2746 results about "Spectrum analysis" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

The invention provides an encryption transmission method and system of a wireless information transmission communication base station, relates to the technical field, discloses the encryption transmission method of the wireless information transmission communication base station, and belongs to the technical field of wireless communication. The method comprises the following steps: receiving a communication signal and extracting identity authentication information and a signal characteristic parameter; performing identity authentication scoring based on a deep neural network, performing signal feature scoring through spectral analysis, and constructing a communication security assessment model; a key pair is generated by adopting an elliptic curve cryptographic algorithm, and data is encrypted after being subjected to confusion transformation and diffusion transformation; transmitting a key by using a quantum key distribution technology; and selecting an optimal transmission channel based on the operation state data of the communication base station, and performing data transmission by adopting interleaving coding and adaptive power control technologies. According to the invention, the security and transmission reliability of wireless communication are improved, and the anti-interference capability is enhanced.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent device communication method and system based on the Internet of Things. The method comprises the following steps: acquiring spectrum occupation data, electromagnetic interference mode data and terminal equipment energy state data of a plurality of communication channels in an industrial environment through a preset distributed spectrum sensing network; performing adaptive spectrum analysis processing on the spectrum occupancy data and the electromagnetic interference mode data to obtain channel quality evaluation data including interference source characteristics and channel attenuation characteristics; and according to the channel quality evaluation data and a pre-acquired historical channel performance memory bank, performing priority ranking on the available communication channels to obtain dynamic channel priority data based on time continuity. The spectrum resource allocation condition in the industrial environment is monitored in real time through the distributed spectrum sensing network, the current interference can be identified, and the interference can be pre-judged through an advanced prediction model.

A tracking and adaptive compensation method for power fluctuation of a pulse load of a ship is provided. The method includes: based on a microgrid topology model of the ship, a pulse load model of the pulse load, and an interharmonic power and pulse parameter relationship model, performing power fluctuation tracking, frequency selection and a power spectrum analysis to obtain filtering parameters of a low-pass filter and filtering parameters of a band-pass filter; performing waveform adaptation to eliminate a phase delay caused by the low-pass filter and the band-pass filter, and obtaining a control command for a power compensation strategy; and generating, by a power loop, a pulse width modulation (PWM) signal to control a switching tube in a bidirectional direct current (DC) / DC converter of each of an energy storage battery pack and a supercapacitor of a hybrid energy storage system.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention provides a high-precision five-axis rotary table cooperative control method and system based on multi-closed-loop feedback, and the method comprises the steps: obtaining a real-time acceleration signal of a five-axis rotary table, carrying out the spectrum analysis of the acceleration signal, determining a current resonant frequency drift range and a main frequency component, and carrying out the frequency spectrum analysis; dynamically simulating the turntable structure by adopting a pre-established finite element model, and predicting the vibration mode change trend of the structure by comparing the deviation between the modal frequency obtained by simulation and the actually measured signal spectrum; according to the vibration mode change trend, matching similar vibration mode modes from a pre-established rotary table vibration mode database, obtaining corresponding vibration energy transmission path distribution, and determining an energy transmission abnormal region by analyzing the distribution of energy on each transmission path to obtain vibration energy transmission characteristics; according to an optimized motion instruction of a five-axis turntable, the dynamic response of the turntable is estimated, vibration frequency domain characteristics are extracted, and frequency domain compensation parameters are generated in combination with a preset frequency domain error matrix.

The invention provides a graphene super-capacitor capacity energy-saving control method based on an intelligent algorithm, and the method comprises the steps: obtaining the harmonic content data of a power grid, carrying out the spectrum analysis of the harmonic content through a Fourier transform algorithm, and obtaining the change trend of the harmonic content; acquiring historical capacitor bank impedance characteristic data and a corresponding harmonic content change trend, and calculating impedance matching degrees of the graphene super capacitor under different load conditions; according to the impedance matching degree of the graphene super capacitor, generating a capacitor bank switching sequence by adopting a dynamic programming algorithm, and determining a target switching sequence of the graphene super capacitor; switching of the graphene super capacitor according to the target switching sequence is executed through an edge calculation unit, the access state of the graphene super capacitor is adjusted in real time, and harmonic wave changes of a power grid are dynamically matched; the method comprises the following steps: acquiring transient current data of a system, and decomposing the transient current data by adopting a wavelet transform algorithm to obtain a frequency component of transient current;

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system is improved.

The invention is an advanced deep learning system that combines a latent transformer core with a latent dynamics analyzer. This system processes input data into latent space vectors, which are then analyzed in parallel for both prediction and dynamic modeling. The latent transformer generates short-term predictions, while the latent dynamics analyzer derives equations of motion describing the underlying system dynamics. By integrating spectral analysis and change detection, the system can identify significant shifts in behavior, particularly useful for complex systems like financial markets. The invention enables more accurate predictions, interpretable insights, and early detection of regime changes. Its end-to-end training approach ensures all components work harmoniously, balancing predictive accuracy with physical plausibility and interpretability.

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention discloses an efficient and high-precision compensation system and method of an intelligent mutual inductor, and particularly relates to the technical field of mutual inductors. The method comprises the following steps: acquiring magnetic flux density and magnetic field intensity data of a mutual inductor under different current and voltage change rates, generating a hysteresis loop diagram, extracting hysteresis loop width, residual magnetism and coercive force characteristics, and establishing a hysteresis effect error model; the harmonic distortion is identified in combination with spectrum analysis, the harmonic distortion degree in a high-frequency environment is predicted, the hysteresis of the hysteresis effect and the harmonic distortion are comprehensively analyzed, and the accuracy of a compensation algorithm for processing nonlinear errors is evaluated; and finally, the compensation signals are divided into high-accuracy signals and low-accuracy signals, the high-accuracy signals are directly output, and the low-accuracy signals are output after error correction, so that the problem that a conventional compensation algorithm cannot correct non-linear errors is effectively solved, and the measurement precision of the mutual inductor in an extreme environment and the operation stability of a system are improved.

The invention relates to the technical field of signal acquisition, and discloses a signal acquisition method and system for an MEMS gas sensor array, and the method comprises the steps: carrying out the analog-to-digital conversion and temperature and humidity compensation of resistance value signals synchronously acquired by a plurality of sensing units in the MEMS gas sensor array, and obtaining a compensated response matrix; performing fast Fourier transform and dynamic feature extraction to obtain a frequency domain feature matrix of each sensing unit; performing sensing unit crosstalk coefficient calculation and response component decoupling to obtain a decoupling feature vector; carrying out Hilbert transform and adsorption-desorption characteristic extraction to obtain a gas sensitive characteristic sequence; inputting a self-adaptive power spectrum analysis model to carry out cross sensitivity identification processing to obtain a target gas response component; according to the method, the accuracy of target gas detection and concentration quantitative analysis is improved.

The invention relates to the technical field of data processing, and discloses a packaging equipment inspection method and system. The method comprises the steps that packaging process parameters are collected through multiple sensors, and a working condition data set is formed; extracting abnormal features by using spectral analysis, and generating an operation deviation index; quality and state data are compared to establish an association rule, and a fault precursor feature library is obtained; the station coordination is analyzed through time sequence comparison, and a component state evaluation table is formed; calculating the fault probability of the key component, and generating an inspection point sequence; and making an inspection scheme including parameters, standards and periods, and forming a preventive maintenance plan. According to the method, key parameters such as tension, temperature and speed in the packaging process are analyzed, the early characteristics of the abnormal operation state of the equipment are recognized, the routing inspection priority and the maintenance period are determined in combination with historical fault data and component state evaluation, and conversion from passive maintenance to active prevention is achieved.

The invention relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

A wide-band oscillation wide-area monitoring system and a wide-band oscillation source localization method, which relate to the technical field of power system stability. The wide-band oscillation wide-area monitoring system comprises: a plurality of signal sensing modules, configured to acquire electrical signals from measured points; a plurality of wide-band measurement devices connected to the signal sensing modules, each wide-band measurement device being configured to perform spectrum analysis on the basis of electrical signals acquired by each signal sensing module, and generate oscillation alarm information on the basis of the spectrum analysis result, and the wide-band measurement devices being further configured to: in response to a waveform recording instruction, start to carry out waveform recording to generate waveform recording files for a same time section, and upload the waveform recording files and the spectrum analysis results; and a wide-band measurement master station in communication connection with each wide-band measurement device, the wide-band measurement master station being configured to: on the basis of the oscillation alarm information uploaded by each wide-band measurement device, determine whether a wide-band oscillation risk is present for a power system, and if so, send the waveform recording instruction to all of the wide-band measurement devices, and on the basis of the spectrum analysis result fed back by each wide-band measurement device, perform oscillation source localization.

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention relates to the technical field of valve opening control, and discloses a valve opening control method for user-side hydraulic balance. The method comprises the following steps: carrying out parameter acquisition on the buried pipe cold storage system to obtain dynamic system parameter data; performing cold load fluctuation spectrum analysis and multi-scale decomposition based on the dynamic system parameter data to obtain a user demand prediction model and a hydraulic balance ideal flow distribution proportion; generating a comprehensive system state evaluation index according to the user demand prediction model and the hydraulic balance ideal flow distribution proportion; constructing a global optimization objective function based on the comprehensive system state evaluation index, and solving to obtain a time-phased valve adjustment strategy; and a valve flow characteristic model is constructed according to the time-phased valve adjusting strategy and the valve historical response data, the target opening degree of each adjusting valve is calculated, and a valve control execution instruction is output. The hydraulic balance strategy can be dynamically adjusted according to different working conditions, and flexible coordination of hydraulic balance and cooling requirements is achieved.

The invention provides an intelligent anesthesia depth monitoring method and system. A plurality of physiological parameter sensors are used for synchronously collecting composite physiological signals of a patient, and meanwhile brain wave signals of the patient in the anesthesia process are obtained; analyzing and processing the anesthesia state by fusing an electroencephalogram spectrum analysis algorithm and a nonlinear kinetic model to obtain a consciousness state map of anesthesia depth and consciousness edge fluctuation; performing real-time evaluation on the current anesthesia depth by using an adaptive threshold algorithm in combination with a Bayesian reasoning framework, and performing prediction processing on the trend change of the anesthesia depth in a future short time by using a long-short term memory neural network to obtain an evaluation result and a prediction trend; the anesthetic infusion rate is automatically adjusted so as to maintain the ideal anesthesia depth, meanwhile, potential risks are subjected to intelligent grading early warning processing, and an anesthesia management strategy is generated. According to the technical scheme provided by the invention, the safety and personalized management level of the anesthesia process are improved.

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

The invention relates to the technical field of polaroid performance optimization, and discloses a performance optimization design method and system for a multi-layer composite polaroid, and the method comprises the steps: carrying out the polarization spectrum analysis of a use environment of a display screen, obtaining incident polarization spectrum data, and calculating the initial stretch ratio data of each polarization layer; measuring to obtain molecular orientation degree data and polarization performance data of each polarization layer; performing feature extraction and fusion to obtain a fusion feature vector of each polarization layer; calculating the influence of the molecular orientation degree change of each polarized light layer on the optical performance of different polarization states to obtain the importance weight of the polarization state of each polarized light layer; reinforcement learning optimization is carried out, and a stretch ratio adjustment strategy of each polarization layer is generated; according to the technical scheme, multi-objective optimization and sequential quadratic programming are carried out on the processing parameters of each polarizing layer, and the stretching temperature and speed control sequence of each polarizing layer is generated, so that the precise control of the stretching temperature and speed is realized, and the performance index and the production efficiency of the polaroid are improved.

A broadband measurement method and apparatus for a power system, and a computer device, which relate to the technical field of broadband measurement. The method comprises: acquiring a target electrical signal; on the basis of a local mean decomposition algorithm, decomposing in a time domain the target electrical signal into a plurality of signal components having a single frequency; determining the instantaneous frequency of each signal component; on the basis of the instantaneous frequencies, determining a corresponding time-frequency transformation algorithm and a corresponding time-frequency transformation parameter; and on the basis of the corresponding time-frequency transformation parameter, using the corresponding time-frequency transformation algorithm to perform time-frequency transformation on each signal component, so as to obtain a broadband spectrum that comprises frequency-domain information corresponding to each signal component. The method is applicable to spectrum analysis performed on a voltage / current signal that includes a plurality of signal components, the calculation is quickly and precisely performed, the broadband measurement requirements of a new power system can be met, and a strong support is provided for constructing a wide-frequency-oscillation wide-area monitoring system for the power system.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention provides a self-adaptive anti-interference method based on unmanned aerial vehicle communication interference monitoring, and belongs to the technical field of unmanned aerial vehicle communication, and the method comprises the steps: obtaining all signal information in a communication range of an unmanned aerial vehicle, carrying out the spectrum analysis of the signal information, and recognizing a normal communication signal and an interference signal according to a spectrum analysis result; performing interference feature extraction on the interference signals, classifying the interference signals according to interference features, determining an interference source based on a classification result, and positioning the interference source; adjusting the original network topology of the unmanned aerial vehicle based on the positioning result, and performing deployment optimization on the relay node to obtain an updated network topology; the communication parameters and the flight path of the unmanned aerial vehicle are adjusted according to the updated network topology, the communication quality and the system adaptability are improved, and the reliability and the stability of a communication system are improved.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and system based on dynamic data monitoring, and the method comprises the following steps: obtaining geological displacement monitoring data, setting a time window range, respectively calculating a local extreme point and a local zero point of the monitoring data in a time window, and calculating the local extreme point and the local zero point of the monitoring data in the time window; obtaining a multi-scale reference value; and based on the multi-scale reference value, calculating a comprehensive statistical magnitude, and generating a data quality evaluation parameter. According to the method, a local extreme point and a zero point are dynamically calculated by setting a time window range, a multi-scale reference value is constructed to carry out comprehensive statistic generation and data evaluation, and collaborative discrimination of piecewise linear regression and threshold comparison are executed to form a critical index; and performing characteristic quantity recursive residual spectrum analysis and matrix decomposition operation by combining the power spectral density distribution and the spectral density matrix to establish an early warning characteristic sequence, and realizing multi-stage early warning indication output based on a grading rule.

The invention discloses a poultry health condition detection method, system and device and a storage medium, and relates to the technical field of poultry health condition detection.The poultry health condition detection method comprises the steps that poultry audio data are collected and preprocessed; based on the time-frequency characteristics of the audio data, constructing an MAE model; and performing pre-training and fine tuning on the MAE model, and evaluating the pre-trained MAE model. Based on a sound spectrum analysis technology, pre-emphasis filtering and Mel-spectrum conversion are combined, the key feature extraction capacity of poultry sound is enhanced, and the method adapts to the complex environment of a livestock farm. An MAE model is adopted, time-frequency characteristics are learned through self-supervised pre-training and a Transform structure, automatic audio acquisition and analysis and real-time health state output are realized, and the labor cost is reduced. The attention visualization technology is introduced, key frequency bands are identified in an auxiliary mode, the model interpretability is enhanced, and the whole scheme has the advantages of automation, high precision and high robustness.

The invention relates to the technical field of power transmission line anomaly recognition, and particularly discloses a power transmission line anomaly recognition model construction method combined with edge calculation, which comprises the following steps of: gradually increasing electromagnetic interference intensity test, acquiring current and voltage signals in real time, and monitoring electric field intensity and signal change by combining a high-precision sensor; dynamically calculating an influence value of electromagnetic interference; recursively estimating the dynamic influence of the electromagnetic interference through a Kalman filter, accurately modeling the influence of the electromagnetic interference by combining the system state and measurement data updating, and realizing the real-time evaluation of the influence of the interference on the system by calculating an electromagnetic interference coefficient and an electromagnetic shielding efficiency expected deviation; according to the method, a spectral analysis and high-frequency current and voltage fluctuation feature extraction method is adopted, and accidental interference and system faults are effectively distinguished by calculating a spectral ratio; through multi-level data analysis and optimization, real fault signals and electromagnetic interference signals can be accurately distinguished, and false alarms and missing alarms are reduced.

The invention discloses an electroencephalogram signal frequency domain feature extraction optimization method and system, and relates to the technical field of biomedical signal processing, and the method comprises the steps: obtaining a time domain signal; carrying out noise removal processing on the acquired time domain signal by adopting an adaptive noise filtering algorithm to obtain a processed time domain signal; performing spectral analysis on the processed time domain signal through a frequency domain conversion algorithm to generate a frequency domain signal; dividing frequency bands of the frequency domain signal according to different frequency ranges to obtain decomposition signals of a plurality of frequency bands; extracting frequency domain features from the decomposed signals, and summarizing the extracted frequency domain features to generate an initial feature set; performing dimension reduction processing on the initial feature set by applying a dimension reduction technology to obtain an optimized feature set; and classifying the optimized feature set by using a machine learning model suitable for a classification task to obtain a classified feature set. The method solves the problems that in the prior art, the current electroencephalogram signal processing efficiency is poor, and the practical application of the electroencephalogram signals is affected.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.