1286 results about "Electromagnetic signal" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses a multi-agent-based gas insulated switchgear fault diagnosis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: obtaining signal data of target equipment, carrying out the feature extraction of the signal data, and constructing a multi-modal feature matrix; time delay features of acoustic and electromagnetic signals are extracted from the multi-modal feature matrix, a GIS propagation model is established, and the space coordinate position of a liberated power source is solved through a wave field inversion algorithm; combining the space coordinate position and the multi-modal feature matrix into a complete fusion feature vector, inputting the fusion feature vector into a dynamic Bayesian model, and outputting a fault type label and a corresponding confidence coefficient; migrating the dynamic Bayesian model based on a migration learning mechanism, and dynamically updating a classification threshold value; inputting the diagnosis history sequence into a time sequence prediction model, and predicting a future operation state; through multi-modal fusion and intelligent reasoning, GIS fault accurate positioning and prediction are realized, and the problems of low precision and poor adaptability of traditional diagnosis are solved.

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention relates to a signal detection and accurate positioning method for a lift-off platform unmanned aerial vehicle, and the method comprises the steps: deploying an unmanned aerial vehicle-mounted platform, capturing a global electromagnetic signal in real time, outputting multichannel original signal data, carrying out the time-frequency domain joint analysis, extracting a frequency hopping period, a modulation mode and pseudo-random code features, and recognizing a target unmanned aerial vehicle signal. A control instruction link signal and an image transmission link signal are separated; the arrival time difference and the arrival phase difference of the target unmanned aerial vehicle signal are converted into a dynamic space coordinate system, a three-dimensional positioning equation set is constructed, and real-time space position coordinates of the target unmanned aerial vehicle are obtained through calculation; and extracting transmitting end characteristic parameters of control instruction link signals, constructing a control signal propagation path model in combination with the real-time position of the target unmanned aerial vehicle, and positioning geographic coordinates of an unmanned aerial vehicle control source by reversely tracking a signal transmitting direction and an attenuation law. The terrain shielding influence is effectively avoided, the target identification accuracy and positioning precision are improved, and the unmanned aerial vehicle control source traceability is realized.

The invention discloses a high-voltage circuit breaker arcing monitoring system and method, and relates to the technical field of power equipment operation state detection, and the method comprises the following steps: S1, synchronously collecting arcing spectral signals and electromagnetic interference vector data under a unified time reference, and carrying out primary differential processing to generate stable base plane signals; s2, inputting the stable base plane signal into the interference identification model, generating a multi-dimensional spectrogram through time-frequency mapping, extracting an interference time sequence boundary, and outputting an interference weight matrix; the arcing monitoring method based on spectrum and electromagnetic signal synchronous acquisition is constructed, interference identification, signal purification, energy analysis and control instruction generation are fused, parameter self-optimization is realized through closed-loop feedback, the arcing identification accuracy and the arc quenching response efficiency are effectively improved, and the arcing monitoring method is suitable for large-scale popularization and application. And the operation safety and the intelligent level of the circuit breaker in a strong interference environment are enhanced.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention belongs to the technical field of geometric pretreatment in computer aided engineering (CAE), and particularly relates to a dirty geometry restoration method for a complex three-dimensional geometric model, which comprises a dirty geometry detection and restoration module based on heuristic rules and geometric topology analysis. By means of a multi-model collaborative alignment and bonding mechanism, high-precision assembly between parts is achieved through geometric feature matching and adjacent domain self-adaptive adsorption; acquiring material attributes provided by the simulation platform, and performing accurate cutting and boundary reconstruction on an intersection domain based on an intersection domain intelligent segmentation algorithm of the material attributes to ensure correct mapping of boundary conditions of a physical field; a geometric Boolean processing engine iterative learning mechanism of a general merging algorithm is integrated, and a high-quality model for simulating a grid is generated through multi-round iterative optimization. According to the method, double challenges of internal dirty geometry of a single model and derivative dirty geometry of intersection / adjacent areas among multiple bodies are solved, and key physical attributes and topological continuity of electromagnetic signal links are ensured and are completely reserved during repair.

The invention relates to the technical field of solid waste treatment, in particular to an intelligent waste sorting method based on multi-sensor fusion. Carrying out multi-level fusion decision making: carrying out sewage reflection suppression on the visible light image; carrying out matching identification and rejecting a liquid pollution area based on a near infrared spectrum; fusing the visible light texture, the spectral feature and the electromagnetic signal to generate a space grid feature vector; identifying the material category through a cascade classifier, and dynamically adjusting the weight of each sensor in the decision according to the environmental parameters; self-adaptive sorting execution: triggering a corresponding sorting execution mechanism according to an identification result; based on the transmission belt speed, the execution mechanism response delay and the material physical attribute, the action advance compensation amount is calculated; the action time of an execution mechanism is adaptively prolonged for the stacked materials according to the stacking hierarchy; and online feedback optimization. Through dynamic compensation and time sequence adjustment, the time sequence consistency of multi-sensor data is ensured, and the influence of band speed fluctuation is overcome.

The invention relates to a metal surface defect identification method and device based on artificial intelligence, equipment and a medium. The identification method comprises the following steps: synchronously acquiring an electromagnetic impedance signal and an optical image of a metal surface based on an eddy current sensor and an industrial camera; generating a spatially positioned interference area mask by quantifying cross-physics field interference intensity of the electromagnetic gradient and the optical edge feature; performing skin effect compensation on an electromagnetic signal by using the mask, and simultaneously performing shadow suppression on an optical image to realize physical field decoupling; a double-branch neural network is called to fuse the decoupled electromagnetic impedance features and optical texture features, and cross-modal associated joint feature expression is established; and analyzing the defect probability heat map based on the fused features, and outputting defect types and coordinates in combination with geometric feature matching. According to the method, the problem of defect feature mixing caused by mutual interference of electromagnetic-optical signals is solved, and the recognition precision and the positioning reliability of the micro defects are remarkably improved.

The invention, which belongs to the technical field of electromagnetic signal monitoring, relates to a rapid judgment method for a spatial spill electromagnetic signal, and the method specifically comprises the following steps: S1, receiving a spill electromagnetic signal in a space; s2, dividing the monitoring total frequency band into a plurality of sub-frequency bands with the same width, rapidly analyzing the obtained electromagnetic signal frequency spectrum in real time based on multi-scale spectrum entropy analysis, and positioning the frequency band where the characteristic signal is located; and S3, converting the frequency spectrum of the frequency band where the characteristic signal is located into a time domain signal, and simultaneously displaying a spectrogram and a time domain waveform. Through spectrum segmentation, multi-scale spectrum entropy analysis, adaptive threshold judgment and frequency domain-time domain combined display, the problems of poor real-time performance, weak anti-noise capability and single dimension in the prior art are solved.

The invention provides an aluminum enameled flat wire online defect detection and self-adaptive deviation correction method and system, and relates to the technical field of enameled wire defect detection.The aluminum enameled flat wire online defect detection and self-adaptive deviation correction method comprises the steps that an aluminum enameled flat wire is immersed in fluid with set viscosity, and a reference fluid resistance signal and a reference electromagnetic signal are collected; fluid resistance signals and electromagnetic induction signals are detected in real time; positioning defect positions of surface defects of the enameled wire; identifying types and distribution of internal defects of the aluminum enameled flat wire; distinguishing defect types through a machine learning algorithm; outputting a corresponding correction scheme through the pre-trained neural network model; and outputting the deviation correction control signal to the correction module for deviation correction operation. Through synchronous acquisition and analysis of fluid resistance signals and electromagnetic induction signals, synchronous detection of surface defects and internal defects is realized, and the limitation of a traditional single detection means is solved; by forming a high-precision three-stage classification system, an interpretability report is generated, and the classification accuracy of the composite defects is remarkably improved.

The invention provides an elevator steel wire rope detection method and device, equipment and a storage medium, and belongs to the technical field of automatic monitoring. The method comprises the steps that electromagnetic signal data in a steel wire rope and vibration signal data of the end of the steel wire rope are obtained; performing feature extraction on the electromagnetic signal data and the vibration signal data to obtain electromagnetic noise features and vibration noise features; performing cross fusion on the electromagnetic noise feature and the vibration noise feature to obtain a first joint noise feature; performing denoising processing on the electromagnetic signal data and the vibration signal data based on the first joint noise feature to obtain denoised electromagnetic signal data and denoised vibration signal data; and performing fault prediction based on the denoised electromagnetic signal data and the denoised vibration signal data to obtain fault information of the steel wire rope. According to the elevator steel wire rope detection method and device, the equipment and the storage medium, the elevator steel wire rope fault detection precision can be improved.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention discloses a poisoning defense method based on characteristic difference analysis and model layer purification. The method comprises the following steps: S1, constructing a poisoning classification model according to an electromagnetic signal sample; adversarial disturbance is introduced, cross entropy loss of disturbed samples is calculated and sorted, and a threshold value is set to distinguish clean samples from poisoned samples; s2, after the poisoning samples are separated out, the poisoning score of each layer of the model is calculated through quantification, and the higher the score is, the stronger the influence of the back door neurons of the layer on the poisoning samples is; s3, generating a pseudo-poisoning sample based on an inversion trigger, analyzing the feature distribution difference between the pseudo-poisoning sample and a clean sample, aligning a feature space and purifying a model layer by optimizing an objective function, and enhancing the distinguishing ability of the model to the sample; s4, repeating the step S3 until the model classification precision converges, and storing the optimal network parameters; and the classification precision of the model on a normal sample and the attack success rate on a poisoning sample before and after defense are evaluated. According to the method, the robustness and the safety of the model are improved, and the method has relatively high universality.

The invention discloses a frequency spectrum situation construction method based on an electromagnetic large model, and the method comprises the steps: 1, obtaining electromagnetic signals in different scenes through simulation generation and actual collection, carrying out the preprocessing, and obtaining a frequency spectrum situation data set in multiple scenes; 2, a Vision Transform architecture is adopted as a large model base, a mask self-encoding mechanism is introduced, and a frequency spectrum situation large model is constructed; 3, performing mask self-supervision pre-training on the spectrum situation large model based on the spectrum situation data set under multiple scenes to obtain a pre-trained spectrum situation large model; 4, performing multi-channel scene fine tuning on the pre-trained spectrum situation large model by adopting data sets of different channel fading scenes; and 5, performing spectrum situation construction by adopting the fine-tuned electromagnetic spectrum large model. According to the method, the construction precision, efficiency and adaptability of the spectrum situation map in the complex electromagnetic environment are remarkably improved.

The invention provides a hydraulic fracturing fracture net distribution form near-field monitoring method, which comprises the following steps of: arranging a plurality of roof monitoring holes in roadways on two sides of an underground coal mine working surface, installing electromagnetic signal sensors in each roof monitoring hole and on anchor rods of roadway stoping sides, and constructing a full-space monitoring network covering a hydraulic fracturing layer; a data interpretation processing module of the electromagnetic signal time-shifting imaging analysis system comprises a multi-field coupling mathematical model for analyzing and deducing real-time imaging of electromagnetic signals; in the hydraulic fracturing process, an electromagnetic signal sensor is used for monitoring a current field, an electromagnetic field and an ion diffusion field in the rock mass fracture development and expansion process, a monitoring result is substituted into an electromagnetic signal time-shifting imaging analysis system for calculation and feature value extraction, and the fracture net distribution form is deduced through the feature value. According to the invention, the expansion path of the fracturing fluid in the rock stratum can be monitored in real time, the three-dimensional distribution form of the hydraulic fracturing fracture network can be inverted, and a new technical support is provided for the inspection of the hydraulic fracturing pressure relief effect of the underground coal mine.

The invention discloses a high boundary sensitivity three-dimensional inversion method based on an electromagnetic gradient constraint matrix, and belongs to the technical field of electromagnetic signal inversion, and the method comprises the steps: constructing an initial inversion model and a target function, and introducing a model roughness item based on electromagnetic gradient constraint into the target function; calculating the electromagnetic gradient anomaly response at each measuring point under each measuring frequency, obtaining an electromagnetic gradient anomaly response vector of each measuring point, performing normalization processing, combining into a matrix, obtaining an overall normalized observation gradient anomaly matrix, and constructing an electromagnetic gradient constraint matrix in a data space; converting the electromagnetic gradient constraint matrix in the data space into a model space through a frequency-depth weighted mapping matrix; and solving the target function by adopting an iterative optimization algorithm until the model converges. According to the method, the gradient constraint matrix based on the electromagnetic field is introduced, the dynamic adjustment of the change amplitude weight of each grid unit in the roughness item of the model is realized, and the target boundary identification precision is improved.

The invention relates to the technical field of signal processing, and provides an intelligent sensing device electromagnetic interference suppression method and system based on AI identification, and the method comprises the steps: obtaining a multi-source electromagnetic signal set of a target intelligent sensing device in a preset monitoring period, and carrying out the electromagnetic interference feature extraction processing of the multi-source electromagnetic signal set, performing interference mode matching processing on the electromagnetic interference characteristic set based on a preset interference suppression model to generate an electromagnetic interference suppression strategy of the intelligent sensing equipment; the electromagnetic interference suppression strategy comprises a frequency band adjustment direction corresponding to an interference frequency band distribution characteristic, a signal gain compensation direction corresponding to an interference intensity fluctuation characteristic and an anti-interference filtering direction corresponding to an interference source correlation characteristic; and performing interference suppression operation on a signal acquisition module of the intelligent sensing equipment according to the electromagnetic interference suppression strategy so as to reduce the electromagnetic signal distortion rate of the intelligent sensing equipment.

The invention is applicable to the technical field of geophysical detection, and provides an underwater target detection method and device based on acoustoelectric joint and width learning, and the method comprises the steps: carrying out the signal collection through an acoustoelectric joint detection network which is disposed in an underwater monitoring region in advance, the method comprises the following steps: acquiring an acoustic signal and an electromagnetic signal synchronously acquired by each detection node in an acoustic-electric joint detection network, performing feature extraction on the acoustic signal and the electromagnetic signal based on a data sliding window mechanism to obtain an acoustic-electric fusion feature matrix, and calculating the acoustic-electric fusion feature matrix according to the acoustic-electric fusion feature matrix. Whether a target object capable of autonomously radiating an acoustic signal and an electromagnetic signal underwater exists in an underwater monitoring area or not is determined through a pre-trained width learning network, so that false alarm interference caused by background noise in an underwater environment is effectively reduced, and the underwater target recognition capability under a weak signal condition is remarkably improved; and the accuracy of underwater target detection is improved.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

The invention discloses a method and a system for constructing a multi-level geologic structure model based on electromagnetic detection, and the method comprises the steps: transmitting electromagnetic signals with different frequencies at all measurement points through an electromagnetic detector, carrying out the resistivity inversion through apparent resistance and impedance phase, obtaining a geologic electrical structure, dividing non-uniform and uniform electrical geology based on electromagnetic static displacement, and obtaining a multi-level geologic structure model. Analyzing an orthogonal component magnetic field to obtain an electromagnetic response parameter and a polarization ellipse parameter; determining an electric field diffusion curve; obtaining an electric field distribution model through finite difference method inversion; deducing underground geological spatial form and distribution law; and finally, calculating an electromagnetic response curved surface to obtain a two-dimensional profile curve, and performing three-dimensional reconstruction to obtain a multi-level geologic structure model. The method not only can improve the precision and reliability of the structure model, but also has good interpretability, and can be directly applied to a construction system of a multilevel geological structure model based on electromagnetic detection.

The invention discloses a series ultrahigh frequency sensing GIS three-dimensional positioning and monitoring system and method, and belongs to the technical field of high-voltage equipment state monitoring. The system comprises a sensing layer, a processing layer, a transmission layer and an application layer, wherein the sensing layer synchronously collects ultrahigh-frequency electromagnetic signals, sound wave vibration signals and SF6 gas state parameters through a plurality of serial multi-physical-quantity sensing nodes; the processing layer eliminates interference based on signal homology verification, calls an intelligent response model to dynamically compensate propagation path errors in combination with a GIS three-dimensional structure model and real-time environment data, outputs accurate three-dimensional coordinates of the partial discharge source by adopting a progressive time difference of arrival positioning algorithm, and identifies fault types and risk levels by fusing multi-dimensional features; the transmission layer encrypts and uploads the diagnosis report; and the application layer maps defect positions in the three-dimensional visual model and automatically generates an operation and maintenance work order according to risk levels. According to the invention, high-precision positioning, multi-source fusion diagnosis and closed-loop operation and maintenance are realized, and the accuracy and intelligent level of GIS equipment monitoring are remarkably improved.

The invention provides a functional composite one-dimensional metal cavity slot array antenna, which belongs to the field of antennae, and comprises a feed cavity and a cover plate located at the top of the feed cavity, a plurality of groups of periodic intermediate-frequency anti-interference metal blocks and high-frequency anti-interference metal blocks are arranged in the feed cavity, and a radiation slot is formed in the cover plate. The polarization conversion structures are arranged on the two sides of the radiation slot in a stepped and open manner to form an undulating shape; an antenna array is also provided. An anti-interference periodic structure is embedded in the feed cavity, the antenna is endowed with anti-interference capacity in multiple different frequency bands, the polarization conversion structures are arranged on the two sides of the radiation slot in a stepped and open mode to form an undulating shape, an electromagnetic signal is input into the feed cavity, linearly polarized electromagnetic waves are radiated outwards through the radiation slot, and the antenna is formed. Linear polarization electromagnetic waves are decomposed into a group of orthogonal components with the same amplitude after passing through the polarization conversion structure, a 90-degree phase difference exists between the two components, circular polarization is formed to radiate towards the space, the polarization conversion structure is simple in structure and convenient to process, and the scattering characteristic of the antenna is improved.

The invention belongs to the technical field of wireless communication networks, and discloses a wireless signal testing method and system for an unmanned vehicle. The method comprises the steps of synchronously collecting electromagnetic signals inside and outside a vehicle, and generating an electromagnetic observation data set; constructing a calibration observation matrix based on a vehicle attitude, position information, a road side reference signal and a communication link state, extracting a candidate interference cluster set and constructing a power curve, and calculating a Pearson's correlation coefficient and Doppler frequency shift to identify an interference type label; determining an interference source position of each interference cluster by adopting a corresponding interference source positioning algorithm according to the interference type label; comprehensively evaluating the communication reliability of the wireless link of the whole vehicle by combining all interference source positions, corresponding labels and power curves and fusing communication link performance indexes and vehicle state information; according to the invention, accurate identification and traceability of wireless link interference of the unmanned vehicle are realized, and the accuracy and real-time performance of communication reliability evaluation are improved.

The invention relates to an FPGA-based GPR ground penetrating radar electromagnetic signal synchronous high-speed data acquisition system and method. The system comprises a front-end signal processing module, a high-speed ADC sampling module, a clock synchronization control module, an FPGA real-time processing module, a target detection and feature extraction module, a mechanism data storage module and a remote data transmission module. On the engineering safety level, the service life of equipment in a strong electromagnetic environment is prolonged by three times through the multi-stage protection design, and the annual failure rate is reduced to 0.1% or below; an ultra-high-speed sampling system (83.2 GS / s) is combined with femtosecond-level clock synchronization, so that 0.5 mm-level thin-layer structure recognition becomes possible, and a technical foundation is provided for geological disaster early warning; a pioneering photoelectric double-channel transmission mechanism is adopted, the electromagnetic interference suppression is improved by 12dB while the 1ps synchronization precision is kept, and the traditional contradiction between high-speed sampling and signal fidelity is broken.

The invention provides a high-frequency electromagnetic excitation cable detection image reconstruction method and system, and belongs to the technical field of underground facility multi-field coupling detection and imaging, and the method comprises the steps: applying high-frequency electromagnetic excitation to an underground medium; receiving an electromagnetic signal transmitted through an underground medium, synchronously collecting a sound signal generated by electromagnetic excitation, and preprocessing the electromagnetic signal and the sound signal; performing structure imaging on the electromagnetic signal by adopting a frequency wave number migration imaging algorithm to obtain medium structure distribution; performing electrical parameter imaging on the electromagnetic signal by adopting a Born approximate inverse scattering imaging algorithm to obtain medium electromagnetic characteristic parameter distribution; sound source distribution reconstruction is carried out on the sound signals through a time reversal algorithm, and medium sound source response characteristic distribution is obtained; and inputting imaging results of the three algorithms into a pre-constructed deep reinforcement learning model for dynamic fusion to generate an underground medium comprehensive parameter reconstruction image.

The invention provides a novel environment-friendly insulating gas surface flashover characteristic evaluation method based on surface charge on-line measurement, which comprises an electric field measurement system based on an electric field induced second harmonic effect (EFISH), a coupling high-voltage surface flashover experiment system, and an evolutionary process of an electric field in a process of applying high voltage to surface flashover, the evolutionary process of charge distribution is obtained based on the electrostatic field source relation, the starting moment of surface flashover is determined by measuring sound, light and electromagnetic signals in the surface flashover process, and the starting area of the surface flashover is observed through a high-speed camera; and associating the relationship between the charge and the surface flashover, and quantitatively explaining the induction effect of the charge on the flashover.

The invention discloses a wire position identification method, system and device of an electromagnetic cruise system and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring an original electromagnetic signal of a target coil in the electromagnetic cruise system; storing a preset number of original electromagnetic signals into a first array; after data storage of the first array is completed, switching to a second array for data storage, extracting data of a preset end segment in the first array and splicing the data with data of a preset start segment in the second array to obtain first spliced data; switching back to the first array for data storage, extracting data of a preset tail segment in the second array and splicing the data with data of a preset initial segment in the first array to obtain second spliced data; and performing Fourier transform and Kalman filtering processing to obtain a first filtering result and a second filtering result, and determining the wire position in the electromagnetic cruise system. By implementing the technical scheme provided by the invention, the lead position identification precision of the electromagnetic cruise system can be improved.

The invention relates to a spectrum occupancy rate calculation method based on full-band spectrum data, and belongs to the field of radio signal spectrum analysis. According to the invention, based on full-band spectrum data at the same sampling time, frequency band division is carried out, and a full-band signal is divided into a plurality of sub-bands; calculating level thresholds of all sub-bands based on the full-band spectrum data at the primary sampling moment; and estimating the occupancy rate based on the frequency band spectrum data at different sampling moments within the measurement time. According to the method, a plurality of interval thresholds in each sub-band are obtained by means of the histogram, the between-class variance and the like, the method can be used for an abnormal electromagnetic signal monitoring scene, the abnormal signal alarm false alarm rate is further reduced, and the electromagnetic space safety supervision capability is improved.