perforated aluminum pipe which are sequentially connected in a sleeved mode from outside to inside, and graphene epoxy resin is filled between the micro-perforated aluminum pipe and the Vonass flower basket imitating structure and between the Vonass flower basket imitating structure and the perforated aluminum pipe. According to the invention, through the application of the Vonass flower basket imitating structure and the micro-perforated aluminum pipe, the broadband sound absorption and noise reduction effect is obviously improved, the performance is excellent in the aspect of low-frequency noise absorption, and compared with a traditional honeycomb structure, the honeycomb structure has obvious advantages; the middle layer can absorb sound waves in the frequency range of 50-5000 Hz, and the low-frequency absorption efficiency is improved by 40%.

The invention belongs to the field of motor control, and discloses a rotating speed fluctuation suppression anti-interference control method for a low-speed permanent magnet direct drive motor. According to the method, a first-order linear active disturbance rejection controller is designed in a rotating speed loop, an improved LESO is designed by constructing a state observation error differential term and a coefficient thereof, an integral + differential parallel structure is constructed in a signal transmission path for estimating total disturbance of the rotating speed loop, and the medium-high frequency disturbance tracking speed is increased by using the phase lead characteristic of the differential term. And by combining a second-order IIR Butterworth high-pass filter, high-frequency noise introduced due to the increase of the bandwidth of the observer can be accurately separated and filtered out, the inherent contradiction between bandwidth improvement and noise amplification in the traditional LADRC is broken through, and the improvement of the rotation speed fluctuation suppression ratio under the low-speed working condition is realized. Model-free predictive control is adopted in a current loop, dependence of a system on accurate modeling of motor parameters is reduced, current tracking errors generated under the condition of parameter mismatch are reduced, torque pulsation caused by current fluctuation is reduced, and then rotating speed fluctuation is reduced.

The invention discloses an electromagnetic vibration table for an uncooled infrared machine core assembly adopting multiple sensors, and relates to the technical field of vibration tables. By arranging four groups of vibration testing mechanisms of low frequency, intermediate frequency, high frequency and ultrahigh frequency, vibration waves of different frequencies can be synchronously or alternately generated, complex operation conditions of the uncooled infrared movement in real environments such as transportation jolting, mechanical shock and high-frequency vibration can be simulated, and vibration testing of different vibration frequencies can be performed on a plurality of uncooled infrared movement assemblies at the same time. Wherein the low-frequency vibration testing mechanism and the intermediate-frequency vibration testing mechanism can simulate low-frequency vibration in a transportation process, the high-frequency vibration testing mechanism can simulate high-frequency noise interference in operation of the uncooled infrared movement, and the ultrahigh-frequency vibration testing mechanism can simulate an instantaneous impact working condition of the uncooled infrared movement in an extreme environment. Therefore, each uncooled infrared movement to be tested can undergo vibration tests in various modes, full-band coverage tests are realized, and the accuracy and credibility of test results are greatly improved.

The invention relates to the technical field of electro-oculogram signal processing, and discloses a medical-grade electro-oculogram signal noise reduction filtering processing method and system, and the method comprises the steps: obtaining a to-be-processed electro-oculogram signal and a synchronous electroencephalogram signal, and marking a target signal segment; separating an electro-oculogram noise component and an electro-oculogram effective component by an electro-oculogram separation algorithm based on mutual information combination; performing decomposition and weighted fusion on the electro-oculogram noise component by adopting a double-coefficient fusion mode denoising model to generate a preliminary denoising signal; a special high-pass convolution noise reduction model for the EEG channel is used for filtering, and low-frequency noise is suppressed; signals are detected according to medical-grade parameter requirements, and if the signals do not reach the standard, the signals are returned for reprocessing; and checking the channel consistency of the qualified signal, and integrating to form a final signal. The system comprises six units. The method and the system can accurately separate the noise, guarantee the signal quality, solve the problems of inaccurate noise processing and poor signal stability in the prior art, and meet the high-quality requirement of clinical medical treatment on the electro-oculogram signal.

The invention provides an electric power meter data real-time analysis and abnormity early warning method based on edge calculation, and belongs to the technical field of data analysis and early warning, and the method comprises the steps: collecting the multi-modal data of an electric power meter based on a multi-channel analog-to-digital converter, employing a hardware filter circuit to eliminate high-frequency noise, and obtaining the data of the electric power meter; signal gain self-adaptive adjustment is carried out based on a reconfigurable amplifier; a digital signal processing accelerator is used to carry out Fourier transform on a matrix constructed by the standardized data stream, and a multi-core processor is combined to carry out analysis to generate a feature vector; the method comprises the following steps: acquiring environmental parameters in real time based on an environmental sensor, dynamically adjusting an anomaly detection threshold in combination with a machine learning model trained by historical data, and comparing a feature vector with the dynamic threshold to generate an anomaly signal; the relay control circuit triggers sound-light alarm, the physically isolated communication module sends an early warning instruction to the cloud platform, and meanwhile, the redundant power supply module is started to control the electric power meter to operate continuously. And the efficiency and accuracy of subsequent abnormity early warning are improved.