5045 results about "High Frequency Waves" patented technology

The invention belongs to the technical field of electromechanical equipment intelligent monitoring and operation and maintenance management, and discloses an electromechanical system health monitoring and optimization method based on artificial intelligence, and the method comprises the steps: building a digital twin architecture based on a simulation model, reasonably arranging a multi-source sensor, and carrying out the optimization of the health of an electromechanical system; key equipment is fully covered; the system acquires operation data at high frequency and synchronously transmits the operation data to the cloud through multiple channels to realize real-time monitoring; the collected data is cleaned and preprocessed to remove abnormities so as to ensure the data quality; inputting the cleaned data into a neural network, extracting features, and identifying an operation trend and a fault mode; establishing a prediction model in combination with historical data, and constructing a maintenance knowledge base; the equipment state is predicted in real time, and early warning is dynamically generated; through virtual-real synchronization, health and carbon emission states are mapped in real time, equipment operation is intelligently optimized, and cloud closed-loop monitoring is continuously iterated.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses a drilling multi-source sensing signal denoising method based on dynamic noise decoupling and a self-adaptive mode, and belongs to the technical field of underground processing. The method comprises the following steps of: separating common noise of a noise energy distribution matrix of a sensor and separating specific noise; carrying out adaptive noise set empirical mode decomposition on the separated signals, carrying out variational mode decomposition on residual signals in the signals, and screening effective modes through a kurtosis-entropy joint criterion to obtain signals with effective characteristic components reserved; high-frequency fluctuation of the signal is punished through total variation regularization, an improved alternating direction multiplier method algorithm is used for solving, short-time Fourier transform is carried out on the solved signal, low-frequency and high-frequency features are extracted through a multi-scale convolutional network, and a final denoised signal is output through gating weight fusion. According to the method, the signal denoising precision in a complex noise environment is remarkably improved, the processing time is shortened, and the resource consumption is reduced.

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The invention discloses an intelligent fire detection method based on artificial intelligence video analysis, and particularly relates to the technical field of computer vision. The method comprises the following steps: acquiring a video monitoring stream, extracting spatial color features and morphological features of an image, constructing a multi-scale time sequence feature graph group, performing joint modeling on color disturbance and structural disturbance by using a dual-channel attention neural network, and outputting a suspected fire area probability graph; candidate fire source areas are screened in combination with boundary disturbance consistency analysis and red channel high-frequency fluctuation detection; the texture stability, the disturbance directivity and the historical smoke evolution characteristics are integrated through a multi-modal fusion judgment model, the fire state is finally judged, an alarm result is output, meanwhile, the system can mark the fire position and the development trend path, and visual tracking is achieved. The method has the advantages of high robustness, high sensitivity and low false alarm rate, and is suitable for intelligent early warning of early fire in a complex environment.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.

The invention provides a magnesium alloy electric drive main shell mold machining precision control method and system, and belongs to the field of precision numerical control machining. The method comprises the steps that dynamic milling force, key point temperature and position signals of all shafts are synchronously collected through a multi-source sensor; a recursive least square method is adopted to identify the dynamic rigidity and damping coefficient of the tool-workpiece system on line, and the thermal deformation drift distance is calculated in combination with a temperature signal; predicting a three-dimensional deformation error vector in real time based on the dynamic model, thermal deformation and geometric errors, and constructing a space compensation field; macro motion compensation is achieved by modifying numerical control program coordinate points, and meanwhile high-frequency micro-amplitude compensation is conducted through a spindle tail end fast tool servo system. And establishing an adaptive disturbance observer to perform online estimation on unmodeled disturbance and feed-forward compensation, and realizing online self-tuning of model parameters in combination with in-situ measurement feedback. Real-time sensing, full-band dynamic compensation and closed-loop self-adaptive control of multi-source errors are achieved, and the machining precision and long-term stability of the mold are remarkably improved.

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

The invention discloses a Fourier transform and S transform combined battery impedance spectroscopy measurement method and device, and aims to improve the precision, speed and reliability of impedance measurement of a battery energy storage system. According to the method, a multi-frequency component excitation signal is generated through a controllable current source, and current and voltage response signals of a battery are synchronously acquired and are respectively input into a Fourier transform module and an S transform module for frequency domain analysis. The Fourier transform module extracts impedance spectrums of low and medium frequency bands, and the S transform module extracts impedance spectrums of medium and high frequency bands through Gaussian window time-frequency analysis. And a data fusion algorithm is adopted to perform composite processing on results of the two modules, including a direct selection method, an arithmetic average method, an amplitude and phase calibration method and a weighted average method, so that smooth transition and optimization between frequency bands are realized, and finally the electrochemical impedance spectrum is generated. Through collaborative analysis of Fourier transform and S transform, parameter optimization and a fusion algorithm, the efficiency and accuracy of impedance spectroscopy measurement are remarkably improved, and the method is suitable for rapid monitoring and diagnosis of the battery state.

The invention belongs to the technical field of comprehensive utilization of ocean renewable energy sources, and particularly relates to a pilot frequency coupling-based wind wave energy multi-degree-of-freedom broadband control method, which comprises the following steps of: performing frequency domain identification on wind wave environment characteristics, and constructing a wind wave pilot frequency energy coupling model; based on the motion response characteristics of the multi-degree-of-freedom floating platform, designing a pilot frequency energy regulation and conversion mechanism; a full-working-condition dynamic optimization control strategy is adopted, and cooperative operation and energy flow self-adaptive distribution of the wind energy conversion unit and the wave energy conversion unit are achieved. Aiming at the significant difference and relevance of wind energy and wave energy in frequency characteristics, time scale and spatial distribution, a pilot frequency wind wave coupling dynamic model is established, and broadband capture of low-frequency wind-induced and high-frequency wave-induced coupling energy is realized under complex sea conditions through multi-degree-of-freedom motion decoupling and state observation, so that the wind-induced and high-frequency wave-induced coupling energy is obtained. The energy utilization rate, the attitude stability and the structural safety of the system are effectively improved, and good robustness and engineering adaptability are achieved.

The invention discloses an impedance characteristic remodeling method, device and equipment and a medium, and is applied to the field of wind turbine generators. Wherein the corresponding rotor reference current is determined according to the electromagnetic reactive power, the active power and the corresponding power reference value; correcting the rotor current according to the rotor reference current by adopting a virtual admittance control mode, and generating a corresponding rotor voltage given value; sequentially performing differentiation, filtering and signal extraction processing on the rotor reference current and the rotor current to obtain an impedance remodeling signal; and correcting the rotor voltage given value based on the impedance remodeling signal so as to enable the corrected rotor voltage given value to serve as the control voltage of a rotor side converter in the medium-voltage doubly-fed wind turbine generator after impedance characteristic remodeling. The virtual admittance control mode and the impedance remolding signal mode are adopted to suppress the negative resistance effect of the rotor side converter in the medium-voltage doubly-fed wind turbine generator under voltage disturbance, and therefore the damping effect on external excitation in the sub-super-synchronous frequency band and the high frequency band is higher.

The invention relates to the technical field of force sensors, and discloses a nonlinear decoupling method and system for a three-axis force sensor, and the method comprises the steps: collecting multi-dimensional original data, and carrying out the adaptive noise reduction, dynamic selection of a wavelet basis, and construction of a filtering matrix, and obtaining a noise reduction signal through calculation; constructing a dynamic PINN proxy model, fusing material parameters and environment parameters, outputting a stiffness matrix, establishing an online parameter updating mechanism, calculating the obtained optimization parameters, and constructing an enhanced dynamic compensation matrix; the output stiffness matrix and the enhanced dynamic compensation matrix are fused, and a main singular value is reserved; reconstructing to obtain a reconstructed strain field, establishing a multi-physics field coupling model to compensate quantum-thermal coupling errors, and compensating wire inductive coupling under high frequency; constructing a multi-scale graph structure, strengthening a loss function through physical constraint, performing crystal plasticity hysteresis compensation, fusing multi-scale features, and calculating and outputting a decoupling force value; and an intelligent calibration decision is established, the drift trend is predicted, and the calibration period is dynamically adjusted.

The invention relates to a high-frequency regeneration and rapid propagation method of Enkianthus chinensis tissue culture seedlings. The invention is characterized in that dormant axillary buds of semi-lignified young stems are used as the base. The method comprises the following main steps: sampling and disinfection treatment of explants, inducing of dormant axillary bud germination, inducing of adventitious bud and lateral bud high-frequency generation, strong seedling culture, test-tube rooting culture and acclimatization and transplanting. Finally, regenerative plants which have consistent parental character, excellent quality and strong stress resistance are obtained. According to the invention, lots of seedlings with consistent specification and excellent quality can be obtained within a short period of time, and marketization progress of Enkianthus chinensis is promoted powerfully.

A wireless device may receive one or more configuration parameters indicating entries of a time domain resource allocation table and one or more scheduling offsets. Each entry of the entries may comprise a respective slot offset. The wireless device may receive downlink control information (DCI) indicating a first entry of the entries and a first scheduling offset of the one or more scheduling offsets. The wireless device may receive downlink data in a first slot determined based on a slot offset of the first entry and the first scheduling offset.

The invention provides an intelligent optimization control method for the energy consumption of a packaging production line, and the method comprises the steps: collecting the processing data of the production of products of two specifications of vacuum can sealing and normal pressure can sealing, extracting the load change of a conveying belt during the switching of different specifications, and obtaining the fluctuation range of the load of the conveying belt; predicting the operation power scheduling of the equipment according to the instantaneous peak value and high-frequency fluctuation characteristics of the power demand, and outputting energy consumption peak value prediction data of the equipment during specification switching; determining and outputting an operation time sequence plan according to the energy consumption peak prediction data of the equipment during specification switching; monitoring the dynamic change of the conveyor belt load in real time according to the operation time sequence plan, obtaining the deviation value of the current load and the predicted load, and judging whether the deviation value affects the energy consumption distribution mode of the whole production line or not; and through the new energy consumption distribution result, generating and executing an equipment cooperative operation control instruction in combination with the operation time sequence plan.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses a laser frequency stabilization system based on Faraday anomalous dispersion atomic filtering, which is used for carrying out frequency stabilization on phase modulation multi-sideband laser, and comprises a frequency doubling laser unit which is configured to comprise a fundamental frequency seed light source, an electro-optical modulator, a fundamental frequency amplifier and a frequency doubling crystal; the light splitting module unit is configured to split the frequency doubling light into a main output light path and a monitoring light path; the atomic optical rotation filter is located on the monitoring light path and is configured to maintain the purity of the frequency-stabilized laser spectrum through purification treatment under the control of the axial magnetic field and the temperature; the frequency stabilization unit is configured to be a frequency stabilization light path and a servo system, and the frequency stabilization light path is used for obtaining laser frequency information and generating an error signal; the servo system is used for controlling the frequency of the fundamental frequency seed light source according to the error signal feedback. According to the laser frequency stabilization system provided by the invention, the spectrum purification and frequency stabilization precision can be improved, so that high-frequency stability locking is realized.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) phase filtering method based on a plurality of interferograms, which comprises the following steps of: preprocessing acquired main and auxiliary SAR images to obtain the plurality of InSAR interferograms; dividing the plurality of InSAR interferograms into square interferogram patches along the azimuth direction and the distance direction; converting each interferogram patch from a spatial domain into a frequency domain signal, and constructing a corresponding frequency spectrum amplitude diagram; using a K-means clustering method to cluster and extract an initial low-pass filter in the spectrum amplitude diagram, and constructing a phase filter together with a Kaiser window function; the frequency domain signals of the interferogram plaques pass through a phase filter, the signal frequency band power is reserved, the noise at the high-frequency position is filtered out, meanwhile, the noise at the low-frequency position is filtered out through smoothing operation, and the filtered frequency domain signals are obtained; and the filtered frequency domain signals are converted to a spatial domain, filtered interferogram patches are obtained and spliced, and a filtered InSAR interferogram is obtained. The method is beneficial to popularization and promotion of the development of the current time sequence InSAR interferometry technology and the application in engineering.

The invention belongs to the technical field of artificial intelligence, and relates to a distributed photovoltaic system abnormity monitoring and intelligent early warning method. Key parameters of the distributed photovoltaic system are collected at high frequency, noise filtering and signal normalization processing are carried out on data, then multi-domain feature extraction is carried out, anomaly detection is carried out through auto-encoder reconstruction error analysis, convolutional neural network feature classification and a single-class support vector machine, and an anomaly detection result is output. Establishing a multi-dimensional anomaly evaluation model, and synthesizing obtained evaluation results of all dimensions to obtain a final evaluation result; the method comprises the following steps: carrying out hierarchical classification to identify specific anomaly types, carrying out diagnosis of different types of anomalies, identification of typical anomalies and evaluation of fault severity, outputting the specific anomaly types and severity, and providing detailed diagnosis information for early warning decision making. The system state is comprehensively evaluated, corresponding measures are taken according to the grading early warning mechanism and the abnormity severity degree, and complete distributed photovoltaic system abnormity monitoring and intelligent early warning are formed.

The invention belongs to the technical field of artificial intelligence and data analysis, and discloses a time sequence prediction method and device based on wavelet transform and a large language model. The time sequence prediction method comprises the following steps: obtaining known historical time sequence data; based on the obtained known historical time sequence data, performing time sequence prediction in a future time period by using the trained time sequence prediction model to obtain predicted values of three frequency domain components of low frequency, intermediate frequency and high frequency; adding the obtained predicted values of the low frequency domain component, the intermediate frequency domain component and the high frequency domain component, and carrying out inverse normalization processing on the added result to obtain a final time sequence prediction result; the time sequence prediction model comprises a wavelet decomposition module, a backbone network of a pre-training large language model and a prediction head. According to the technical scheme disclosed by the invention, the accuracy of time sequence prediction can be remarkably improved.

The invention discloses a current source type LCCL double-frequency resonance system and a parameter matching method thereof, which are suitable for synchronous double-frequency induction quenching of complex workpieces such as gears. The system is composed of two current type resonant inverters working at a low frequency and a high frequency respectively and a dual-frequency LCCL resonant cavity, and dual-frequency current superposition is realized through parallel output. The double-frequency LCCL resonant cavity has two parallel resonance points, a mode based on simplified topology is adopted to simplify parameter design, and meanwhile design errors are maintained at a low level. A power matching coefficient is introduced, the ratio of high-frequency output power to low-frequency output power is adjusted by adjusting the value of the power matching coefficient, and the problem of double-frequency power unbalance caused by frequency variation characteristics of load equivalent resistance and the like is solved. The superposed double-frequency current forms a low-frequency envelope embedded high-frequency oscillation composite waveform on the induction heating coil, cooperative control of tooth root deep quenching (low frequency) and tooth surface hard quenching (high frequency) is achieved, and the method has the advantages of being simple in parameter design, high in power decoupling performance, high in operation stability and the like.

The embodiment of the invention discloses an insulation aging early warning method and device for a medium-high frequency transformer and a medium, and relates to the technical field of transformers, and the method comprises the steps: collecting a multiband light intensity distribution signal of a target resin region in the transformer according to a preset collection frequency through a preset multispectral distributed optical fiber sensing array; the insulation state of the target resin area is predicted through a pre-constructed neural network identification model and a multi-spectral distributed optical fiber sensing array, and the predicted insulation state health degree of the target resin area is determined; acquiring real-time environment parameters of the target resin area, dynamically correcting a preset early-warning discharge threshold according to the real-time environment parameters, and determining a current early-warning dynamic discharge threshold; and extracting a plurality of current light intensity parameters in the multiband light intensity distribution signal, predicting an insulation state health degree and a current early warning dynamic discharge threshold through the plurality of current light intensity parameters, and performing insulation defect early warning on the insulation state of the target resin area.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

Plug connectors for use with high speed signals. The connector includes an insulative housing having a base portion and a tongue portion extending from the base portion, and conductive terminals held by the housing. Each conductive terminal has a mating portion held on a side of the tongue portion, a tail portion extending out of the housing, and an intermediate portion held in the base portion. The conductive terminals comprise signal terminals and ground terminals. A shielding member is disposed in the housing and includes features configured to provide shielding to signal terminals and electrically connect ground terminals. The shielding member also includes features configured to improve impedance consistency along conduction paths. Such a configuration meets signal integrity requirements in connectors designed for higher frequencies, while conforming to a standard that constrains mating and mounting interfaces.

The invention provides a noise elimination structure, a compressor and an air conditioner, the noise elimination structure comprises a first noise elimination component which is detachably arranged on a mounting base, and at least part of the first noise elimination component is used for defining a first cavity; at least part of the second silencing component is arranged on the first silencing component in a sleeving mode, and a second cavity is formed by the second silencing component and at least part of the outer wall face of the first silencing component; wherein at least part of the first silencing part is provided with a plurality of silencing holes, so that the first cavity and the second cavity are communicated through the silencing holes, the silencing frequency band of the first cavity is different from that of the second cavity, the first cavity is communicated with the air outlet of the mounting base, and the second cavity is communicated with the air outlet of the mounting base. At least part of gas flowing out of the mounting foundation flows to the first cavity, the silencing holes and the second cavity for vibration reduction; the problems that in the prior art, compressor micro-perforation can only reduce noise in the same frequency band, and full-frequency-band noise control from low frequency to high frequency cannot be effectively achieved are solved.

The invention belongs to the technical field of testing equipment, and particularly relates to a multipurpose high-frequency fatigue testing machine which comprises main control equipment and a fatigue testing machine body, the fatigue testing machine body comprises a bottom machine and an electromagnetic high-frequency generating machine body, and a balance weight table is assembled under the electromagnetic high-frequency generating machine body through connecting rod connection; clamp assemblies are assembled on the surface of the bottom machine and the lower surface of the counterweight table; the multiple balance weights are adjustably stacked on the surface of the balance weight table and used for being matched with the resonance points by replacing balance weights; and the plate clamp is detachably assembled with the clamp assembly and is used for detecting cracks generated in the test process of the test sample. The device can replace manpower to flexibly adjust the counterweight, realizes rapid and convenient operation, can timely detect a cracked plate in a test, and is convenient to timely stop the test before the sample is fractured.