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

The invention discloses a torsion test method based on a Kalman filtering algorithm, and relates to the technical field of torsion test.According to the torsion test method, a dynamic noise model and a material feature library are combined to enable a filter to automatically adapt to acoustic characteristics of different substrates, and the problem of misjudgment caused by fluctuation of material parameters in a traditional method is solved; secondly, a sub-band processing mechanism can restrain high-frequency noise and meanwhile completely reserve transient characteristics before the material is broken, and effective signal loss is avoided; the transfer learning model eliminates batch differences through adversarial training, and the generalization ability of dynamic threshold judgment is ensured; the real-time compensation strategy generates a refastening scheme considering both mechanical safety and assembly precision in emergency.

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

The invention 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.

The invention relates to the technical field of intelligent detection of power equipment, in particular to a complex illumination low-voltage electricity meter frequency sensing detection method, system and equipment and a medium, and aims to solve the problems of high-frequency noise interference, intra-class feature difference, insufficient edge end energy efficiency and the like existing in electricity meter detection in a high-brightness environment. Proposing a dynamic frequency domain compensation mechanism to suppress light noise and recover signal details, and optimizing dense scene feature consistency in combination with adaptive filtering and a multi-scale sampling strategy; the boundary fuzzy problem is solved through dynamic feature alignment and an edge compensation mechanism, and parallel acceleration of frequency domain decomposition and dynamic resampling is realized by adopting a heterogeneous hardware architecture; a multi-modal data training framework and a cross-domain transfer learning strategy are fused to enhance the generalization ability of the model; according to the invention, collaborative optimization of detection precision, real-time performance and low power consumption is realized in a complex illumination scene, and high-robustness technical support is provided for intelligent power grid inspection.

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 pipeline leakage monitoring method and system, and belongs to the technical field of pipe network leakage monitoring. Based on leakage and non-leakage signal spectrum comparison, a high-difference frequency band far away from low-frequency noise is extracted as a characteristic frequency band, high-frequency effective characteristics are focused, and low-frequency interference is avoided; savitzky-Golay smooth filtering is used to eliminate random noise and maintain spectrum peak shape characteristics at the same time, so that the representativeness of a characteristic curve is improved; amplitude difference caused by environmental factors is eliminated through normalization processing, so that the characteristic curves under different working conditions can be compared; shape and amplitude dual verification is carried out through a Pearson's correlation coefficient PPC and a mean square error RMES, and the misjudgment rate of pipeline leakage is reduced; and through a dynamic threshold setting mechanism, different pipe types and working condition changes are self-adapted, and the problem of sensitivity reduction caused by a fixed threshold is avoided.

The invention relates to a magnetotelluric dead band data correction method and system based on deep learning, and the method comprises the steps: carrying out the segmentation processing of actually-measured magnetotelluric data, obtaining a plurality of original segments, carrying out the processing of each original segment through a DnCNN-GRU network, extracting a low-frequency effective signal, and separating a high-frequency noise-containing signal; performing identification and classification processing on the high-frequency noisy signal through an IncepTCN network model to identify a first high-quality fragment and a noisy fragment, and performing denoising processing on the noisy fragment through an IVIT network model to obtain a second high-quality fragment; and splicing the first high-quality segment and the second high-quality segment to obtain a spliced segment, and carrying out combination reconstruction on the spliced segment and the low-frequency effective signal to obtain a reconstructed signal. According to the invention, the method can achieve the automation of the whole process from low-frequency protection, noise classification to denoising, can avoid the manual parameter adjustment, and remarkably improves the recognition and suppression precision of complex noise.

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 discloses a simulation verification method for an ASIC (Application Specific Integrated Circuit) control loop of a metal micro-resonance hemisphere-like gyroscope. The method comprises the following steps: based on an input angular velocity and a control signal, generating a vibration signal of a harmonic oscillator by using a pre-constructed dynamics simulation model; modulating and demodulating the vibration signal through a signal modulation and demodulation module, extracting a low-frequency component and removing high-frequency noise to obtain a parameter representing a modal precession state; an antinode control signal and a wave node control signal are generated through a PID controller module based on the parameters representing the modal precession state; an antinode control signal and a wave node control signal are decomposed to an orthogonal direction through a coordinate transformation and combination module to generate a driving signal, and the driving signal is applied to a harmonic oscillator to form a closed-loop self-excitation driving control loop; and verifying the dynamic performance of the closed-loop self-excitation driving control loop in a simulation environment. The technical problems of high power consumption, complexity and development cost of simulation verification are solved.

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.

The invention discloses a blind separation and time-frequency noise reduction combined comprehensive pulsar time establishing method. The method comprises the following steps: selecting a pulsar meeting a preset requirement; processing the timing residual error of the selected pulsar; establishing a mixed signal model, iteratively solving an anti-mixing matrix through an approximate negentropy contrast function, and separating independent components from the multi-dimensional features; carrying out Pearson correlation analysis on each separated independent component and reference time; and performing multi-scale decomposition on the selected common-mode clock difference signal by adopting a plurality of wavelet bases, and performing Bayesian soft threshold processing to obtain the final comprehensive pulsar time. According to the method, the long-term stability of the comprehensive pulsar is improved, the common problems of aliasing and modal mixing in a high-noise environment in the prior art are solved, and the extracted signal is more stable.

The embodiment of the invention provides a compressor sound insulation structure and a control method thereof. The compressor sound insulation structure comprises a sound insulation cover and a deformation device. The compressor is covered with the sound insulation cover. The sound insulation cover comprises an inner sound insulation layer and an outer sound insulation layer which are sequentially arranged from inside to outside, and a resonant cavity is formed between the inner sound insulation layer and the outer sound insulation layer; the inner sound insulation layer is provided with a plurality of through holes and is made of a shape memory material; the deformation device is arranged in the soundproof cover and used for receiving the operation frequency of the compressor and driving the inner soundproof layer to deform according to the operation frequency so that the hole diameter of the through hole can be changed. Through the arrangement of the resonant cavity, the intensity of low-frequency noise is gradually weakened under the resonance effect, and therefore effective absorption of the low-frequency noise is achieved; by arranging the deformation device and the inner sound insulation layer made of the shape memory material, intelligent adjustment of the hole diameter of the through hole is achieved, so that the sound absorption frequency of the inner sound insulation layer and the resonant cavity is dynamically adjusted, the noise reduction requirement for broadband noise is met, and the operation noise of the outdoor unit is reduced.

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 invention discloses an electromagnetic signal superconducting observation system based on micro-shielding and thermal management and a design method, and the system comprises a high-temperature sensor unit which comprises a pressure-resistant constant-temperature container, a specially-made non-magnetic glass Dewar flask disposed in the pressure-resistant constant-temperature container, and a built-in three-axis high-temperature superconducting sensor. The pressure-resistant constant-temperature container comprises a base body made of carbon fiber reinforced plastics, and a low-frequency frequency-selecting shielding layer, an insulating thin layer, an absorbing layer and a high-frequency shielding layer are sequentially arranged outside the base body. According to the invention, the interference of high-frequency noise in a complex environment and the thermal noise of the three-axis high-temperature superconducting sensor can be significantly reduced. According to the system provided by the invention, the low-noise electromagnetic signal detection of the high-temperature superconducting sensor can be realized in a complex environment, the application range of electromagnetic method technology resource exploration can be widened, and the detection precision can be improved.

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.