1874 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 discloses an unmanned aerial vehicle signal identification method and system in a complex electromagnetic environment, and belongs to the field of unmanned aerial vehicles. According to the invention, the form of the liquid metal channel of the liquid metal antenna is adjusted to adapt to the target frequency band, and regional electromagnetic signals are collected in all directions. And generating spatial distribution data including the unmanned aerial vehicle and the interference direction based on the phase difference and preset features. The deformation of the liquid metal droplets is regulated and controlled through the electrowetting voltage, so that the antenna main lobe is aligned with the unmanned aerial vehicle, and the side lobe suppression area covers an interference source. Then, a target direction signal set is collected, and time domain fluctuation, frequency domain distribution and spatial domain direction multi-dimensional features are extracted; and inputting the multi-dimensional features into a joint probability model constructed by a Bayesian network, and outputting posterior probability information. Markov chain Monte Carlo self-adaptive sampling is utilized, the classification confidence coefficient is calculated through iterative convergence, the signal category is judged according to a threshold value, and the unmanned aerial vehicle signal recognition accuracy can be improved in the complex electromagnetic environment.

The invention discloses a dam termite cave positioning method based on transient electromagnetism carried by an unmanned aerial vehicle. The method comprises the following steps: constructing an unmanned aerial vehicle transient electromagnetic detection system; preprocessing the received secondary induction electromagnetic field signal, and obtaining a comprehensive electromagnetic signal according to the response difference of the multi-frequency signal; inputting the integrated electromagnetic signals into a pre-trained deep learning neural network model, identifying characteristic signals related to termite acupoints in the integrated electromagnetic signals, and outputting position coordinates of suspected termite acupoints; a dam surface thermal imaging image obtained by a thermal imaging module is combined for secondary verification, and termite cave position information is transmitted to a ground control terminal in real time through a communication module carried by the unmanned aerial vehicle. According to the invention, efficient, accurate and lossless positioning of the termite cave of the dam is realized, underground electromagnetic signals and surface heat distribution information of the dam are rapidly obtained, and accurate termite cave position coordinates are output.

The invention relates to a charging pile gun state intelligent detection method and system, and the method comprises the steps: collecting a charging gun head electromagnetic signal, a gun handle temperature gradient and an interface contact surface mechanical strain signal in real time based on a multi-mode sensor array, constructing a space-time coupling data cube, separating the independent components of an electromagnetic field, a temperature field and a strain field, and obtaining a space-time coupling data cube; the multi-physical field coupling interference is eliminated; designing a dynamic cross compensation mechanism to correct mutual influence among sensor data; extracting multi-domain features and carrying out adversarial weight fusion to generate a comprehensive state representation vector; state scores are generated in combination with similarity matching of the dynamic reference library, and abnormal grade classification is achieved; according to the method, the problems of multi-field coupling interference separation, dynamic drift compensation and multi-dimensional feature joint modeling can be solved, the accuracy and real-time performance of poor contact of the charging gun, overheating early warning and mechanical deformation detection are improved, and a guarantee is provided for safe operation of charging facilities.

The invention relates to the technical field of electronic information, in particular to a multi-band cooperative interference gallium nitride phased-array antenna control method and system. The method comprises the steps of extracting key parameters of electromagnetic signals, and generating a cooperative interference strategy combination; mapping the frequency band optimization weight to a dynamic working mode of the gallium nitride power amplifier unit, adjusting the radiation pattern characteristics of the reconfigurable dipole array according to the power distribution parameters, and constructing a composite beam forming framework; phase weighting and spatial multiplexing are carried out on the interference signals of all the frequency bands based on the composite beam forming architecture, and a multi-beam interference cluster is generated; thermodynamic state data of the gallium nitride power amplifier module in the multi-beam interference cluster generation process are collected in real time, and heat dissipation parameters of the liquid cooling system are regulated and controlled according to the thermodynamic state data. Intelligent collaboration and accurate pointing of multi-band interference signals are realized, the interference coverage range and the energy utilization rate are improved, and an efficient interference system integrating electromagnetic sensing, multi-beam collaboration and thermal management is formed.

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 the technical field of unmanned aerial vehicle control, in particular to a low-altitude operation unmanned aerial vehicle cooperative control method, which comprises the steps of acquiring a broadband electromagnetic signal of a surrounding environment of a communication iron tower; identifying electromagnetic interference components of the broadband electromagnetic signals based on spectrum characteristic analysis, and decomposing characteristic parameters of the electromagnetic interference components; positioning the spatial orientation of an interference source according to the frequency spectrum energy difference of electromagnetic interference, and constructing an electromagnetic risk topological graph in combination with a three-dimensional point cloud model of the communication iron tower; generating a dynamic compensation amount to correct the trajectory of the unmanned aerial vehicle by using the electromagnetic interference component characteristic parameters, and reconstructing the unmanned aerial vehicle cooperative formation topology based on the interference source spatial orientation; and generating an anti-interference obstacle avoidance constraint according to the electromagnetic risk topological graph and the reconstructed cooperative formation topology, and cooperatively controlling the unmanned aerial vehicle to complete scanning in a preset trajectory interval. The technical problems of unmanned aerial vehicle flight attitude instability and high collision risk caused by communication iron tower electromagnetic environment interference are solved, and rapid and accurate cooperative control of communication iron tower unmanned aerial vehicle low-altitude operation patrol inspection is realized.

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 magnetotelluric signal noise suppression method and system based on complexity driving, and the method comprises the steps: firstly calculating the complexity feature CS of a signal segment, and then constructing and training a noise reduction model based on a multi-path attention residual denoising network, the network is formed by connecting a plurality of feature recovery modules with heterogeneous structures in parallel to form different main branches, different main branches are respectively provided with a shallow convolution module CBL, a channel structure modeling module MACNet and a global attention modeling module DHVT, each main branch is provided with a complexity sensing gating unit taking a complexity feature CS as input, and an output signal of the complexity sensing gating unit is input into a feature recovery module of the main branch in cooperation with a noisy signal segment; the path scheduling mechanism guides whether the feature recovery module of the main branch participates in data processing or not for an output result of the complexity sensing gating unit on each main branch; and finally, performing noise reduction on the actually measured magnetotelluric signal by using the trained multi-path attention residual denoising network.

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.

A novel connection port is described for connecting a signal transmitter to an apparatus configured to wirelessly communicate video and images from a pipe inspection system. Such a connection port may establish an electrically conductive pathway to a push-cable and camera head further disposed in cable storage drum that the wireless communication apparatus may couple thereto. A signal transmitter, having an established electrically conductive pathway to the push-cable and camera head, may transmit electromagnetic signals that may be emitted by the push-cable and camera head and further be located at the ground surface via a utility locator device.

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

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 discloses an intelligent identification system and method based on time-frequency feature fusion and adaptive classification, and relates to the technical field of power equipment state monitoring. Comprising an ultrahigh frequency signal processing unit, a filter amplification unit, an analog-to-digital conversion unit, a digital signal preprocessing unit, a multi-dimensional feature extraction unit, a discharge phase analysis unit, a classification model optimization unit, an interference elimination unit, a defect type identification and diagnosis unit and a discharge source positioning unit. The ultrahigh frequency signal processing unit is used for collecting and transmitting partial discharge weak electromagnetic signals, and the filtering and amplifying unit is used for filtering, eliminating interference and amplifying signal amplitude. Through the ultrahigh frequency signal processing unit, the filtering and amplifying unit and the analog-to-digital conversion unit, weak electromagnetic signals are accurately collected, interference is efficiently filtered out, the signals are amplified and converted, the accuracy and effectiveness of the detection process are improved, and the misjudgment risk is reduced through pulse segmentation and normalization, multi-dimensional feature extraction and classification model establishment and optimization.

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 relates to the technical field of electromagnetic inversion, and particularly discloses a transient electromagnetic artificial intelligence inversion method based on physical driving, and the method comprises the steps: generating an equivalent anti-magnetic flux transient electromagnetic signal through an intelligent transmitter controlled by an FPGA, and generating a real-time electromagnetic field data set after noise suppression; according to the real-time electromagnetic field data set, carrying out similarity calculation on adjacent measuring point data to obtain an online quality factor, and further generating a dynamically optimized electromagnetic field data set; based on the dynamically optimized electromagnetic field data set, executing an equivalent anti-flux transient electromagnetic fast inversion algorithm through an edge computing node, and outputting an inversion confidence index; and according to the inversion confidence coefficient index, a launching parameter optimization function is constructed, and a dynamic launching control instruction set is generated, so that the precision and efficiency of electromagnetic detection are improved, the flexibility and the intelligent level of the system are enhanced, and the method has important technical significance and application value in efficient and accurate electromagnetic detection in a complex environment.

The invention discloses a directional broadcast multi-band interference suppression method based on dynamic beam forming, and relates to the technical field of computer processing, and the method comprises the steps: S01, synchronously capturing electromagnetic signals of at least two working bands through a heterogeneous band receiving array synchronized by a hardware-level clock; s02, analyzing space parameters of a target signal and an interference source in the electromagnetic signal in real time; s03, constructing a layered adaptive codebook based on the interference dynamic parameters; s04, fusing the target signal parameter and the interference dynamic parameter by adopting a block matrix acceleration algorithm and an orthogonal decomposition technology, and calculating a beam forming weight; s05, executing operation through the multi-band coprocessor; and S06, establishing an interference source azimuth drift prediction model, and setting a trigger threshold as the coverage tolerance angle theta. According to the method, the anti-interference real-time performance is improved by utilizing layered adaptive wave beams, frequency band isolation, super-resolution multi-path identification and block decomposition.

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 an AI-based passive radar multi-source electromagnetic positioning matching method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a multi-source electromagnetic signal data set of a target region, the multi-source electromagnetic signal data set is electromagnetic radiation signals synchronously captured by a plurality of signal collection devices in a preset time window, and the electromagnetic radiation signals are acquired by a plurality of signal collection devices; secondly, performing feature extraction on the multi-source electromagnetic signal data set to obtain a signal feature set containing signal intensity distribution, frequency domain correlation and time-space correlation features, then fusing the signal feature set based on a dynamic weight distribution strategy to generate a multi-dimensional positioning feature set, and then calling an AI positioning matching model to analyze positioning parameters to obtain a multi-dimensional positioning feature set; and obtaining a spatial position and a signal propagation parameter of the target electromagnetic radiation source. And finally, a positioning optimization instruction is generated according to the parameters and is fed back to the signal acquisition device cluster for parameter calibration, so that the electromagnetic positioning precision and reliability can be improved.

The invention discloses a laser radar attitude measurement positioning method and device for an aviation electromagnetic detection suspension system, and belongs to the technical field of aviation electromagnetic detection, attitude measurement and coordinate positioning. According to the invention, real-time space positioning information and attitude information of the aviation soft suspension flight pod subsystem are accurately obtained by adopting a non-contact laser remote sensing measurement mode, and attitude and positioning devices do not need to be fixedly installed on the suspension flight pod subsystem. Electromagnetic interference or dynamic stability of the attitude and positioning device and related auxiliary electronic equipment on the soft suspension flight pod subsystem is avoided, the accuracy of electromagnetic signals and data reliability in the detection process are ensured, and therefore the precision of aviation electromagnetic detection is improved.

The invention relates to the technical field of state monitoring, in particular to a non-intrusive equipment operation state monitoring method and system based on an electromagnetic spectrum. The method comprises the following steps: dynamic multi-dimensional spectrum fingerprint extraction; generating a prototype according to the extracted multi-dimensional spectrum fingerprints; respectively performing intra-prototype contrast learning and inter-prototype contrast learning based on the generated prototypes; constructing a prototype contrast loss function based on contrast learning; image modeling is carried out according to the extracted multi-dimensional spectrum fingerprints, and an image comparison loss function is constructed based on image modeling; performing function mixing based on a loss function mixing strategy to obtain an overall loss function; performing equipment state judgment based on the overall loss function; and outputting a discrimination result. The problem of wide-frequency-band electromagnetic signal aliasing in a petroleum equipment dense scene is effectively solved.

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 belongs to the technical field of electric power and communication engineering, and particularly relates to an intelligent line patrol instrument based on multi-band electromagnetic induction and TDR fusion, and the intelligent line patrol instrument comprises a signal transmitting end which comprises a main control unit, a multi-band electromagnetic signal generator, a TDR pulse module and a self-adaptive coupling circuit. And the signal receiving end comprises a three-axis magnetic field sensor, an ADC module, an FPGA processing unit, a DSP processing unit and a touch display screen. The invention relates to an intelligent line patrol instrument combining multi-band electromagnetic induction and time domain reflection (TDR) technologies, which is used for path tracking, depth measurement and high-precision fault positioning of underground cables and optical cables, and is particularly suitable for underground pipeline maintenance and construction scenes under complex electromagnetic environments (such as transformer substations and dense pipeline areas).

The invention provides an electric wire and cable nondestructive on-line detection method which comprises the following steps: acquiring an electromagnetic signal, temperature distribution data, chemical component change data and mechanical vibration data of an electric wire and cable to be detected through a multi-mode sensor network; the electromagnetic signals, the temperature distribution data, the chemical component change data and the mechanical vibration data are subjected to data preprocessing, and a multi-dimensional data set is constructed; performing data fusion on the multi-dimensional data set through a multi-physics field coupling model to generate multi-dimensional feature data; performing defect identification based on the multi-dimensional feature data to generate a defect data set; and performing dynamic early warning according to the defect data set to generate an early warning data set. Electromagnetic, temperature, chemical and vibration data are comprehensively covered through the multi-mode sensing network, accurate recognition and early warning of wire and cable defects are achieved through preprocessing and multi-physics field coupling model fusion, the method has the advantages of being lossless online, comprehensive, intelligent and dynamic, and detection reliability and operation and maintenance efficiency are remarkably improved.