11210 results about "Signal acquisition" patented technology

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault positioning method based on traveling wave detection, and the method comprises the following steps: S1, distributed detection network construction and signal collection: collecting a three-phase current traveling wave signal in real time; s2, time-frequency joint analysis and feature extraction: extracting time-frequency energy distribution features; s3, intelligent screening of effective fault wave heads: screening out the effective fault wave heads; s4, initial arrival time parameter determination: determining the initial arrival time of each detection point; s5, according to topology self-adaptive error correction, transmission path wave velocity deviation is corrected through line parameter dynamic compensation, and clock errors of the asynchronous detection device are eliminated through adjacent node clock difference estimation; and S6, multi-source data fusion and fault positioning: resolving space coordinates of fault points. According to the invention, the method improves the recognition accuracy of fault features, and solves the problems of poor anti-interference capability and high misjudgment rate of a single feature in a conventional method.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention relates to the technical field of battery management systems, and particularly discloses a battery signal acquisition precision optimization method of a BMS (Battery Management System), which comprises the following steps of: dynamically adjusting a covariance matrix of process noise and measurement noise by monitoring the voltage, current and temperature of a battery in real time so as to cope with the change of the working state of the battery; according to the change trend of the charge and discharge rate and the temperature, noise parameters are predicted and corrected, and the adaptability of the system in an extreme environment is improved; in combination with data from a plurality of sensors, weighted average and a self-adaptive adjustment mechanism of a Kalman filter are adopted, so that evaluation of the system on signal quality of different sensors and effective suppression of noise are enhanced; aiming at the sudden change of the battery state, the system can be ensured to quickly respond by automatically adjusting the filtering algorithm, the accurate monitoring of the battery health state is ensured, the stability and the accuracy of the battery management system can be improved, and the problem of estimation error under the extreme working condition of a traditional method is solved.

The invention relates to the technical field of electromagnetic flowmeter calibration, and discloses an electromagnetic flowmeter online calibration system and an acquisition and storage device. The system comprises a signal acquisition module, a data processing module, an error analysis module, a parameter calibration module and a real-time monitoring module. The signal acquisition module captures an original signal waveform through a multi-channel sensor array and acquires flow signal data to generate a state set; the data processing module uses a wavelet packet transformation and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time warping error matching model to obtain an error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional integral differential composite strategy, and adjusts excitation current and electrode sensitivity according to an error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates a calibration instruction in combination with signal stability and power consumption limitation. According to the system, online accurate calibration of the electromagnetic flowmeter is realized, and the measurement accuracy and the equipment operation stability are improved.

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 provides a civil air defense engineering concealed conduit leakage positioning method based on acoustic characteristics, and belongs to the technical field of civil air defense engineering. An acoustic signal acquisition network is constructed by deploying a high-sensitivity hydrophone array, and signals are preprocessed by using a time division multiple access technology and a pulse compression technology; and carrying out time-frequency analysis by applying wavelet transform to extract acoustic features. A time reversal mirror technology is introduced to identify direct propagation and multipath reflection signals, a fluid acoustic coupling propagation equation is constructed to analyze a leakage sound source mechanism, and pipe network topological information and a sound wave speed correction function are combined to compensate a measurement error. Wherein the deep learning pipe network acoustic propagation multi-modal model is fused with a pipeline structure encoder, an acoustic feature extractor and a position prediction decoder, and high-precision leakage positioning and degree evaluation in a complex environment are realized through a three-stage pre-training strategy; the technical problem that it is difficult to accurately locate the position of a leakage point in the complex pipe network environment of civil air defense engineering is solved.

The invention discloses a fault diagnosis and detection system for production automation equipment. The fault diagnosis and detection system comprises a data sensing layer which is used for carrying out multi-mode signal acquisition and real-time preprocessing; the feature extraction layer is used for constructing a recursive block convolution module, capturing transient impact features in four time steps by using an L1-layer gating convolution unit, associating a 16-time-step cross-block periodic degradation mode with an L2-layer sparse attention mechanism, aggregating multi-sensor spatial-temporal features by using an L3-layer global context node, and performing multi-scale feature extraction; the causal reasoning layer is used for establishing a physical constraint driven causal graph engine and outputting a fault propagation path with probability weight; the state modeling layer is used for constructing a continuous health evolution model by adopting a Shenchang differential equation, embedding a physical constraint loss function, and performing equipment full life cycle health state prediction and residual service life estimation in combination with a three-stage memory fusion mechanism of LSTM short-term memory, differentiable neural dictionary medium-term memory and knowledge graph long-term memory; and the decision support layer is used for generating a personalized maintenance work order.

The invention relates to the technical field of chip heat dissipation, and discloses a photoelectric co-packaging heterogeneous chip heat dissipation integration method, device and equipment, and the method comprises the steps: carrying out the finite element modeling analysis of a photoelectric co-packaging heterogeneous chip, and obtaining a three-dimensional thermal characteristic model; performing signal acquisition and frequency domain characteristic analysis on each functional module of the photoelectric co-packaged heterogeneous chip to generate power consumption prediction data; performing temperature field distribution calculation on the photoelectric co-packaging heterogeneous chip according to the power consumption prediction data and the three-dimensional thermal characteristic model, and constructing a temperature outer ring control model and a heat flow inner ring control model; and performing heat dissipation structure layout optimization calculation based on the temperature outer ring control model and the heat flow inner ring control model, and generating a hybrid heat dissipation layout scheme. Dynamic distribution of heat dissipation power and automatic threshold adjustment are realized, frequent mode switching is avoided, and the thermal characteristic stability of a chip is improved.

The invention relates to the technical field of edge calculation application and partial discharge detection, in particular to a partial discharge multichannel signal real-time synchronous acquisition method based on edge calculation. According to the method, the functions of multi-channel signal acquisition, time synchronization, feature analysis, hierarchical storage and the like are integrated at edge nodes, asynchronous acquisition, unified timestamp marking and data synchronous fusion of multiple types of partial discharge signals are realized, and extraction of multi-dimensional feature parameters such as time domain, frequency domain and energy and intelligent event discrimination are locally completed. For abnormal signals, the system realizes classified storage and remote uploading after encryption and compression processing; and for normal signals, dynamic management is carried out through circular caching. According to the method, the accuracy and efficiency of multichannel signal synchronous acquisition are remarkably improved, the data safety and the system adaptive capacity are enhanced, and the method is suitable for real-time online monitoring and intelligent diagnosis of partial discharge of power equipment.

The invention relates to the technical field of nondestructive testing, and discloses an ultrasonic nondestructive testing method and system for a welding seam of a steel structure. The method comprises the following steps: adopting a self-adaptive coupling ultrasonic probe to carry out signal acquisition on the surface of a welding seam of a steel structure to obtain original ultrasonic signal data; performing multispectral adaptive wavelet transform processing on the original ultrasonic signal data to obtain an enhanced ultrasonic signal; performing blind source separation on the enhanced ultrasonic signal to obtain a separated weld defect signal; performing triangular constraint average interpolation and adaptive extended Kalman filtering fusion processing on the separated weld defect signals to obtain filtered weld defect feature data; and performing regularization Gaussian field weld contour reconstruction and defect positioning processing based on the filtered weld defect feature data to obtain a weld contour model and defect position data. According to the invention, the stability and reliability of weld defect detection are improved, and high-precision detection of weld internal defects is realized.

The invention relates to the technical field of electrical fault detection, in particular to a photovoltaic inverter fault detection system based on an electric signal, and the system comprises a signal collection module which continuously collects the output electric signal of an inverter, records the amplitude, frequency and phase of the electric signal, and generates an original signal record; and based on the original signal record, screening the electric signal, eliminating noise and non-characteristic data, and generating screened signal data. According to the method, the time frequency characteristics of the signals are extracted in real time through continuous wavelet transform, and the sensitivity and accuracy of fault detection are improved. According to the detailed feature recognition method, even tiny abnormal waveforms or frequency deviations can be captured, and therefore early warning of potential faults is achieved. Meanwhile, the fault detection threshold value is dynamically adjusted through the environmental parameters, flexible response is allowed according to the actual operation environment change, the false alarm and missing alarm conditions are reduced, and the adaptability and the reliability are improved.

The invention relates to the technical field of signal acquisition, and discloses a signal acquisition method and system for an MEMS gas sensor array, and the method comprises the steps: carrying out the analog-to-digital conversion and temperature and humidity compensation of resistance value signals synchronously acquired by a plurality of sensing units in the MEMS gas sensor array, and obtaining a compensated response matrix; performing fast Fourier transform and dynamic feature extraction to obtain a frequency domain feature matrix of each sensing unit; performing sensing unit crosstalk coefficient calculation and response component decoupling to obtain a decoupling feature vector; carrying out Hilbert transform and adsorption-desorption characteristic extraction to obtain a gas sensitive characteristic sequence; inputting a self-adaptive power spectrum analysis model to carry out cross sensitivity identification processing to obtain a target gas response component; according to the method, the accuracy of target gas detection and concentration quantitative analysis is improved.

The invention discloses a multi-parameter measurement system and method for a power frequency non-partial discharge test transformer, and relates to the technical field of state monitoring of high-voltage test equipment and power equipment, and the system comprises a synchronous trigger unit which is connected to a power frequency voltage signal collection end and is used for detecting a zero crossing point of a power frequency voltage signal and generating a synchronous trigger signal, a power frequency period is divided into a plurality of sub-windows with equal phase angles. According to the multi-parameter measurement system and method for the power frequency non-partial discharge test transformer, the problem of phase mismatch of a power frequency signal and a high-frequency partial discharge signal in traditional multi-parameter measurement is effectively solved through a power frequency phase locked synchronous trigger mechanism and a dynamic noise suppression technology; and the positioning precision of the partial discharge source and the insulation defect diagnosis reliability are improved. The nanosecond-level time sequence error control is realized, and the power frequency coupling interference is inhibited while the details of the high-frequency pulse are kept by combining a phase correlation dynamic filtering strategy, so that the signal-to-noise ratio of the weak discharge signal is improved.

A transmission tower deformation monitoring device based on a phased array radar technology relates to the field of irregular contour metering, and comprises a map construction module used for constructing a regional digital map; the boundary determination module is used for near-field region boundaries; the signal acquisition module is used for acquiring a near-field echo signal of a near-field region through a narrow beam and acquiring a far-field echo signal of a far-field region through a wide beam; the calibration table construction module is used for constructing a signal intensity comparison table; the data processing module is used for determining effective monitoring data; the error calculation module is used for performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; and the parameter determination module is used for determining iron tower deformation parameters. According to the invention, the accuracy of transmission tower deformation monitoring can be improved.

The invention discloses a pipe network on-line monitoring system, which relates to the field of urban capital construction and comprises a sensing deployment module, a signal acquisition module, a signal processing module, a pipe network modeling module, a leakage positioning module, an edge calculation module, an energy scheduling module, an anomaly prediction module, a deposition evaluation module, an early warning visual module and a center management module. According to the method, the recognition capability of the system on a hidden and complex pipeline structure is improved, accurate and efficient three-dimensional modeling can be carried out, the path deviation resistance capability is improved, and the more comprehensive and robust leakage sensing capability is realized; the method is advantaged in that communication burden and response time delay are substantially reduced, sensitivity and foresight of abnormal evolution trend identification are improved, overall identification accuracy of the system is improved, a global optimal risk solution can be efficiently output, a high-risk area is visually displayed, and rapid pre-judgment and decision making of operation and maintenance personnel are facilitated.

The broadband weak current signal detection system comprises a signal acquisition mother board and an upper computer system, the signal acquisition mother board is provided with a signal input interface, a channel switching circuit, a transimpedance amplification circuit, an analog-to-digital conversion circuit, a control core board and the upper computer system, and a three-coaxial BNC interface is adopted. Electromagnetic noise interference in the environment is shielded by a metal noise shielding structure; detecting the switching of a wide measuring range and the switching of an alternating current and direct current coupling mode; weak current signals are converted into measurable voltage signals through a trans-impedance amplifier, and the voltage signals obtained after conditioning are converted into digital quantized values through an analog-to-digital converter (ADC); after being processed by a digital noise reduction algorithm, the data are stored; therefore, the real-time measurement of the broadband weak current signal is realized. The broadband weak current signal detection system has the characteristics of high resolution, good repeatability, high measurement accuracy and good real-time performance, and has good detection capability on weak current signals.

The invention relates to the technical field of laser control, and provides a high-dynamic-range multi-channel laser interference control method and related equipment. Channel signals of a plurality of target channels are obtained in real time according to a plurality of preset photoelectric detectors, time domain-frequency domain-space three-dimensional sampling is performed on the channel signals to construct an original tensor, joint optimization processing is performed on the original tensor to obtain a noise reduction tensor, phase unwrapping is performed on the noise reduction tensor to obtain an absolute phase matrix, and the absolute phase matrix is subjected to noise reduction processing to obtain an absolute phase matrix. Performing dynamic range expansion on the absolute phase matrix to obtain an enhanced phase matrix, performing multi-wavelength calculation and environment compensation on the enhanced phase matrix to obtain a distance matrix, and performing vibration compensation on the distance matrix to obtain a piezoelectric driving signal. According to the channel laser interference control method, through a series of advanced algorithms and processing means such as multi-dimensional signal acquisition, combined noise reduction optimization, precise phase unwrapping, dynamic range expansion and vibration compensation, comprehensive improvement of laser interference measurement signals is realized.

The invention relates to the technical field of steam turbine testing, in particular to a testing system for mismatch correction of a wide-load-interval thermal power generating unit speed regulation model, which comprises a thermodynamic system component, a speed regulation system device, a dynamic signal acquisition device and a control device, a dynamic signal processing module analyzes the relevance between main steam pressure sudden change and control valve opening oscillation in real time, a dynamic response database including a time sequence is constructed, and a control valve opening feed-forward compensation instruction and a combustor fuel distribution gradient adjustment signal are generated according to correction parameters. Power over-modulation wave crests caused by sudden load change are suppressed, and primary frequency modulation action delay is reduced; and the simulation verification module compares measured data with model output through an error feedback mechanism, dynamically adjusts high-pressure cylinder flow characteristic curve calibration parameters, optimizes a control strategy in combination with a network-related safety margin evaluation result, and realizes improvement of dynamic response precision in a full peak regulation interval and enhancement of power grid frequency stability.

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention discloses a multichannel high-precision PAF signal synchronous acquisition system and a multichannel high-precision PAF signal synchronous acquisition method, and relates to the technical field of phased array signal acquisition and processing, the multichannel high-precision PAF signal synchronous acquisition system is characterized in that a hydrogen atomic clock time-frequency distribution module is in signal connection with a plurality of signal acquisition processing boards, and each signal acquisition processing board comprises a double-loop phase determination clock circuit and an RFSoC circuit; and the double-loop phase determination clock circuit is in signal connection with the RFSoC circuit. The time-frequency distribution module adopts a time-frequency signal output by a hydrogen atomic clock as a reference, so that the synchronization precision is ensured; the clock architecture of the acquisition processing board card adopts a phase determination mode, so that a Tile clock and a PL logic clock in the board are strictly in phase with an external input reference clock, and ADC acquisition synchronization of all channels is ensured; a phase clock circuit design is determined by adopting double loops, and all output clocks and an input reference clock keep a strict phase relationship; the system adopts a three-step synchronization process, so that the synchronous acquisition of hundreds of paths of PAF signals reaches the ps-level precision.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The invention belongs to the technical field of liquid level measurement and industrial process control, and particularly relates to a radar liquid level meter system fusing an adaptive algorithm and multi-modal data. Comprising a data acquisition unit, a signal acquisition and preprocessing unit, a signal denoising and optimizing unit, a data fusion and anomaly detection unit, a self-adaptive calibration and drift compensation unit and a display and alarm terminal. The system has the advantages that the radar liquid level meter, the auxiliary sensor, signal processing, data fusion, anomaly detection, self-adaptive calibration, drift compensation and other technical means are organically combined, and the system can achieve high-precision and real-time monitoring of the liquid level under the complex working condition.

The invention relates to the technical field of nondestructive testing, in particular to a multi-modal fusion metal microcrack ultrasonic detection system and method, and the system comprises an ultrasonic signal acquisition module, a signal preprocessing module, a feature alignment network module, an image segmentation module, a time-frequency analysis module and a segmentation result fusion module. According to the system, three modes of electromagnetic ultrasound, laser ultrasound and phased array ultrasound are utilized to detect a metal sample, and real-time nondestructive signal data acquisition and pretreatment are realized. The feature alignment network aligns and fuses the three modal signal features, and the U-Net segmentation neural network performs submillimeter crack segmentation on the fused features. And the time-frequency analysis module extracts signal frequency and amplitude information, and evaluates the breaking strength in combination with the crack label. Finally, a segmentation result fusion module integrates crack labels and time sequence features, and whether cracks exist or not is classified through a weighted convolutional neural network. According to the system, the detection performance, the practicability and the adaptability are remarkably improved, and a new scheme is provided for precise detection of metal microcracks.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention provides an antenna radio frequency performance test system and method, and relates to the technical field of antenna testing, the system adopts an intelligent signal source module, uses a quantum random number generator to generate a test signal of an ultra-wide frequency spectrum, and an adaptive matching network module can automatically adjust impedance according to impedance characteristics of an antenna to be tested. The space electromagnetic environment reconstruction module accurately simulates various electromagnetic environments through an electromagnetic radiation source and a wave-absorbing material, the multi-dimensional signal acquisition module simultaneously acquires electric field, magnetic field, phase and frequency spectrum information, and the intelligent analysis and diagnosis module analyzes signal data by using an artificial intelligence algorithm and performs fault diagnosis based on a machine learning model. In general, the system and the method improve the comprehensiveness, the accuracy and the intelligentization of the antenna test, and provide a new test solution for the field of wireless communication.

The invention relates to the technical field of digital audio processing, in particular to a digital audio processing method and system for sound equipment. The system comprises an audio signal acquisition module, a signal analysis module, an adaptive adjustment module, a howling suppression module and a sound effect optimization feedback module. The audio signal acquisition module acquires data such as frequency spectrum and amplitude of the sound equipment in real time. And the signal analysis module generates a signal analysis report based on a non-negative matrix factorization algorithm and a generative adversarial network algorithm. And the adaptive adjustment module automatically adjusts audio parameters through a fuzzy logic control algorithm. The howling suppression module suppresses howling noise by using an adaptive feedback cancellation algorithm. And the sound effect optimization feedback module adopts a Bezier curve to enhance the sound effect and realize frequency, phase and space positioning optimization. Through cooperative processing of audio acquisition, accurate analysis, adaptive adjustment, howling suppression and spatial sound effect optimization, the audio quality and stability are improved, and the audio spatial effect and user experience are enhanced.

The invention discloses a GIS partial discharge fault detection device and method based on D-S evidence multi-source information fusion, and relates to the technical field of power system control, and the device comprises a signal collection module which is used for collecting an electromagnetic wave signal, a mechanical vibration signal, an ultrasonic signal and a partial discharge optical radiation signal generated by the partial discharge in GIS equipment; the GIS partial discharge fault detection module is used for processing, storing and sending the signals acquired by the signal acquisition module; the coordinator is used for realizing data transmission between the GIS partial discharge fault detection module and the background terminal; and the background terminal is used for receiving the processing information of the GIS partial discharge fault detection module and judging the fault type of the GIS equipment. According to the GIS partial discharge fault diagnosis system, various signals generated by partial discharge in the GIS equipment are detected through the information acquisition module, and the relationship between the characteristic data and the GIS partial discharge fault is established, so that diagnosis of the GIS partial discharge fault is realized, and the fault judgment efficiency of the background terminal is improved.

The invention discloses a mechanical transmission system fault trend prediction system based on dynamic feature recognition, and relates to the technical field of mechanical state monitoring. Comprising the following steps: synchronously acquiring a load torque signal and a lubrication state parameter signal of a transmission system and vibration acceleration signals of a plurality of measuring points through a signal acquisition module; the working condition decoupling characteristic generation module carries out time-frequency analysis on the vibration signal, calls a pre-stored load disturbance spectrum template according to a load torque signal to carry out adaptive differential processing so as to eliminate load fluctuation interference, and calls a correction rule set according to a lubrication state parameter signal to carry out form recombination on the signal so as to compensate the lubrication state influence; and finally outputting a working condition decoupling feature representing the health state of the mechanical part. And the trend prediction module calculates and obtains fault development trend and residual life estimation data through a pre-trained fault prediction model. According to the method, the dynamic characteristics representing the essential degradation of the part are effectively extracted, and the accuracy and reliability of fault trend prediction of the mechanical transmission system are improved.

The invention relates to the technical field of adaptive control, in particular to an electrical automation adjustment method and system, and the method comprises the following steps: obtaining a three-phase current signal, collecting single-cycle high-frequency sampling, extracting an instantaneous extreme point, recording a time coordinate and an amplitude value, calculating a time interval and a change rate, screening the distribution of the extreme point, and obtaining a harmonic distortion characteristic parameter. According to the method, on the basis of high-frequency sampling, instantaneous extreme point extraction, time interval and change rate calculation, harmonic distortion feature description enhancement, extreme value growth rate and phase drift rate are utilized, time periods with prominent fluctuation amplitude are screened, harmonic change trend prediction precision is improved, and the harmonic change trend prediction precision is improved through zero-sequence component and phase offset calculation in combination with periodic change features. The harmonic source is accurately separated, the harmonic cause identification capability is improved, the compensation current is calculated based on the harmonic source, the injection opportunity is optimized, the compensation parameter is adjusted before the harmonic peak value, and the compensation response speed and the matching precision are improved.