2259 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 provides an unmanned aerial vehicle countering plan generation method and system based on an artificial intelligence technology, and the method comprises the steps: analyzing the dynamic monitoring data flow of a target unmanned aerial vehicle, recognizing the flight path characteristics and electromagnetic signal frequency spectrum of the target unmanned aerial vehicle, and constructing a three-dimensional space threat situation model to evaluate the threat level. Decomposing behavior patterns and attack intention parameters of a target unmanned aerial vehicle based on threat levels, generating a dynamic decision factor set in combination with geo-fence constraint conditions and environmental interference factors, generating a plurality of candidate countering plans by matching countering means basic plans in a plan knowledge graph, and constructing a plan logic tree; a multi-dimensional evaluation parameter set is generated based on a three-dimensional space threat situation model, a dynamic decision factor set and a countering efficiency index, collaborative optimization processing is performed on a plan logic tree, an optimal countering plan is generated and converted into an executable instruction set, the executable instruction set is synchronized to a countering device for execution, and the real-time performance and scene adaptability of the unmanned aerial vehicle countering plan are improved.

The invention discloses a cargo transportation state monitoring system based on the Internet of Things, and relates to the technical field of logistics management. Electromagnetic signal intensity of each frequency band in a cargo box is recorded in real time through an electromagnetic interference monitoring module, and multi-point temperature data and temperature deviation information collected by a redundant sensor module are combined; the data processing and transmission module is used for preprocessing the signals and uploading the signals to the cloud platform, and the comprehensive analysis module extracts spectrum interference intensity abnormal features and temperature difference fluctuation features and accurately evaluates the influence of electromagnetic interference on the sensor data precision; then, the interference division module divides the interference event into instantaneous interference and continuous interference, the early warning processing module delays the instantaneous interference to trigger the temperature control equipment to adjust and send low-level early warning, and the continuous interference is deeply analyzed to generate a graded early warning signal and guide a coping strategy; and the data reliability, the equipment stability and the cargo protection capability of the cold chain transportation system in a complex electromagnetic environment are effectively improved.

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.

A method and system for protecting low-altitude airspace against unauthorized unmanned aerial vehicles by using a base station having communication and jamming functions. The present invention relates to the field of communications, unmanned aerial vehicles, and security supervision, and specifically to a method and system for managing unmanned aerial vehicles to protect low-altitude safety by using a wireless communication network base station to provide low-altitude wireless signal coverage comprising communication signals and jamming signals. "Low altitude, slow speed, and small size" unmanned aerial vehicles have become the main users of low-altitude airspace in China, but the safety supervision on the flight of unmanned aerial vehicles in low-altitude airspace is difficult to implement. By means of the present invention, the function of a jamming signal generator is integrated into a base station (base station having communication and jamming functions), and the effective coverage of communication / jamming signals in restricted areas can be realized in a cost-effective manner through wide deployment of base station network systems, imparting the restricted areas with a significant electromagnetic signal characteristic, thereby effectively preventing the intrusion of unauthorized unmanned aerial vehicles. In addition, the management and control of authorized networked unmanned aerial vehicles is implemented through networking and communication, providing technical means for the electronic management of unmanned aerial vehicles in low-altitude airspace.

The invention relates to the technical field of data management, in particular to an electrical equipment fault prediction system and method based on data fusion, and the system intelligently adjusts a data collection strategy by accurately capturing electromagnetic signal characteristic parameters of an electrical equipment operation environment and scientifically evaluating an electromagnetic interference index. The accuracy and efficiency of data acquisition are effectively improved, meanwhile, by monitoring the operation state parameters of the electrical equipment, the characteristics of the electrical equipment can be deeply extracted, the data acquisition compliance index is calculated, a powerful basis is provided for data preprocessing, finally, the multi-dimensional operation state parameters of the electrical equipment are highly fused, operation degradation factors can be accurately identified, and the accuracy and efficiency of data acquisition are improved. According to the invention, the method achieves the precise prediction of the fault level of the electrical equipment, gives out an early warning in time, remarkably improves the accuracy and timeliness of fault prediction, effectively reduces the operation and maintenance cost, enhances the stability and safety of a power system, and provides a powerful technical guarantee for the intelligent management and maintenance of the electrical equipment.

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 relates to the technical field of unmanned aerial vehicle application, and discloses a multi-mode electromagnetic field distribution surveying and mapping unmanned aerial vehicle system and an application method thereof. The system comprises a dynamic adjustment module, a multi-band acquisition module, a hybrid positioning module, a three-dimensional modeling module and a multi-modal fusion module. The dynamic adjusting module adjusts the distance between the electromagnetic sensor and the rotor wing according to the rotor wing electromagnetic interference suppression rule; the multi-band acquisition module acquires and processes electromagnetic signals; the hybrid positioning module integrates TDOA and AOA algorithms to position the radiation source; the three-dimensional modeling module generates a three-dimensional electromagnetic field intensity distribution diagram; the multi-modal fusion module fuses the visible light image and the electromagnetic data. The system can also evaluate the risk of the radiation source and plan a flight path. The application method comprises the steps of distance adjustment, data acquisition and processing, radiation source positioning, model construction and the like. According to the method, rotor interference is effectively suppressed, the positioning precision is improved, multi-modal data fusion is realized, and the method has a wide application prospect in the fields of electric power inspection, urban electromagnetic environment evaluation and the like.

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 embodiment of the utility model provides clothes processing equipment, which comprises a box body, a clothes processing drum and a wireless energy supply assembly, the box body comprises a rear plate, the clothes processing drum is rotatably arranged in the box body, the clothes processing drum comprises a drum body and a rear cover, and the rear cover is arranged at the rear end of the drum body; the wireless energy supply assembly comprises a transmitting module and a receiving module, and the receiving module is used for receiving an electromagnetic signal transmitted by the transmitting module and generating an induced current; the transmitting module is arranged on the rear plate, and at least one part of the receiving module is arranged on the rear cover. According to the clothes processing equipment provided by the embodiment of the invention, the transmitting module and the receiving module can transmit electric energy without contact, and electric energy transmission between the relatively rotating clothes processing drum and the relatively static rear plate is realized. The receiving module supplies power to the electrical elements, so that the electricity demand of the electrical elements is met, and the clothes state can be sensed in a close range.

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.

The embodiment of the utility model provides clothes processing equipment which comprises a clothes processing drum and a wireless energy supply assembly, the wireless energy supply assembly comprises a transmitting module, a receiving module and at least one support, the transmitting module is arranged on a part outside the clothes processing drum, and the receiving module is arranged on the clothes processing drum; the transmitting module comprises a transmitting coil, the receiving module comprises a receiving coil, and the receiving coil is used for receiving electromagnetic signals transmitted by the transmitting coil and generating induction current; the at least one support is used for supporting the transmitting coil and / or the receiving coil. According to the clothes processing equipment provided by the embodiment of the invention, the transmitting module and the receiving module can transmit electric energy without contact, and electric energy transmission between the relatively rotating clothes processing drum and the relatively static clothes processing drum is realized. The electrical apparatus element of the clothes processing equipment can be arranged in the clothes processing drum, and the receiving module supplies power to the electrical apparatus element, so that the electricity demand of the electrical apparatus element is met, and the close-range sensing of the clothes state is facilitated.

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 discloses an asphalt pavement internal disease detection method, which comprises the following steps: acquiring antenna attitude data, and correcting the antenna attitude data; acquiring acquisition parameters of the ground penetrating radar according to the corrected antenna attitude data; acquiring an original electromagnetic signal through the ground penetrating radar according to the acquisition parameters; performing feature extraction on the original electromagnetic signal to obtain a reflection feature; the reflection features are judged, abnormal reflection points are obtained, and the position and intensity data of the abnormal reflection points are extracted; obtaining an abnormal reflection point distribution diagram according to the positions and the intensities of the abnormal reflection points, and obtaining disease types of the abnormal reflection points according to the abnormal reflection point distribution diagram; generating a disease distribution diagram according to the disease type and distribution of the abnormal reflection points, analyzing the severity and range of a disease range according to the disease distribution diagram, and repairing the internal diseases of the pavement according to the severity and range.

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 present invention discloses a real-time electromagnetic environment monitoring system and method, which relates to the technical field of electromagnetic environment monitoring, including using sensors to obtain real-time electromagnetic signal data and historical electromagnetic environment data and preprocessing; constructing a trend prediction model based on the electromagnetic signal data and historical electromagnetic environment data and predicting changes in the electromagnetic environment; automatically adjusting the sampling frequency according to the electromagnetic environment change trend prediction results and real-time electromagnetic signal data; and continuously optimizing the parameters in the electromagnetic environment change trend prediction model according to monitoring data and feedback. The present invention constructs a trend prediction model to analyze the electromagnetic signal data and historical environment data, which can improve the accuracy of the prediction of the electromagnetic environment change trend, and automatically adjusts the sampling frequency so that the system can adjust the frequency of data collection according to the dynamic changes of the electromagnetic environment, ensuring that the collected data can reflect the key changes in the electromagnetic environment without wasting resources, which reflects a high degree of flexibility.

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.