11925 results about "Signal" patented technology

Techniques are provided for estimating an auto-correlation matrix for a transceiver that is coupled to a multi-antenna array. A different power sensor may measure the power of the signals received at a corresponding antenna of the multi-antenna array. The signals received at one antenna of each unique pair of antennas may be shifted in phase using a plurality (e.g., three) of different phase settings to generate a plurality of different shifted signals for each unique pair of antennas. Each of one or more power sensors, for each unique pair of antennas, may measure different combined powers based on the shifted signals and the RF signals received at the other antenna of the unique pair. A module may use the different combined powers with the power measured for each antenna to estimate the auto-correlation matrix.

The invention discloses an experimental calibration method based on multi-sensor signal fusion processing, particularly relates to the field of data analysis, and comprises the steps of a stepped confrontation disturbance protocol, data stream processing and real-time analysis, and multi-stage logic judgment and calibration execution. A stepped confrontation disturbance protocol is created for the first time, potential errors of the sensor are excited in real time and corrected in a closed loop mode under the condition that system operation is not interrupted, and a traditional shutdown calibration mode is thoroughly replaced; a multi-stage disturbance defense system is constructed, and composite interferences such as spectrum aliasing, overload distortion and signal-to-noise ratio attenuation caused by electromechanical thermal multi-physical field strong coupling are effectively suppressed; laser space calibration and dynamic rigidity modulation technologies are fused, and space drift errors caused by deformation of the mounting base and mechanical loosening are accurately diagnosed and compensated. Finally, long-term precision stability and data fusion reliability of the multi-source sensor under complex working conditions are achieved.

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The invention relates to the technical field of probe testing, and discloses a probe testing device and method, and the method comprises the steps: carrying out the synchronous collection of the electric signal response characteristics of a plurality of chip testing probes in a chip contact process, analyzing the signal transmission interference relation between the probes, and obtaining a multi-probe signal coupling feature pattern; identifying a target signal transmission channel to obtain a first test probe configuration scheme; performing test signal input and response acquisition on the chip to obtain electrical characteristic time-frequency data of the chip; and performing multi-probe collaborative analysis and feature extraction on the time-frequency data of the electrical characteristics of the chip to obtain abnormal indication information of the electrical performance of the chip, and adjusting test parameters and contact positions of a plurality of chip test probes to obtain a configuration scheme of a second test probe. Therefore, the test system can intelligently adjust the subsequent test strategy according to the preliminary test result, and the intelligence and automation of the test process are realized.

The invention provides a high-temperature chemical pipeline defect detection system and method based on magnetostrictive ultrasonic guided waves, and relates to the technical field of high-temperature chemical pipeline defect detection.The method comprises the steps that magnetostrictive excitation signals are applied, the propagation process of the guided waves in a pipeline is calculated through numerical simulation, and the amplitude and phase information of guided wave signals is obtained; performing time-frequency domain analysis on the guided wave signal, extracting group velocities and phase velocities of guided waves in different modes, determining frequency dispersion characteristics of the guided waves, and evaluating the influence of a high-temperature environment on guided wave propagation; processing the received guided wave signal by adopting a modal decomposition and reconstruction algorithm to obtain a corrected guided wave signal; and carrying out feature extraction and parameter estimation on the corrected guided wave signal to obtain the position, size and type information of the pipeline defect. According to the invention, the problem of multilayer heterogeneous pipeline guided wave detection in a high-temperature environment is solved, the accuracy and reliability of defect detection are improved, and an effective method is provided for pipeline health monitoring and safety assessment.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

The invention provides a traveling wave cable measurement sensing method and system based on a time-frequency domain combined reflection principle, and relates to the technical field of measurement, and the method comprises the steps: arranging a high-frequency signal transmitting device and a signal receiving device at one end of a traveling wave cable; establishing a time domain reflection function and a frequency domain reflection function of the traveling wave cable; constructing a time-frequency domain joint reflection model; collecting a reflected signal of the traveling wave cable through the signal receiving device to obtain time domain data and frequency domain data of the reflected signal; carrying out conjoint analysis on the time domain data and the frequency domain data by utilizing a time-frequency domain joint reflection model, and extracting characteristic parameters of a reflected signal; calculating impedance change and signal propagation characteristics along the traveling wave cable based on the characteristic parameters in combination with a time-frequency domain joint reflection model; through analysis of impedance change and signal propagation characteristics, intrusion detection, leakage positioning and fault diagnosis along the traveling wave cable are realized.

The invention relates to the technical field of discharge detection, in particular to a high-precision detection method and device for partial discharge of totally-enclosed GIS equipment. The method comprises the following steps: carrying out metal shell three-dimensional structure modeling on the GIS equipment, and carrying out electromagnetic shielding evaluation so as to obtain a multi-point electromagnetic shielding evaluation value; acquiring an original discharge signal of each sampling point based on the electromagnetic wave sensor, and performing interference suppression processing and back propagation attenuation compensation according to a multi-point electromagnetic shielding evaluation value, so as to obtain an attenuation compensation discharge signal waveform; micro-vibration response parameters are collected based on an MEMS micro-vibration sensor, actual influence waveform point detection marking and physical vibration interference filtering are carried out on attenuation compensation discharge signal waveforms, and therefore pure optimized discharge signal waveforms are obtained. According to the invention, through efficient and accurate partial discharge detection, real-time early warning of the state of the GIS equipment is realized, and the stability and safety of the equipment are improved.

The present application relates to a switch cabinet discharge fault detection method, system, equipment and medium, belonging to the technical field of power equipment fault detection, the fault detection method includes: receiving real-time monitoring data of the switch cabinet to be monitored and aligning the timestamp; performing dynamic amplitude compensation on the electric field signal data based on the aligned current humidity data, generating the corrected electric field signal data and performing time-frequency domain analysis, extracting the discharge pulse density and high-frequency energy proportion; performing frequency filtering and waveform analysis on the vibration signal, calculating the impact intensity factor and the vibration energy entropy value; making abnormal judgments on the discharge pulse density and the impact intensity factor, and marking the abnormal discharge according to the abnormal judgment results; based on the synchronous change correlation of the high-frequency energy proportion and the impact intensity factor and the vibration energy entropy value, verifying the abnormal discharge marking result, and obtaining the discharge fault result. The present application can realize efficient detection and accurate diagnosis of switch cabinet discharge faults.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention relates to the technical field of abnormity early warning, in particular to a transformer load abnormity early warning method and system, and the method comprises the following steps: obtaining an electrical parameter sequence and correcting a phase, constructing a current and power change rate sequence, extracting a load fluctuation trend factor, calculating a disturbance growth rate and judging whether the disturbance growth rate crosses a boundary, and recognizing a power gradient abrupt change feature. And judging whether periodic overlapping exists or not, and outputting a combined abnormity early warning identification sequence. According to the invention, by introducing phase difference and time base offset calculation, constructing a phase-aligned electrical parameter sequence group, and analyzing a periodic rate mean value and an integral quantity, disturbance trend evaluation is realized, a power gradient sequence is used to identify a section mutation interference signal, and a joint anomaly identification mechanism is constructed on two dimensions of a period and a section. The method achieves the composite judgment of the load disturbance trend and sudden change interference, effectively improves the accuracy and response time efficiency of load abnormity early warning, and reduces the recognition error risk caused by hidden fluctuation or local sudden change.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

Devices and methods enable optimizing a signal of interest based on identifying and analyzing the signal of interest based on radio frequency energy measurements. Signal data is compared with stored data to identify the signal of interest. Signal degradation data is calculated based on noise figure parameters, hardware parameters and environment parameters. The signal of interest is optimized based on the signal degradation data. Terrain data is also operable to be used for optimizing the signal of interest.

The invention relates to a calibration method of an ultrasonic flaw detector calibrating device, which comprises the following steps of: acquiring and transmitting ultrasonic waves of which the frequency is matched with the theoretical frequency of a material to be detected, acquiring and dynamically correcting the actual propagation path of the ultrasonic waves in real time, generating a calibration parameter of a frequency-delay compensation coefficient, and compensating the transmitted ultrasonic waves based on the calibration parameter. After the compensated ultrasonic waves enter a to-be-detected material, time-base line signals are collected and input into the trained deviation calculation model to calculate time axis scale deviation, time-base line linearity is adjusted based on the time axis scale deviation, stepped ultrasonic attenuation signals are generated, echo signal amplitude values of the flaw detector are collected, and the amplitude values of the echo signals of the flaw detector are calculated. And comparing the echo signal amplitude value with a preset amplitude threshold value to obtain a dynamic difference value of the two, and adjusting the attenuation amount in real time based on the dynamic difference value. The problems of insufficient calibration efficiency and consistency, poor dynamic parameter adaptability, limited sensitivity calibration precision and complex working condition simulation capability in the prior art are solved.

The invention relates to the technical field of draught fan detection, in particular to a draught fan current detection method and system, computer equipment and a storage medium, and the draught fan current detection method comprises the following steps: collecting draught fan current data, analyzing the time sequence characteristics of the draught fan current data, monitoring and comparing the change trend of the distance between adjacent points, and determining the distance between adjacent points; and determining an optimal embedding dimension and time delay through a phase space reconstruction method, and generating a fan current phase space trajectory. According to the method, the time sequence characteristics of the fan current data are analyzed, the embedded dimensions and the time delay parameters are optimized by adopting the phase-space reconstruction method, and the track point distribution mode is deeply analyzed, so that the capability of capturing abnormal behaviors is effectively improved. The transient characteristic signals are classified and indexed by combining the signal decomposition and frequency band screening technology, the short-time abnormal fluctuation in the current signals is accurately identified, and the response capability to the short-time high-frequency signal change is improved.

Logic may send information on cross-link interference reference signal (CLI-RS) resource and measurement configuration via an interface to a second base station, the CLI-RS resource and measurement configuration to comprise an identification of a communication resource within which to measure the CLI-RS. Logic may cause transmission of the CLI-RS. Logic may receive the CLI-RS measurement report based on measurement of the CLI-RS based on a definition for a CLI-RS measurement report from the second base station via the interface. Logic may decode information on CLI-RS resource and measurement configuration received via the interface from a first base station. Logic may measure the CLI-RS based on identification of the communication resource. Logic may generate the CLI-RS measurement report based on measurement of the CLI-RS. And logic may send the CLI-RS measurement report to the first base station.

The invention provides a data transmission rapid verification method of a non-standard Type-C interface, which comprises the following steps: firstly, obtaining a verification request triggered when target equipment accesses the non-standard Type-C interface, extracting interface real-time signal characteristics including a voltage fluctuation sequence and the like based on the physical connection state identifier, and sending the extracted interface real-time signal characteristics to the non-standard Type-C interface; and then dynamically matching the real-time signal characteristics with a standard transmission parameter library, generating a test instruction set containing a priority ranking verification action and a trigger condition threshold value, calling the test instruction set to perform staged signal injection and feedback capture on the interface, recording an execution result and marking a time sequence, and finally generating a verification report according to the result. And the abnormal nodes which do not meet the transmission stability index are mapped to the interface physical structure defect description, so that rapid, comprehensive and accurate data transmission verification of the non-standard Type-C interface is realized.

The invention relates to the technical field of abnormal state prediction, provides a multi-dimensional time sequence equipment abnormal state prediction method and system, and solves the problems of high false alarm rate and risk prediction inaccuracy in the prior art. The method comprises the following steps: collecting a multi-node operation state data set and communication transmission layer time sequence offset information of an equipment distributed system; jitter and delay association processing is carried out on the data, and a communication stability feature set is generated by quantizing space-time correlation of a jitter extreme value and delay fluctuation; obtaining physical layer deformation and temperature drift data of the communication cable, analyzing and generating a physical layer disturbance feature sequence based on optical signal feature offset, and converting the physical layer disturbance feature sequence into channel abnormal strength features; and cross-level feature coupling is carried out on the communication stability feature and the channel abnormal strength feature, and a communication node failure or data transmission abnormal risk is predicted based on a coupling result. According to the invention, millisecond-level linkage prediction of equipment-level physical disturbance and system-level communication risks is realized, the fault false alarm rate is reduced, and the cascade failure risk is blocked.

The invention relates to the technical field of electrical equipment fault diagnosis, and discloses an electrical equipment multi-modal fault diagnosis method based on dynamic self-adaption, and the method comprises the steps: obtaining an original multi-modal signal flow containing vibration, temperature and current signals, inputting the original multi-modal signal flow into a dynamic self-adaption diagnosis network, obtaining a fault feature matching result set, and determining each modal result. The network is trained by historical fault data, and the data comprises time domain, frequency domain and fusion feature parameters extracted from continuous multi-mode signals, and corresponding fault type labels and confidence coefficients. The network comprises a cross-modal feature fusion module, a spatial-temporal feature coding module and the like, and when the confidence coefficient of at least two modal results exceeds a dynamic threshold value, three-level early warning is triggered. The method improves the comprehensiveness, accuracy and real-time performance of diagnosis, and is suitable for fault diagnosis of electrical equipment.

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 a copper bar connection state detection method and system, particularly relates to the technical field of copper bar state detection, and is used for solving the problem of poor early recognition of copper bar connection deterioration. According to the method, resistance and temperature rise conditions at the joint of the copper bars are measured synchronously in the load change process, signal denoising is realized in combination with finite difference filtering, and characteristic evolution conditions at the joint are captured; the method comprises the steps of determining an analysis window based on a real-time load change event, constructing a thermoelectric track point set and dividing a load rising and falling period, forming a continuous dynamic thermoelectric response curve, dividing a temperature rising track and a temperature falling track through a temperature change trend, extracting a resistance drift amount and a hysteresis loop area, and identifying a connection degradation trend and a structure fatigue characteristic. And connection state information generated by thermal stress action and thermal cycle asymmetry is obtained during load change, and a detection strategy is adjusted according to an analysis result, so that early degradation identification and intelligent risk grading of the copper bar connection state are carried out, and the sensitivity and accuracy of copper bar detection are improved.

The invention provides a multi-parameter fusion drilling tool state intelligent diagnosis method, device and equipment, and the method comprises the steps: determining a stable working period through obtaining basic operation parameters of a drilling tool, and applying specific frequency excitation vibration to the drilling tool in the stable period to form an active propagation wave; a multi-point monitoring technology is adopted to obtain a response vibration signal and extract a time sequence, and a time sequence offset is obtained through differential processing; identifying a signal propagation delay section based on the time sequence offset, extracting actual propagation time, comparing the actual propagation time with standard propagation time to generate a time delay abnormal value, and determining an internal state change position; a high-damage section is determined by combining energy dissipation analysis, and fault positioning information is generated through spatial superposition; performing frequency sensitivity analysis by utilizing the damage characteristic parameters, capturing a resonance response peak value through frequency sweep excitation, and forming a secondary diagnosis result in combination with the wear severity; and finally, determining a fault development rate, generating a partition maintenance instruction, and completing intelligent diagnosis of the drilling tool state.

The invention relates to the technical field of anti-electromagnetic interference, in particular to an anti-electromagnetic interference method for a flywheel energy storage system of a data center, which comprises the following steps: acquiring change rate data of the rotating speed and the angular speed of a flywheel, judging the attribution of a disturbance interval, recording change characteristics, extracting an interference identifier, comparing the interference identifier with impedance change, and generating a switching node. Frequency response and reflection coefficient characteristics of the filtering unit are detected, a coupling channel generation label is identified, and output fluctuation is evaluated to generate an anti-interference capability state label. According to the method, the surface resistivity of the shielding layer is dynamically switched according to the comparison result of the specific interference triggering identification bit and the signal link impedance change state in each disturbance stage through classification judgment and correlation marking of inertial disturbance, so that the effect of suppressing electromagnetic interference in real time is optimized; the influence degree of electromagnetic interference can be dynamically evaluated in the system operation process, and the adaptability of the energy storage system to the electromagnetic interference is judged and adjusted in real time.

The invention discloses an actuator sealing performance test method and system, particularly relates to the technical field of industrial equipment sealing performance detection, and is used for solving the problem of positioning failure caused by signal aliasing in a multi-leakage scene in an existing method. Pressure attenuation frequency spectrum characteristics and leakage impact frequency spectrum peaks are extracted respectively by pressurizing a sealed cavity and synchronously collecting dynamic pressure and vibration signals; calculating a path loss value based on the amplitude gradient and the propagation time difference, and screening candidate leakage nodes; constructing a space grid model to solve three-dimensional coordinates of the leakage source; after the coordinates are projected to the surface of the structure model, pressure modal coherence and vibration impact energy are coupled and analyzed to generate a detection map; and finally, outputting a multi-leakage-point identification graph through spatial clustering and intensity grading. Accurate separation and spatial positioning of multiple leakage signals are achieved, and the reliability of complex sealing structure detection is remarkably improved.

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention discloses a power grid equipment state sensing driving dynamic response method based on the Internet of Things technology, and relates to the technical field of power system automation and informatization, and the method comprises the following steps: S001, collecting original multi-dimensional state signals of a plurality of sensors deployed in a power grid equipment state sensing channel, constructing an electromagnetic disturbance recognition model, and carrying out the recognition of the original multi-dimensional state signals; frequency domain and time domain feature extraction is carried out on the signals, and a feature comparison parameter set used for distinguishing electromagnetic interference and real faults is generated. Frequency domain and time domain features are extracted through an electromagnetic disturbance recognition model, dynamic threshold judgment and response strategy adjustment are achieved in combination with multi-source sensing data, interference and faults can be accurately distinguished, early warning and protection logic can be corrected in real time, protection actions can be accurately triggered, closed-loop control is achieved, the delayed fault tolerance and multi-source verification capacity is achieved, and the method is suitable for large-scale popularization and application. The false operation rate and the false stop risk are effectively reduced, and the intelligence, the safety and the stability of power grid operation are improved.

The invention relates to the technical field of adaptive control, in particular to an electrical automation adjustment method and system, and the method comprises the following steps: obtaining a three-phase current signal, collecting single-cycle high-frequency sampling, extracting an instantaneous extreme point, recording a time coordinate and an amplitude value, calculating a time interval and a change rate, screening the distribution of the extreme point, and obtaining a harmonic distortion characteristic parameter. According to the method, on the basis of high-frequency sampling, instantaneous extreme point extraction, time interval and change rate calculation, harmonic distortion feature description enhancement, extreme value growth rate and phase drift rate are utilized, time periods with prominent fluctuation amplitude are screened, harmonic change trend prediction precision is improved, and the harmonic change trend prediction precision is improved through zero-sequence component and phase offset calculation in combination with periodic change features. The harmonic source is accurately separated, the harmonic cause identification capability is improved, the compensation current is calculated based on the harmonic source, the injection opportunity is optimized, the compensation parameter is adjusted before the harmonic peak value, and the compensation response speed and the matching precision are improved.

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

The invention discloses a metering equipment interference signal suppression method and system in an electromagnetic compatibility test, and the method comprises the steps: obtaining an input signal of metering equipment in the electromagnetic compatibility test, performing windowing processing on the input signal by adopting a self-adaptive fractional order window function, and then obtaining a frequency spectrum component of the input signal through self-adaptive fractional order Fourier transform; calculating the amplitude of the frequency spectrum component, comparing the amplitude with a power threshold value, and screening out the frequency spectrum component of the interference signal; establishing a spectrum model of the interference signal based on the interference signal spectrum component; and generating a frequency domain compensation signal considering interference signal phase correction, converting the frequency domain compensation signal into a time domain compensation signal, and superposing the time domain compensation signal and the input signal to obtain a signal after interference suppression. According to the method, the frequency spectrum characteristics of the signals in different frequency bands can be ensured to be clear, the interference signal components can be accurately identified, the energy distribution of the interference signals can be effectively simulated, and the high-efficiency interference suppression capability can still be kept in a variable signal environment.