545 results about "Time signal" patented technology

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

The invention provides a signal shielding method based on deep reinforcement learning, and belongs to the technical field of artificial intelligence and communication crossing. The method comprises the steps that S1, a large number of historical wireless communication signals are acquired to construct a data set to train a signal interference strategy generation model; s2, deploying the signal interference strategy generation model to a signal shielding device; s3, verifying and analyzing the signal shielding instruction to obtain a shielding parameter, collecting a real-time wireless communication signal, and inputting the real-time wireless communication signal and the shielding parameter into a deployed signal interference strategy generation model to obtain a real-time signal interference strategy; and S4, the signal shielding device modulates the real-time interference signal based on the real-time signal interference strategy, and the power amplifier performs power amplification on the real-time interference signal and then transmits the real-time interference signal to the outside through the radio frequency antenna so as to perform signal shielding. The method has the advantages that the accuracy, the real-time performance and the expansibility of signal shielding are greatly improved, and the power consumption of signal shielding is reduced.

The invention discloses a multi-mode fusion time service method and system based on dynamic calibration of DCP equipment. The method comprises the following steps: completing initial time reference data acquisition based on a multi-mode satellite signal; meanwhile, time data of a ground time service network are collected, and motion parameters of DCP equipment are monitored in real time through an inertial navigation unit; establishing a correlation model of the motion parameter and the time service error; quantitative analysis of error sources is completed based on the correlation model; fusing the multi-source time data; dynamically adjusting the fusion weight of each data source according to the real-time motion state of the DCP equipment; redundant time slots are automatically distributed to high-error equipment; outputting the calibrated time signal to DCP equipment through an interface to complete clock synchronization; and continuously monitoring the time deviation of the DCP equipment, and dynamically updating the calibration parameters to form closed-loop optimization. And the environmental adaptability and the precision stability of the timing system are improved.

The invention provides an intelligent shelter equipment operation state monitoring method and system based on machine learning, and the method comprises the steps: firstly obtaining a synchronous operation real-time signal set which comprises the real-time parameters of all operation parts, the interaction between equipment and the comprehensive influence of a shelter environment, and carrying out the mutual feedback correlation processing of the synchronous operation real-time signal set to extract a mutual feedback correlation feature set; inputting the set into an operation state deduction model to generate a short-term operation state evolution sequence, identifying a potential abnormal evolution trend and determining an abnormal association influence range based on the short-term operation state evolution sequence, and generating collaborative regulation and control demand information; then, inputting the collaborative regulation demand information and the mutual feedback correlation feature set into a collaborative regulation model to generate a multi-device collaborative operation regulation instruction, after the multi-device collaborative operation regulation instruction is sent, collecting an execution feedback signal, updating a feature extraction rule, and adjusting model parameters; the comprehensive, dynamic and accurate monitoring and regulation of the operation state of the square cabin equipment are realized, and the stability and the reliability of the equipment operation are improved.

The invention relates to a detection-interference integrated unmanned aerial vehicle electromagnetic interference device, which belongs to the technical field of electronic countermeasure, and comprises an integrated design of an antenna module, a radio frequency front end module, a digital signal processing module and a main control module, the radio frequency front-end module comprises a detection receiving channel and an interference transmitting channel to process signal conversion, the digital signal processing module executes signal detection and analysis, intelligent interference decision and anti-self-interference signal processing based on a self-adaptive filtering algorithm, and the main control module is responsible for overall control and power supply. According to the invention, the technical problems of large response delay, complex system cooperation and incapability of continuous reconnaissance in the interference process of traditional anti-unmanned aerial vehicle equipment are solved. Real-time signal cancellation is achieved through the normalized least mean square algorithm, the reconnaissance capability is kept while interference is applied, the method has the advantages of being rapid in response, accurate in interference, low in power consumption and the like, and the countering efficiency is remarkably improved.

The invention discloses a power distribution network fault early warning method and system based on online monitoring, and relates to the field of fault early warning, and the method comprises the steps: continuously collecting real-time electrical signals; signal waveform prediction of the real-time electrical signal is completed according to the real-time signal prediction result, and whether an early fault signal exists in the target power distribution network is judged; if the target power distribution network has the early fault signal, obtaining power distribution network information, environment prediction data and geographic position data; constructing a topological structure of the power distribution network, and extracting early fault information of the target power distribution network; constructing an icing evolution model of the target power distribution network; completing fault risk coupling between the icing evolution model and the early fault information; and carrying out risk accumulation analysis on the topological structure of the power distribution network, and outputting fault early warning information of the target power distribution network according to a risk accumulation analysis result. According to the invention, the power distribution network fault early warning precision and efficiency can be effectively improved.

The invention discloses a dual-mode TDC implementation method and system based on an FPGA, and relates to the technical field of electronic circuits, and the technical key points are as follows: after a dual-mode signal generation circuit receives each STOP pulse, a logic sequence opposite to the last time is immediately output as an effective delay signal, the reset of the last time signal in a time delay chain (TDL) is not required, and the time delay time is shortened; when the logic signal propagated last time does not completely fade, the new reverse logic signal can be independently propagated in the TDL, and the new reverse logic signal and the new reverse logic signal do not interfere with each other; meanwhile, the rising edge of the system clock only updates the state of the main trigger to serve as the logic reference of the next trigger, and an additional reset process is not needed, so that the dead zone time of single measurement is strictly controlled to be a single period corresponding to the system clock, and compared with a double-period dead zone in an existing scheme, the dead zone time is remarkably shortened, and the measurement rate is synchronously doubled; in a high-frequency pulse measurement scene, pulse loss caused by an overlong dead zone can be effectively avoided, and the measurement efficiency is remarkably improved.

The invention discloses a low-altitude economic flight data management method based on a block chain technology, and relates to the technical field of low-altitude traffic management, and the method comprises the steps: verifying an encrypted positioning proof, generating a verification state set, decrypting flight coordinates according to the verification state set, and generating a credible coordinate data stream; performing sensitive airspace intrusion detection according to the credible coordinate data stream and the high-security protection node, performing collision risk analysis by the traffic management node to obtain an encrypted aggregated threat vector, and identifying a threat level according to the encrypted aggregated threat vector; and dynamically adjusting the working parameters of the airborne equipment according to the threat level, generating an airborne equipment regulation and control instruction set, scanning the signal-to-noise ratio and the threat intensity in real time, dynamically allocating a communication frequency band, and generating a new positioning strategy parameter. According to the method, the real-time signal-to-noise ratio and the threat intensity scanning result are combined, communication frequency band dynamic allocation and encryption strategy dynamic optimization are driven, the airspace situation awareness capacity is formed, the resource utilization rate is increased, and the anti-interference capacity is enhanced.

The invention discloses a signal reconstruction and noise suppression method and system for a distributed Raman temperature measurement sensing system, and the method comprises the steps: obtaining original anti-Stokes light and Stokes light time domain signals of the distributed Raman temperature measurement sensing system, and constructing a space-time signal matrix; performing preprocessing and data enhancement on the space-time signal matrix; constructing a dual-path feature fusion network for Raman signal reconstruction and noise suppression, and initializing the network; defining a loss function fusing temperature physical constraints, and training the dual-path feature fusion network; and inputting an original Raman signal to be processed into the trained network, outputting the reconstructed anti-Stokes and Stokes signals with the high signal-to-noise ratio, and carrying out temperature demodulation according to the anti-Stokes and Stokes signals with the high signal-to-noise ratio. While the spatial resolution is maintained or improved, the noise of the Raman temperature measurement system is effectively suppressed, especially the real signal of a temperature abrupt change point can be recovered, the temperature demodulation precision, stability and reliability are improved, and reliable data are provided for infrastructure safety monitoring.

The invention discloses a GIL equipment fault detection method, system and equipment based on a voiceprint dynamic coupling model and a storage medium, and relates to the technical field of power system equipment monitoring and operation and maintaining.The method comprises the steps that sound signals in GIL equipment are collected in real time, feature parameters are extracted, and feature vectors are obtained; establishing a Gaussian mixture model by using the normal working condition data, and training the Gaussian mixture model based on the feature vector to obtain an anomaly detection model; carrying out feature parameter extraction on a real-time signal to be detected, and calculating an estimated value and a residual error of an extracted feature on the anomaly detection model; and based on the calculated estimated value and the residual error, calculating an abnormal score and outputting corresponding early warning information and a fault positioning result. According to the method, the whole process from data acquisition to fault diagnosis is optimized, the accuracy, timeliness and intelligent level of GIL equipment voiceprint monitoring are effectively improved, and a powerful guarantee is provided for safe and stable operation of equipment.

The invention provides a fan fault diagnosis method for recognizing a vibration map based on a convolutional neural network, and relates to the technical field of neural networks, and the method comprises the steps: obtaining a multi-dimensional vibration signal in the operation process of a fan, and generating a two-dimensional vibration map through time-frequency transformation; and a convolutional neural network is utilized to automatically extract multi-layer time-frequency features and realize fault category discrimination. In the training stage, parameter optimization is carried out based on known fault samples, in the reasoning stage, real-time signals are input into a trained model to obtain fault type probability distribution, the fault type is determined according to the maximum probability, a fault evolution result is generated in combination with the historical operation trend, and therefore automatic, intelligent and rapid diagnosis of fan faults is achieved.

The invention discloses a neural patient rehabilitation training method based on task training and brain-computer interaction, and belongs to the technical field of rehabilitation training. The method comprises the following steps: acquiring data signals of a patient in current rehabilitation training in real time, performing segmentation processing to obtain a plurality of independent data blocks, performing frequency domain conversion on the independent data blocks, extracting time-frequency features, and calculating KL divergence between adjacent data blocks; if the KL divergence of any adjacent independent data block is larger than or equal to a preset threshold value, historical data signals are obtained, the motion decoding model is corrected, motion decoding is carried out on the patient according to the corrected motion decoding model, rehabilitation training is carried out on the patient according to the motion decoding model, and rehabilitation training is carried out on the patient. According to the method, the corresponding model is selected according to the current rehabilitation training task, dynamic adjustment is performed in combination with real-time signal features, high matching of the decoding result and the training target is ensured, and the rehabilitation training effect of the patient is improved.

A method and system for cloud-based V2X for providing real-time broadcast of signal phase and timing (SPaT) data. An example method includes receiving SPaT data from one or more traffic signal controllers, processing the SPaT data by converting the SPaT data from an original format into one or more different formats, where the processing includes distributing a processing load over a plurality of docker containers using a grouping or clustering algorithm that takes into account a raw data arrival sequence from the traffic signal controllers, determining one or more nearest intersections based on geolocation of a client device, and transmitting processed SPaT data related to at least one of the one or more nearest intersections to the client device.

The invention discloses an electrocardiosignal feature extraction and classification method and a sensor system, and particularly relates to the technical field of electrocardiosignal processing and mode recognition. The core of the method is that an original electrocardiosignal is obtained through a sensor, after preprocessing, a self-adaptive strategy based on real-time signal quality evaluation is adopted to dynamically select and fuse time domain, frequency domain and nonlinear features, and then a machine learning model integrated with an incremental learning function is utilized to classify the features; the corresponding system comprises a sensor module, a preprocessing module, a feature extraction module, a classification module and a system control module which are connected with one another. According to the core structure, the dynamic optimization of the feature extraction strategy along with the signal quality is realized, and the classification model can be continuously self-updated according to new data, so that the adaptability of electrocardio analysis and the accuracy of long-term monitoring are improved.

The invention discloses a soft soil foundation structure visual monitoring network and navigation system synchronous settlement early warning method, and relates to the technical field of geological monitoring and investigation. The soft soil foundation structure visual monitoring network and navigation system synchronous settlement early warning method comprises the steps that S1, image data, time signal data and environment data in the soft soil foundation monitoring process are collected and preprocessed, and a standardized soft soil foundation state data set is constructed; s2, evaluating the spatial offset characteristic of the reference point, and dynamically adjusting the stability identifier and the subsequent replacement trigger condition of the reference point; s3, analyzing the image semantic consistency of the candidate reference points, and screening stable replacement points; s4, the coordinate consistency of the replacement reference points is evaluated, and the spatial positions of the reference points are dynamically corrected; and S5, performing difference comparison on the multi-base-station monitoring data after rollback correction, and repairing curve abrupt change. The problems that the visual reference is prone to drifting and an automatic repairing mechanism is lacked under the condition that the reference point in the soft soil foundation is unstable are solved.

The invention discloses an industrial equipment data acquisition and abnormal behavior monitoring system, which belongs to the field of data acquisition and processing, and comprises a working condition interval division and modeling module used for dividing an equipment operation state into discrete steady-state working condition intervals according to acquired equipment data, independently constructing a static statistical reference model for each working condition interval; the nonlinear abrupt change signal acquisition module is used for deploying a high dynamic response sensor and anti-electromagnetic interference hardware aiming at nonlinear abrupt change signals, and synchronously adopting a real-time signal processing technology to eliminate environmental noise; the data synchronization fusion module is used for establishing a unified time scale and synchronously aligning multiple physical quantity data through a hardware clock; the method has the beneficial effects that different working condition intervals are divided, and the static statistical reference model and the dynamic health model are independently constructed, so that the response problem of monitoring signals under different working conditions can be effectively identified, and misjudgment of abnormal behaviors of equipment is avoided.

The invention relates to an active sonar echo signal phase jump compensation method and device based on a synchronous serial number, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: adding a synchronous serial number into a wet end signal to form an expansion matrix; transmitting the extended signal to a signal processor and windowing the extended signal; traversing a synchronous serial number in the windowing signal, and detecting the continuity of the synchronous serial number to judge whether packet loss exists or not; if packet loss exists, positioning a packet loss position and performing zero padding operation; and finally, carrying out fast Fourier transform on the zero-filled signal to obtain a spectral analysis result. According to the method, accurate identification and phase compensation of the packet loss position are achieved through the synchronous serial number, the method is suitable for underwater acoustic single-frequency signals, and when the packet loss rate does not exceed 2.5%, spectrum sidelobes can be effectively restrained, and the peak value of the spectrum sidelobes is reduced to be within 10% of the packet loss state. The method has the advantages of being clear in principle, easy and convenient to operate and high in calculation efficiency, is suitable for real-time signal processing, and remarkably improves the reliability and efficiency of target detection.

The invention provides a partial discharge monitoring and early warning method for a switch cabinet. The method comprises the following steps: acquiring ultrasonic sensing data and transient grounding voltage data through configuration acquisition software, and preprocessing the ultrasonic sensing data and the transient grounding voltage data; inputting the real-time signal peak value into a pre-constructed CNN and LSTM-based partial discharge prediction model, and outputting a combined sequence containing original data and a predicted value; and performing M-K detection on the joint sequence for trend analysis, and performing early warning analysis in combination with the predicted value and an alarm threshold. According to the technical scheme, the CNN-LSTM is utilized to predict the partial discharge data sequence, and then trend analysis is carried out through the M-K inspection method to comprehensively consider two indexes of the amplitude of the partial discharge sequence and the trend parameter so as to realize early warning of the partial discharge of the cable joint. The CNN-LSTM combined method is adopted to carry out partial discharge data sequence prediction, the feature information extraction capability of the CNN and the sensitivity of the LSTM to the time sequence can be effectively integrated, and the trend data prediction effect is improved.

The invention relates to the technical field of digital twinning, in particular to a steel processing equipment self-adaptive control method based on digital twinning, which comprises the following steps: acquiring real-time signals such as displacement, load, rotating speed and inter-unit tension of an actuating mechanism, inputting the real-time signals into a digital twinning model, and constructing an instantaneous dynamic geometric model to calculate an instantaneous feedback value of a controlled object; the influence coefficient between the first control loop and the second control loop is analyzed through virtual perturbation, and a partial derivative sensitivity matrix is generated; and then, inputting the instantaneous feedback value into a self-adaptive controller to obtain an original control increment, calculating a compensation control increment of a second execution mechanism in combination with an actually measured morphology deviation signal and an influence coefficient, and finally driving a controlled object to complete action response. A cyclic storage buffer area is established, and asynchronous timestamp alignment based on spatial displacement is executed. Through multi-physics field coupling and asynchronous correction, self-adaptive control over the steel machining equipment is achieved.

The invention discloses a distribution network line distributed signal processing method, system and device and a medium, and belongs to the technical field of distribution network lines, and the method comprises the steps: collecting node data of a power distribution network, carrying out the time alignment processing, generating a monitoring data set, and determining nodes with abnormal data in a power grid; decomposing and extracting characteristic data representing change characteristics; updating the feature data of each abnormal node based on distributed feature interaction; and generating a fault identifier according to the updated feature data, and determining a fault area. The power grid area is dynamically divided according to the monitoring range of the protection device, the protection area matrix is formed, dynamic adjustment of the protection area is achieved, the impedance and current data of all lines in the protection area are monitored in real time, abnormal lines are rapidly recognized, the fault detection precision and sensitivity are improved, and the fault detection efficiency is improved. A real-time signal analysis framework is constructed through EMD and Hilbert transformation, and rapid decomposition and feature extraction can be carried out on abnormal signals collected by edge nodes.

The invention relates to the technical field of power generation equipment fault diagnosis, and discloses a fan gear box fracture early warning method and device based on an acoustic emission positioning technology, and the method comprises the steps: obtaining the internal structure parameters of a fan gear box, and constructing a sound wave propagation model based on the internal structure parameters of the fan gear box; performing sound wave propagation simulation at the positions of the plurality of sound emission sources by using the sound wave propagation model to obtain sound wave propagation simulation data; acquiring an acoustic emission real-time signal, and performing path separation on the acoustic emission real-time signal by using the graph convolutional network model to obtain classification probability data of a multipath signal path; and based on the sound wave propagation simulation data and the classification probability data of the multipath signal paths, fault signal detection is carried out by using a reinforcement learning agent model, and fan gearbox rupture early warning information is obtained. According to the invention, the false alarm rate is effectively reduced, the accuracy and reliability of the early warning system are greatly improved, and a more reliable guarantee is provided for the early warning of the rupture of the fan gearbox.

The invention provides a distributed control system and a time synchronization method, and the method comprises the steps: a target data station determines whether time pulse signals are transmitted abnormally based on three received continuous time pulse signals; if not, the data station generates a contention owner request and sends the contention owner request to other data stations, and the contention owner request at least comprises a data station identifier; all data stations determine time data stations based on all contention owner requests of the round; and the time data station sends time information to other data stations in response to time synchronization requests sent by other data stations through a preset protocol, so that other data stations synchronize the received time information as current time to complete time correction of all data stations. The time signal is diagnosed, and the standard time source is dynamically adjusted, so that the accuracy and consistency of the time of the distributed system are ensured.

The invention discloses an electrical primary equipment switching operation analog simulation method based on real-time signal collection, and belongs to the technical field of analog simulation, and the method specifically comprises the steps: collecting related data generated by electrical primary equipment in the switching operation process, building a dynamic evolution prediction model of equipment switching operation based on the collected related data, and predicting the switching operation of the equipment according to the dynamic evolution prediction model. And inputting an output result of the dynamic evolution prediction model into a three-dimensional visual simulation platform, and dynamically generating a virtual operation scene of the electrical primary equipment, so that an operator observes electrical topology evolution and abnormal feedback of switching operation in real time in a virtual environment. Constructing a switching operation optimization rule base according to an operator action track and an abnormal triggering condition collected in a virtual simulation interaction process, and performing self-adaptive correction on the dynamic evolution prediction model; according to the application, high-simulation-degree simulation can be realized, the accuracy and reliability of switching operation are improved, and various complex working conditions and potential abnormal paths are covered in a virtual environment.

The invention relates to the technical field of communication, in particular to an anti-interference satellite signal capturing antenna suitable for a complex environment. Comprising an environment perception and digital twinning module, a multipath signal prediction and separation module, a synthetic array signal processing module, a beam forming control module, an incremental learning module, a radiation unit module, a temperature compensation module and a multi-system data interface. A direct signal and a characteristic multipath signal can be actively predicted and separated, so that the physical limitation of a traditional antenna is broken through, an anti-interference synthetic wave beam is formed in a polarization domain and a space domain in a combined manner, the wave beam can accurately point to a target satellite, and meanwhile, depth null is generated in a shielding area and an interference source direction; therefore, stable and high-gain acquisition of satellite signals is realized in a complex environment, the antenna system can continuously optimize the model according to real-time signal quality feedback, and the adaptive capability, the anti-interference performance and the long-term reliability of the antenna in a changing scene are improved.

The invention provides a brain electrical stimulation control method and device, a medium and computer equipment, and the method comprises the steps: setting an electrical stimulation period time signal through a local control module, and dividing the electrical stimulation period time signal into a stimulation stage, a window stage and a recovery stage with continuous time periods; in the stimulation stage, an electrical stimulation signal with preset parameters is output through the time-multiplexed electrode slice; in the empty window stage, disconnection or hardware protection is carried out on the electrode slices to shield crosstalk of stimulation signals to an electroencephalogram acquisition channel; in the recovery stage, electroencephalogram signals are collected and subjected to suppression algorithm processing to eliminate residual artifacts, and in the recovery stage, the collected electroencephalogram signals are subjected to suppression algorithm processing to eliminate residual low-frequency artifacts of electrode capacitance. The problems of hardware redundancy and artifact interference of an existing electrical stimulation system are effectively solved, and accuracy and safety are improved.

The present invention relates to the technical field of rail transit. Disclosed are a railway crossing control system, method and apparatus, and a storage medium. The system comprises: a speed measurement and distance measurement device, which is used for acquiring the real-time speed of a train and the real-time distance from the train to a crossing on the basis of the real-time signal strength and the real-time signal strength change rate of a detection signal; and a central device, which is used for determining a target crossing closure notification position by means of a pre-trained train operation state prediction model on the basis of the real-time speed of the train and the real-time distance from the train to the crossing, generating crossing closure information and sending the crossing closure information to a crossing device, and generating a train movement permission and sending the train movement permission to an onboard device. In the technical solution of the present embodiment, a crossing closure notification position is determined on the basis of the real-time speed of a train and the real-time distance from the train to a crossing, such that a crossing closure occasion can be automatically determined on the basis of the real-time running speed of the train, the distance from the crossing closure notification position to the crossing can be shortened, the crossing closure occasion is delayed, and the transportation efficiency on a railway side and a road side can be improved.

The invention belongs to the technical field of automatic monitoring of welding equipment, and discloses a welding machine counting and welding omission prevention monitoring system, which comprises a data acquisition module for performing multi-channel parallel acquisition on welding operation state data based on a time slice polling mechanism in a welding process, the collected welding operation state data are embedded into the parameter hash signature stream to form a tamper-proof real-time signal chain; the abnormal state judgment module is used for mapping the real-time signal chain into a state transition hypergraph, analyzing time sequence phase deviation between state nodes based on a time window sliding resonance detection algorithm, identifying a welding skipping related abnormal state and marking an abnormal topological position; the welding spot counting correction module is used for correcting the current welding spot counting value in real time when an abnormal topological position is detected, introducing a cost benefit balance factor between different nodes and outputting a corrected credible welding spot counting value; intelligent monitoring of welding and missing welding prevention are achieved, and operation safety and production efficiency are improved.

The invention discloses an ultrasonic stress-induced ice layer breaking deicing method, system and device for an insulator, and the method comprises the steps: sequentially transmitting a first frequency detection signal and a second frequency detection signal through an ultrasonic transmitting unit, collecting a reflection signal, and judging whether ice coating exists or not based on the characteristic difference of amplitude attenuation, propagation time difference and phase offset; if icing exists, deicing ultrasonic waves with initial power are transmitted, a power adaptive adjustment model is constructed by using real-time signals reflected by an icing layer, and the deicing power is dynamically optimized in combination with the characteristic change rate; the double-frequency signals are transmitted again at regular intervals, and after re-check reflection signals are collected and subjected to temperature correction, whether ice coating is completely removed or not is judged through characteristic differences; the system comprises an ultrasonic transmitting / receiving module, an icing judgment module and a power adaptive adjustment module. The device comprises a high-frequency ultrasonic generator, an ultrasonic transducer and a microcontroller. The device realizes integration of ultrasonic detection, deicing and effect verification, and is suitable for various insulator winter icing removal scenes.

The invention discloses an electronic fence safety control method and system based on behavior analysis. The method comprises the following steps: acquiring a signal of a fence area in real time through electronic fence equipment, and starting video monitoring equipment to obtain real-time video data; the central processing unit carries out preprocessing, target detection and tracking on the collected signals and data, analyzes a behavior mode of a target by utilizing a behavior analysis algorithm, and judges whether an abnormal behavior exists or not. And according to the behavior analysis result, making a decision according to a preset control strategy, controlling the electronic fence equipment to be turned on or turned off or triggering an alarm device, and recording related information of the abnormal event. According to the system and the method, multiple technical means are fused, so that accurate detection and quick response to security threats are realized, the intelligent level of security protection is improved, the false alarm rate is reduced, and the reliability and expandability of the system are enhanced.

The invention discloses a broadband oscillation uniform suppression method and system for an offshore wind power flexible direct current grid-connected system, and relates to the technical field of offshore wind power flexible direct current grid-connected control, and the method comprises the steps: carrying out the multi-source signal decoupling and frequency domain joint mapping of broadband oscillation characteristic parameters, and generating real-time signal characteristic data; performing impedance cooperative tuning and energy transmission path reconstruction processing on the real-time signal characteristic data to generate port energy absorption parameters; performing asymmetric coupling attenuation and time domain sequence reforming on the port energy absorption parameters to generate port post-dissipation electrical state parameters; processing the electrical state parameters after port dissipation through a switch sequence collaborative application to generate source end spectrum optimization parameters; after global parameters are subjected to feature analysis and classification, the construction of real-time signal feature data is realized by adopting independent component analysis and frequency domain joint mapping, so that not only is the decoupling and uniformity of multi-source signals enhanced, but also an accurate basis is provided for subsequent regulation and control.