6733 results about "High Frequency Waves" patented technology

The invention relates to an underwater image enhancement method based on frequency domain analysis and visual Mama, comprising the following steps: constructing a multistage wavelet transform network, the network being an encoder-decoder structure, the encoder comprising a space-frequency feature fusion module and discrete wavelet transform; the decoder comprises a space-frequency feature fusion module and inverse discrete wavelet transform; an underwater image is decomposed into a low-frequency part and a high-frequency part through discrete wavelet transform in an encoder, spatial feature extraction and frequency feature extraction are conducted on the low-frequency part and the high-frequency part through corresponding space-frequency feature fusion modules based on Mama and frequency domain convolution respectively, and splicing is conducted on the low-frequency part and the high-frequency part along a channel in the decoder according to the sequence of low frequency and high frequency. Reconstruction is carried out through inverse discrete wavelet transform; and inputting the underwater image data into the multistage wavelet transform network to obtain enhanced underwater image data. Compared with the prior art, the method has the advantages of improving the enhancement quality, reducing the complexity, quickly enhancing and the like.

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 power flow generating capacity prediction method based on STL decomposition and multi-model fusion, which adopts an STL decomposition method to perform trend term, season term and residual term decomposition on historical generating capacity data, optimizes the decomposition process through a Bisquare weighting function and an internal loop iteration mechanism, and enhances the processing capacity for abnormal values and high-frequency fluctuations. The method comprises the following steps: extracting multi-scale frequency domain features by using a TimeNet model in combination with fast Fourier transform (FFT), predicting a trend term and a seasonal term, and performing accurate modeling on a residual term by using an Itransform model in combination with a self-attention mechanism and gating residual connection. Meteorological factors related to the tidal current generating capacity are screened, prediction is carried out in combination with historical operation data, and high-precision prediction of the tidal current generating capacity is achieved. The method has high adaptability and robustness, can effectively deal with multi-source influence and high-frequency fluctuation in tidal current power generation data, and provides an accurate and stable prediction result.

The invention discloses a safety risk assessment system based on slope construction, relates to the technical field of slope construction, constructs a complete system composed of a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module, and acquires multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration at high frequency. The time sequence analysis and disturbance feature extraction algorithm is combined to realize the rapid identification of the sudden disturbance event; risk score prediction is further carried out based on a polynomial regression model, and multi-level early warning is generated through comparison with a set threshold value; and finally, by combining the construction state, the support parameters and the disturbance intensity, a safety control suggestion is dynamically generated, so that the response speed and prediction precision of the system to the sudden slope instability risk are effectively improved, and real-time perception and intelligent management and control of the risk in the slope construction process are realized.

The invention relates to the technical field of power distribution system reliability analysis, in particular to a power distribution system reliability analysis method and system. The method comprises the following steps: performing multi-dimensional data acquisition on a power distribution system through a sensor network, and performing multi-dimensional data fusion to obtain multi-dimensional associated data; performing comprehensive operation data set construction on the multi-dimensional associated data to obtain a comprehensive operation data set; performing time sequence data segmentation according to the comprehensive operation data set to obtain a rapid fluctuation characteristic sequence and a slow change long-term trend sequence; performing time sequence decomposition on the rapid fluctuation characteristic sequence and the slow change long-term trend sequence to obtain high-frequency fluctuation characteristic data and long-term trend characteristic data; and performing feature component analysis on the high-frequency fluctuation feature data and the long-term trend feature data to obtain a multi-scale feature set. Through risk factor correlation analysis and reliability network model construction, the reliability analysis precision of the power distribution system is improved.

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 provides an assembly line electronic tag management system based on multi-frequency fusion, and relates to the technical field of the Internet of Things, and the system comprises a dual-band module which is used for integrating a high-frequency and ultrahigh-frequency dual-band radio frequency electronic tag on the surface of a target object, and forming a tag dual-mode function; and the signal processing module is used for synchronously acquiring the high-frequency positioning signal and the ultrahigh-frequency identification signal through the multi-band reader-writer array on the basis of a tag bimodal function so as to obtain a preprocessed signal flow with aligned time domains. According to the invention, the efficiency of label identification, data transmission and production management is improved in a complex scene, errors and cost are reduced, and efficiency and quality are improved.

A base station may transmit, to a wireless device, configuration parameters for semi-persistent scheduling (SPS), indicating periodic downlink resources for SPS physical downlink shared channel (PDSCH) transmissions. The base station may transmit, to the wireless device, a downlink control information (DCI). The DCI may indicate: an activation of the periodic downlink resources, a PUCCH resource for receiving feedback information corresponding to a plurality of SPS PDSCH transmissions via the periodic downlink resources, and a total downlink assignment index (DAI) incremented based on a number of the plurality of SPS PDSCH transmissions corresponding to the PUCCH resource. The base station may receive, from the wireless device via the PUCCH resource, feedback information bits for the plurality of SPS PDSCH transmissions based on the total DAI.

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 provides an unmanned aerial vehicle obstacle avoidance method and system based on inertial navigation, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance systems. The problem that an existing unmanned aerial vehicle is insufficient in obstacle avoidance capacity under the environment that GPS signals are weak, vision is limited or a sensor detection blind area exists is solved. Acquiring broadband high-frequency dynamic response data output by the inertial navigation system of the unmanned aerial vehicle in real time; establishing or acquiring a reference high-frequency dynamic response model when the unmanned aerial vehicle is in a specific flight state and a power system working mode and is not influenced by an external obstacle; the signal is dynamically responded according to high frequency; identifying whether a specific air medium disturbance echo feature formed by disturbance generated by motion of the unmanned aerial vehicle through an external obstacle exists or not; and finally, generating and executing an obstacle avoidance flight instruction according to the estimation information. The method does not depend on a traditional external environment sensor for initial detection, physical interaction is sensed through the INS, and the autonomous obstacle avoidance capacity and safety of the unmanned aerial vehicle in the complex and challenging environment are improved.

The invention belongs to the technical field of connectors and discloses a design method for a low-loss cable connector without resonance points, comprising: designing a spiral gradient groove structure on the surface of an inner conductor based on the impedance continuity principle, establishing a composite gradient impedance theoretical model of the connector, constructing a dielectric constant gradient distribution model of a double insulating layer, and setting a transition function between an inner layer and an outer layer; solving the S parameters and electric field distribution of the connector through electromagnetic-thermal coupling finite element simulation, optimizing the spiral angle θ and the gradient index n, establishing a quantitative model of the surface roughness of the conductor and high-frequency loss, and determining a threshold range of the surface roughness through calculation of the quantitative model; performing global optimization of structural parameters based on a multi-objective genetic algorithm, outputting a theoretical design parameter set and a report collection of performance verification, eliminating the resonance point problem of traditional connectors, and providing excellent low-loss performance in the full frequency band, which is particularly suitable for high-frequency and high-power transmission environments.

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 embodiment of the invention provides a signal processing circuit, a chip and electronic equipment. The signal processing circuit comprises an analog-to-digital conversion module and a signal amplification module, the first chopping switch module is used for modulating the differential input signal to high frequency under the control of a preset clock signal and inputting the signal to the signal amplification module; the second chopping switch module is used for modulating the differential output signal output by the signal amplification module to a low frequency under the control of a preset control signal and inputting the low frequency to the analog-to-digital conversion module; and the second chopping switch module also enables the analog-to-digital conversion module to establish sampling under the control of a preset control signal. According to the application, the second chopping switch module is multiplexed as a sampling switch of the analog-to-digital conversion module, so that the problem that on-resistance is increased due to the fact that a chopping demodulation switch and the sampling switch are arranged at the same time can be avoided.

The invention provides a high-frequency eddy current-based metal pipeline conductivity measurement method, which comprises the following steps: designing an absolute eddy current sensor, positioning a sensor coil on the inner side of a metal pipeline and coaxially arranging the sensor coil and the pipeline, and introducing high-frequency alternating current into the coil to generate eddy current; establishing a simplified high-frequency eddy current detection analysis theoretical model, and setting geometric dimensions of a sensor and a pipeline; a Bessel function and a spatial resolution expression are used for analyzing the relation between the real part of the eddy current impedance change under high-frequency excitation and the conductivity of the pipeline; setting the conductivity of the metal pipeline according to the analysis result, and fitting the relation between the impedance change and the conductivity through a power function; and according to a coil impedance measurement result, combining the fitted power function curve, and performing inversion to obtain the actual conductivity of the metal pipeline. According to the invention, the conductivity of the metal pipeline can be effectively measured.

The invention discloses an intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency domain separation, and provides a synchronous parameter acquisition and frequency domain separation processing scheme for solving the problems of temperature detection lag, insufficient frequency domain interference processing and weak dynamic adaptation capability in the prior art. Transient current, voltage and change rate (a first parameter group) of an electromagnetic coil are synchronously acquired, and high-frequency fluctuation (greater than or equal to 10kHz) and low-frequency disturbance (1t, 1t) are converted into low-frequency fluctuation (greater than or equal to 10kHz) by combining environment temperature, cookware materials and user preferences (a second parameter group); 10kHz) is used for frequency spectrum separation. Phase difference features are extracted through band elimination filtering and FFT, heating priorities are corrected in combination with dynamic compensation factors, and a segmented heating curve is generated through time-frequency domain weighted fusion. And the system adjusts PWM parameters in real time, and triggers adaptive protection when detecting that the phase is abnormal or the power frequency exceeds the limit. According to the method, the limitation of single-frequency-band detection is broken through, high-frequency noise suppression and low-frequency disturbance compensation are realized, and accurate temperature control of ferromagnetic / non-ferromagnetic cookware is supported.

The invention discloses a variable-frequency ash removal device for improving photovoltaic power generation efficiency, and a control system of the variable-frequency ash removal device can dynamically adjust the output frequency and amplitude of a signal generator according to cleaning requirements. Particularly, when different types of dust are cleaned, the system can automatically adjust the vibration frequency, so that the cleaning process is more accurate. Large-particle dust is effectively removed through low-frequency and large-amplitude vibration, and small-particle dust is finely cleaned through high-frequency micro-vibration. The dynamic adjustment of the frequency and the amplitude ensures that each dust type can be thoroughly removed, and the problem of poor effect caused by fixed frequency in a traditional cleaning method is avoided.

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 discloses an online monitoring system for partial discharge of a main transformer. The online monitoring system specifically comprises a series of modules including sensors, detection equipment, a data acquisition module, a data processing module, a data transmission module and an online monitoring platform, wherein the sensors are reasonably arranged outside the transformer by adopting multi-band combination of ultrahigh frequency, high-frequency current, ultrasonic waves and the like; the detection equipment realizes high efficiency and flexibility through a C language underlying driver and an assembly language data interface, and has various anti-interference and fault early warning technologies; the data acquisition module is used for conditioning an original signal and improving the data quality by adopting various filters; reliable and real-time data transmission is ensured by adopting a TCP / IP protocol; the data processing module comprises a time domain and frequency domain conversion sub-module and other sub-modules for auxiliary diagnosis; the online monitoring platform is based on Web, and is rich in function and extensible; the system monitors a plurality of main transformers in a transformer substation, collects signals through sensors, processes the signals through the modules and transmits the signals to a platform, meanwhile, the system is subjected to regular inspection, software and hardware upgrading optimization and the like to guarantee stable operation of the system, and support is provided for safety of an electric power system.

The invention relates to a residential area charging pile load prediction method based on deep learning and load peak correction, and the method comprises the following steps: A), carrying out the preprocessing of residential area charging data, including abnormal value identification, missing value completion, standardization and the like; b) adopting MIC (Microphone) to screen characteristics with relatively high load correlation, and taking the characteristics as input of a load peak value and load trend prediction model; c) constructing an LSTM load peak prediction model; d) carrying out CEEMDAN preliminary decomposition on the load trend, carrying out aggregation reconstruction on each sequence by using K-Means, and then carrying out secondary decomposition on high frequency by using VMD; e) a sequence obtained through secondary reconstruction is transmitted into an Informer prediction model; f) replacing a load peak value to complete curve shape correction; and G) calculating an error index, and evaluating the performance of the model. The method has the advantages that charging behaviors, historical loads, external characteristics and the like are selected as input according to the charging load characteristics of the residential area, rules in load data are fully mined through secondary decomposition and reconstruction, the load peak value is corrected, the prediction precision is improved, and therefore the transformer capacity of the power distribution network is optimized.

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 relates to the technical field of power systems, and discloses a broadband oscillation risk assessment method and system for offshore wind power through a flexible direct current sending-out system, and the method comprises the steps: dividing a broadband oscillation risk assessment frequency band into a medium-high frequency band and a sub-super-synchronous frequency band; aiming at different broadband oscillation risk assessment frequency bands, respectively establishing an offshore wind power plant side impedance model and a flexible direct current converter side impedance model in a corresponding offshore wind power flexible direct current sending-out system, and determining an equivalent impedance frequency characteristic curve of the offshore wind power plant side and the flexible direct current converter side. The broadband oscillation risk of the offshore wind power through the flexible direct current sending-out system is comprehensively evaluated by using the equivalent impedance frequency characteristic curve, the topological structure and the operation condition are fully considered, and the accuracy and adaptability of evaluation are improved. And when the broadband oscillation risk occurs, the system is adjusted in a targeted manner according to different evaluation frequency bands, so that the broadband oscillation risk is effectively eliminated, and the operation stability of the offshore wind power output system through the flexible direct current is enhanced.

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 provides an online monitoring method and device for partial discharge of GIS equipment, and relates to the field of online monitoring of high-voltage electrical equipment, and the method comprises the steps: collecting a partial discharge signal in the GIS equipment, and synchronously obtaining the data of an environment noise sensor; processing the acquired ultrahigh frequency signal based on the data of the environmental noise sensor to obtain a preprocessed analog signal; converting the preprocessed analog signal into a digital signal through a high-precision analog-to-digital converter; key characteristic parameters of the partial discharge signals are extracted from the digital signals; dynamically adjusting a discharge detection threshold according to the extracted key characteristic parameters by applying a hybrid optimization algorithm; and based on the adjusted discharge detection threshold, setting an event trigger condition, and when the detected partial discharge signal amplitude and the trigger frequency reach the event trigger condition, sending out an alarm signal. According to the invention, the partial discharge phenomenon can be detected and alarmed in time, so that the operation reliability and safety of the GIS equipment are improved.

The invention relates to a near-surface defect positioning method based on a laser ultrasonic surface wave, which comprises the following steps of: extracting a defect tiny inverse signal and calculating reflection time delay by utilizing a high-frequency surface wave signal excited by laser through a wavenumber domain method to determine a defect position, and determining the burial depth of the defect through a reflection wave amplitude value of the defect; comprising the steps of S1, laser ultrasonic finite element simulation; s2, acquiring high-frequency laser ultrasonic signals; s3, direct wave and reflected wave extraction and time delay calculation; s4, determining defect space coordinates; and S5, determining the burial depth of the defect. The method makes full use of the advantages of high frequency and long propagation distance of surface waves, can effectively solve the problem of defect detection and positioning of the tested piece, can quickly detect the submillimeter defects, is high in positioning precision, and is suitable for positioning and detecting the submillimeter defects on the near surface of a structure.

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.