4043results about "Electromagentic field characteristics" patented technology

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention relates to the technical field of electromagnetic environment detection, and discloses a multi-mode electromagnetic environment detection system and method. The system acquires multi-modal data such as electromagnetic field intensity and spectrum distribution through a multi-modal sensor array to generate a data set, and realizes interference source identification and dynamic adjustment of detection parameters through nonlinear signal processing, depth feature extraction based on a convolutional neural network, topology analysis of a graph neural network and adaptive optimization of reinforcement learning. And an electromagnetic field simulation module is also arranged to simulate electromagnetic wave propagation and early warn abnormal radiation, and an abnormal mode library is constructed to identify potential interference types. The system can comprehensively and accurately detect an electromagnetic environment, efficiently position an interference source, adaptively adjust detection parameters, improve detection accuracy, real-time performance and system stability, and effectively meet complex electromagnetic environment monitoring requirements.

The invention discloses a booster station monitoring operation and maintenance system, and relates to the technical field of data management. A plurality of heterogeneous intelligent sensors are deployed in a booster station to monitor the operation state of equipment in real time, an electromagnetic field sensor is utilized to sense the electromagnetic interference intensity, and when abnormal high-frequency pulses are detected, a data anomaly detection mode is triggered to detect the abnormal high-frequency pulses; calculating an error compensation value according to the abnormal degree, correcting abnormal data, ensuring the accuracy of the data, performing equipment state evaluation according to the recovered data, and if potential safety hazards are found, sending intelligent early warning to operation and maintenance personnel and providing suggestions for adjusting monitoring strategies or optimizing shielding measures; according to the invention, the anti-interference capability and accuracy of booster station monitoring data are effectively improved, the risk of operation and maintenance misjudgment caused by electromagnetic interference is reduced, the reliability of intelligent monitoring in an ultrahigh-voltage power transmission scene is enhanced, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

The invention provides a transformer substation monitoring method and system based on the Internet of Things, and the method comprises the steps: obtaining an original electromagnetic disturbance signal of a transformer substation, and constructing an electromagnetic disturbance feature matrix; carrying out the anomaly detection of the electromagnetic disturbance signal through employing a self-adaptive attention mechanism in combination with asymmetric anomaly detection loss; inputting the time sequence input vector into a fault classification model to perform fault type identification, outputting a fault type label and severity, and performing fault propagation path calculation on the fault type label and severity to obtain a fault propagation path; and simulating fault evolution through a digital twin model, predicting a fault development trend, a secondary fault influence range and a future state vector, and finally generating a maintenance priority, a time window and a resource scheduling scheme. The system breaks through the limitation of a traditional monitoring technology, improves the intelligent level of a transformer substation, reduces the maintenance cost, and improves the operation safety and reliability of a power grid.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

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 switch cabinet quality comprehensive evaluation system and method based on the Internet of Things technology, and particularly relates to the technical field of switch cabinets. Electromagnetic interference detection equipment is installed, the electromagnetic field intensity around a switch cabinet and the operation state of the equipment are monitored in real time, and electromagnetic field data and state data in different time periods are obtained; carrying out weighted calculation on the electromagnetic interference index to generate an overall electromagnetic interference index; the fuzzy logic is utilized to comprehensively analyze the electromagnetic interference index and the real-time state data of the switch cabinet, a health assessment report is generated, the health condition of the equipment is accurately displayed, the operation monitoring and maintenance optimization capability of the switch cabinet is effectively improved, potential faults can be predicted, and misjudgment can be reduced.

The invention discloses a method and a device for calculating an electromagnetic-heat flow field under a turn-to-turn short circuit fault of a converter transformer. The method comprises the following steps: establishing a converter transformer multi-physical field numerical calculation theoretical model based on circuit-electromagnetic-heat flow coupling; based on the multi-physical field numerical calculation theoretical model, establishing a converter transformer electromagnetic-heat flow field model under the turn-to-turn short circuit fault; setting turn-to-turn short circuit faults at different positions in the electromagnetic-heat flow field model of the converter transformer; and performing winding current waveform, voltage waveform and electromagnetic field analysis and heat flow field analysis of the converter transformer under the set turn-to-turn short circuit fault.

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.

An apparatus (100) for detecting an Electro-Magnetic, EM, field, said apparatus comprising a: Rydberg-Atom EM Field Detector (107), RAEM-FD, configured to detect an EM field (170) of a given frequency; and Multi-Plate WaveGuide (105), MPWG, comprising: at least three overlapping and non-intersecting plates (180); and at least two waveguides (190), each formed by an adjacent pair of the at least three plates, and wherein each waveguide has a separation distance suitable (h) for supporting propagation of the EM field; and wherein the MPWG is arranged so as to direct the EM field towards the RAEM-FD.

The invention discloses an electromagnetic shielding effectiveness evaluation system for an environmental laboratory, which relates to the technical field of electromagnetic shielding effectiveness evaluation and comprises a signal source module, a shielding body test module, an electromagnetic field detection module, a data acquisition and processing module and a verification and calibration module. Wherein the signal source module and the shielding body test module are used as hardware parts and are necessary parts in the evaluation system, and the electromagnetic field detection module, the data acquisition and processing module and the verification and calibration module are used as software parts, so that high-efficiency evaluation processing on a product is also realized; the technical key points are as follows: the scheme not only ensures the accuracy and reliability of an evaluation result, but also improves the consistency and repeatability of evaluation, and a scientific and effective method is provided for the evaluation of the shielding effectiveness of the wire sleeve; meanwhile, the scheme is also helpful to timely discover and correct problems in evaluation, and ensures that the product can meet specific requirements in actual use.

The invention discloses a thunder and lightning early warning method based on atmospheric electric field networking monitoring. The method comprises the following steps: collecting multi-point electric field intensity data in a region in real time through a high-density electric field sensor network, and constructing an electric field intensity distribution matrix; for the constructed electric field intensity distribution matrix, space-time evolution characteristics of lightning activities are extracted, and lightning occurrence probability distribution is generated; acquiring temperature, humidity and air pressure parameters from multi-source meteorological data, performing data fusion with the generated thunder and lightning occurrence probability distribution, and constructing a comprehensive environment characteristic data set; and for the constructed comprehensive environment characteristic data set, calculating a comprehensive risk index, and if the index exceeds a preset thunder and lightning triggering threshold, etc. According to the lightning early warning method, the distribution matrix is constructed through the high-density electric field network, the spatial-temporal characteristics are extracted to generate the initial probability, and the physical rationality and the environment false alarm resistance of early risk identification are remarkably improved through fusion with the multi-source meteorological data.

The invention provides an unmanned aerial vehicle terrain acquisition method, system and device and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: determining the number of power supply nodes needed by a target region and the position of each power supply node according to the terrain features of the target region; according to the position of each power supply node, the target area is divided into a plurality of sub-areas, the number of unmanned aerial vehicles required by each sub-area is determined according to the area of each sub-area, and the unmanned aerial vehicles of the corresponding number are distributed to each sub-area; determining an initial flight plan of each unmanned aerial vehicle by combining the residual electric quantity of each unmanned aerial vehicle in each sub-region and the position of a power supply node in each sub-region; and determining electromagnetic interference intensities of different positions in each sub-region according to the region characteristics of each sub-region, adjusting the initial flight plan according to the electromagnetic interference intensities of different positions in each sub-region, and determining a target flight plan of each unmanned aerial vehicle. The technical effect of the invention is that the terrain acquisition work can be reliably completed.

The invention discloses an efficient information extraction method based on Rydberg atom low-frequency detection and a measurement system thereof, and the extraction method is based on the avoidance of background noise by a coherent detection technology, converts the modulation of a low-frequency electromagnetic field into quantum coherent phase change by using Rydberg atoms, and adopts the means of homodyne detection and heterodyne detection. The amplitude information and the phase information of the low-frequency electromagnetic field are accurately extracted, and the measurement sensitivity of the weak low-frequency electromagnetic field is remarkably improved.

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 belongs to the technical field of geophysical monitoring, and relates to an electromagnetic interference detection method for earthquake monitoring, which comprises the following steps: deploying a multi-dimensional electromagnetic field sensing unit and an earthquake signal acquisition unit and realizing high-precision synchronization; synchronously acquiring multi-dimensional electromagnetic field and seismic physical signal data; performing multi-feature and noise feature extraction on the two; inputting the features into a pre-trained machine learning / deep learning model for identification, and outputting information such as interference type, intensity and azimuth angle; and carrying out influence evaluation and quality marking on the seismic data according to the output, and selectively inhibiting or compensating. Through the above scheme, multi-dimensional perception and characterization, high-precision synchronization and correlation analysis, intelligent identification and spatial positioning are realized, and the reliability and accuracy of earthquake monitoring data are significantly improved.

This invention provides an electromagnetic field detector, a system comprising the electromagnetic field detector, and a method of operating an electromagnetic field detector, the electromagnetic field detector comprising a first optical signal interface configured to receive, from one or more remote optical sources, a first optical signal; an electromagnetic field transmitter configured to transmit a local oscillator electromagnetic field generated from the first optical signal, the electromagnetic field transmitter comprising: a photocurrent generator configured to generate a photocurrent from the first optical signal; and an antenna interface configured to supply the photocurrent to an antenna to transmit the local oscillator electromagnetic field; a second optical signal interface configured to receive, from the one or more remote optical sources, a probe optical signal and a coupling optical signal; and a transmission medium configured to be excited by the probe optical signal and further excited by the coupling optical signal, wherein the probe optical

The invention relates to the technical field of meteorological monitoring, in particular to a ring main unit lightning micrometeorological online monitoring system and method, and the system comprises a feature extraction module, a frequency spectrum detection module, a frequency band pool screening module, a path adjustment module and an interference avoidance module. The method comprises the following steps: collecting an electric field intensity extreme value sequence, calculating a change rate difference ratio, identifying thunder and lightning precursor characteristics, combining a frequency spectrum energy density map, analyzing an energy peak frequency band and a drop point of a frequency hopping signal, judging a thunder and lightning frequency signal concentration area, performing frequency spectrum interference filtering, and combining a dynamic time warping algorithm. The time deviation between a lightning stroke time point and frequency hopping scheduling is analyzed; a frequency hopping path is adjusted in real time; the response timeliness is improved; the difference value before and after electric field disturbance is monitored; and support is provided for monitoring stability and response adaptability of the thunder and lightning risk environment.

The invention discloses a Rydberg atom microwave measurement sensitivity improving method based on atom velocity selection, and relates to the field of Rydberg atom microwave measurement, and the method comprises the steps: moving the frequency of probe light to an energy level resonance transition position of cesium atoms from a ground state to an excited state, scanning the frequency of the coupled light within a Rydberg state energy level preset range, and observing an EIT signal; the frequency of the detection light is changed, the detuning amount of the detection light is selected to enable the EIT signal to reach the maximum, and the frequency of the detection light is locked to the frequency corresponding to the maximum position of the EIT signal; meanwhile, a local oscillation field signal generator in the Rydberg atomic microwave sensitivity measurement device is turned on, and corresponding EIT-AT splitting is generated; the frequency of the coupling light is locked to the coupling light laser frequency corresponding to the lowest point of EIT-AT splitting, and the Rydberg atom microwave measurement sensitivity reaches the maximum. The cesium atom speed is screened and controlled, and the signal-to-noise ratio of the measurement signal is improved, so that the measurement sensitivity is improved.

The invention discloses a method, a system and a detection device for detecting a partial discharge signal by Rydberg atoms, and the method comprises the steps: receiving the Rydberg atoms and optical signals prepared in a cesium atom steam chamber by a photoelectric detector, obtaining an electromagnetic induction transparent spectrum, and taking the electromagnetic induction transparent spectrum as an electromagnetic induction transparent background spectrum; the propagation direction of the signal to be measured is perpendicular to the propagation direction of the laser in the cesium atom steam chamber; preparing Rydberg atoms in a cesium atom steam chamber, receiving the electromagnetically-induced transparent spectrum under the influence of the to-be-detected signal by a photoelectric detector, and taking the electromagnetically-induced transparent spectrum as a to-be-detected electromagnetically-induced transparent spectrum; and comparing the to-be-detected electromagnetic induction transparent spectrum with the electromagnetic induction transparent background spectrum, if the spectrum has a signal peak, indicating that the to-be-detected signal has partial discharge, and if the spectrum does not have the signal peak, indicating that the to-be-detected signal does not have partial discharge. And arc signal frequency spectrum measurement without a local oscillation field is realized.

The invention discloses a method, system and device for monitoring the quality of an operating lightning arrester, and the method comprises the steps: collecting a lightning current, a leakage current, the temperature of the lightning arrester and the environment temperature during lightning stroke, evaluating the residual life of the lightning arrester through the lightning current, and achieving the quality monitoring of the operating lightning arrester based on the residual life, the leakage current, the temperature of the lightning arrester and the environment temperature. Healthy operation of the lightning arrester can be ensured, safe and reliable operation of a distribution line can be ensured, and great economic value and social benefit are achieved.

The invention provides a lightning proximity prediction method based on a space-time separation convolutional neural network. The method comprises the following steps: S1, acquiring satellite infrared brightness temperature observation data and lightning positioning data; s2, performing spatial resolution adjustment and standardization processing on the satellite infrared brightness temperature observation data; gridding and de-noising processing is carried out on the lightning positioning data; s3, defining a prediction task; s4, establishing a space-time sequence prediction model; based on the processed data, storing the processed data in a numpy file format according to a time point sequence, dividing the processed data into a training set, a verification set and a test set, and loading the training set, the verification set and the test set to a space-time sequence prediction model for model training to obtain an optimization model; and based on the prediction task, using the optimization model to carry out lightning approaching prediction to obtain lightning prediction data. According to the method, the development of cumulus cloud is captured by using satellite infrared brightness temperature observation data and brightness temperature difference data, so that the problem that thunder and lightning inception cannot be effectively predicted is solved.

The invention discloses a response time conduction type testing device and method for a radio frequency channel protection device. The response time conduction type testing device comprises a transient field intensity generating device, an isolator, a directional coupler, a first attenuator, a second attenuator, a third attenuator, a first power meter, a second power meter, a high-speed oscilloscope, a vector network analyzer and a radio frequency channel protection device to be tested. According to the method, the response performance of the protection device under the influence of the transient strong field can be comprehensively, safely and accurately evaluated, the amplitude limiting function of the protection device can still be effectively exerted under the action of the transient strong field with an extremely narrow rising edge, and then subsequent devices, circuits and equipment are prevented from being damaged.

The invention relates to the technical field of semiconductor equipment, in particular to an intelligent electromagnetic interference suppression system for semiconductor equipment. The system comprises a sensor network which is installed at an electromagnetic interference key position of semiconductor equipment, monitors and collects electromagnetic data of the semiconductor equipment in real time, and sends the electromagnetic data to a central data processing module; the central data processing module is used for analyzing the received electromagnetic data, identifying and positioning an interference source based on an interference source positioning model of a machine learning algorithm, performing behavior prediction analysis on the interference source, generating a self-adaptive interference suppression strategy and sending an interference suppression instruction; and the feedback control module is used for executing a feedback control action based on the interference suppression instruction, realizing real-time suppression of multi-level electromagnetic interference of the semiconductor equipment and feeding back a suppression effect. According to the invention, real-time effective suppression of electromagnetic interference is realized, and the process stability and the control precision of semiconductor equipment are remarkably improved.

The invention relates to a lightning nowcasting method based on multi-source satellite data, and belongs to the technical field of lightning nowcasting. The method comprises the steps of obtaining and preprocessing satellite data of a multi-source channel and corresponding lightning data, constructing a U-Net lightning prediction model, collecting a historical multi-source satellite image sequence, constructing a PredRNN satellite image prediction model and predicting the lightning occurrence probability. According to the method, the PredRNN and the U-Net model are combined, the lightning distribution can be predicted at the future moment only by using the satellite image, the dependence on historical lightning data is broken through, and the method has a wide application prospect in the field of lightning nowcasting.

The invention relates to the technical field of intelligent inspection, and discloses an outdoor substation unmanned aerial vehicle safety inspection distance calculation method, which comprises the following steps: scanning an outdoor substation by adopting three-dimensional laser scanning equipment, and constructing a three-dimensional model of the outdoor substation after processing; analyzing an electromagnetic field generated by the operation of the high-voltage live equipment in the outdoor substation, obtaining the distribution condition of the electromagnetic field of the outdoor substation, and determining the safety limit values of the electric field and the magnetic field; calculating an alarm distance of the unmanned aerial vehicle based on the safety limit values of the electric field and the magnetic field and the comprehensive influence factors; and based on the positioning precision of the unmanned aerial vehicle and the influence of environmental factors, calculating the safety inspection early warning distance of the unmanned aerial vehicle. According to the invention, the safety of unmanned aerial vehicle inspection can be improved, and a scientific basis is provided for the large-scale application of the unmanned aerial vehicle in outdoor transformer substation inspection.