21 results about "Time frequency spectrum" patented technology

The invention discloses a heat dissipation unit control method and electronic equipment, and relates to the technical field of heat dissipation unit control, and the method comprises the steps: obtaining acoustic signals and vibration signals of a heat dissipation unit in a server in various operation states, and determining a baseline embossing feature and a resonance frequency spectrum of the heat dissipation unit, so as to establish a heat dissipation acoustic archive; acquiring and analyzing a real-time acoustic signal and a real-time vibration signal of the heat dissipation unit to obtain a real-time embossing feature and a real-time frequency spectrum, and judging whether the running state of the heat dissipation unit is abnormal or not; in response to abnormal operation of the heat dissipation unit, judging whether the heat dissipation unit has a resonance risk or not; and in response to existence of the resonance risk, generating a fusion adjustment instruction based on the feature comparison result and the spectrum comparison result. The operation state of the heat dissipation unit can be sensed in real time and pre-controlled, the resonance risk and the abnormal state are avoided, noise is reduced, and the problems that noise control lags behind, depends on hardware structure modification and cannot avoid the resonance risk in advance in the prior art are solved.

The invention relates to a method for realizing full-time frequency spectrum monitoring based on a nest unmanned aerial vehicle and a fixed monitoring station. The method comprises the following steps: issuing a full-time frequency spectrum monitoring task; continuously scanning the monitoring target frequency band; extracting a signal frequency, and performing parameter measurement; signal parameters which are not matched with the known signal library are stored, and the equipment state is recovered; setting a time interval, and generating a direction finding task list; the unmanned aerial vehicle advances according to a preset track and performs a monitoring direction finding task; the unmanned aerial vehicle returns to enter the nest, and charging and heat dissipation reconditioning are automatically completed; and updating the signal positioning data. By adopting the method, the system, the device, the processor and the computer readable storage medium for realizing full-time frequency spectrum monitoring based on the nest unmanned aerial vehicle and the fixed monitoring station, a reliable positioning result is given through a multi-point direction finding result of the unmanned aerial vehicle on the whole track, high-frequency weak signals are easy to capture, the monitoring utilization rate of the unmanned aerial vehicle is greatly improved, and the real-time performance of the unmanned aerial vehicle is improved. The total element information of the signal is collected, and the management is scientific and accurate.

The application discloses a teaching method for the characteristics of the singing tune of the Liuqin opera based on sound processing, and comprises the following steps: constructing a singing tune audio library, and marking the time axis characteristics by experts to form a first positive sample; carrying out human voice separation, band filtering and differential frame processing based on rhythm types on the sample to generate a time-spectrum graph as a third positive sample; extracting a singing tune characteristic sequence through a characteristic extraction network; finally, using the trained network to carry out multi-dimensional evaluation on the singing tune of students, calculating quantitative indexes such as overall similarity, rhythm deviation and time sequence deviation, and providing objective singing feedback and individualized correction guidance for students. The application realizes the change of the teaching of the Liuqin opera from traditional experience transmission to modern quantitative analysis, and significantly improves the teaching efficiency and precision.

The invention discloses an insulator internal defect identification method and system based on a terahertz technology, and relates to the technical field of nondestructive detection.The method comprises the steps that firstly, a frequency spectrum obtained after echo signal processing of an insulator is obtained in the healthy state of the insulator and serves as a reference healthy fingerprint; then, echo signals are monitored in real time, the real-time frequency spectrum is compared with the reference frequency spectrum through a state difference value method, and newly-added reflection caused by internal defects such as microcracks is recognized. Furthermore, the invention provides an optimized deployment strategy for installing the sensors at electric field change sensitive positions in the insulator string, and provides a scheme for carrying out data cross validation by adopting multiple sensors for a long insulator string, so that the detection accuracy is improved to the maximum. The system is also integrated with a LoRa wireless communication module, and reliable remote transmission of data in a high-voltage strong electromagnetic interference environment is realized. According to the invention, on-line, lossless and high-precision monitoring of internal defects of the insulator is realized.

The application provides a self-supervised abnormal sound detection method based on spectrum and time information combination, comprising the following steps: collecting original audio clips of each target device in advance, and storing the original audio clips in association with machine ID as an identifier; using a preset mel filter to extract a mel spectrum graph of the original audio clips; using a preset time feature extraction network to extract a time spectrum feature graph of the original audio clips; performing feature fusion on the mel spectrum graph and the time spectrum feature graph of each target device respectively; inputting the mel and time fusion features of each target device into a classifier, taking the machine ID of each target device as a category label, and training to obtain a target classification model; in the test, inputting the to-be-tested sound information into the obtained target classification model to obtain real-time feature sound indicators of each target device, and judging whether the target device is in an abnormal state according to the comparison result of the real-time feature sound indicators and a critical value.

The application discloses a real-time recursive discrete Fourier transform implementation method and system based on FPGA. The method comprises the following steps: obtaining an initial frequency spectrum result; buffering an input data window, obtaining a removed sampling value and a newly entered sampling value through sliding update; for a target frequency point, performing differential update on the current frequency spectrum result, the removed sampling value and the newly entered sampling value, and performing complex multiplication in combination with a rotation factor lookup table to obtain an updated frequency spectrum result; buffering and feeding back the updated frequency spectrum result, and outputting a continuous real-time frequency spectrum analysis result corresponding to the target frequency point. The method does not need to recalculate all frequency points every window, significantly reduces the calculation amount and resource consumption, supports an arbitrary window length, has strong real-time performance, and is suitable for embedded spectrum analysis scenes.

The invention belongs to the technical field of power electronics, and discloses a harmonic suppression optimization method and device for an electric energy quality treatment device. The method comprises the following steps: synchronously acquiring a three-phase signal of a power grid target node, and preprocessing to obtain a normalized signal; analyzing to obtain a current power spectral density, a fundamental wave parameter and a real-time spectrum map; carrying out harmonic blind separation on the normalized signal, and extracting a feature vector from a separated signal; searching frequency spectrum holes based on a real-time frequency spectrum map, evaluating the interference cost in combination with the feature vector, and generating an optimal suppression strategy according to interference cost maximization; generating a modulation voltage according to a suppression strategy, and injecting the modulation voltage into the IGBT as a switching signal to realize harmonic suppression; the method effectively breaks through the limitation that the exogenous harmonic waves are useless in the prior art, remarkably improves the adaptability and harmonic suppression accuracy of the device in a complex power grid environment, ensures that the total harmonic distortion rate of the power grid meets the national standard requirement, and guarantees the stable electric energy quality.

To separately collect a sound field radiated from a sound source in an area and a sound field coming from the outside of the area by using a distributed spherical microphone array.SOLUTION: A separation matrix calculation part 10 of a sound collection device 1 calculates a separation matrix in which position information and size are reflected and which is expressed by a spherical harmonic function on the basis of preset distributed spherical microphone array position information. The frequency analysis unit 11 obtains a time-frequency spectrum by performing Fourier transform on a sound collection signal from the distributed spherical microphone array M in a time domain. A spherical harmonic spectrum vector calculation part 12 calculates a spherical harmonic spectrum vector having a spherical harmonic spectrum of a sound field radiated from a sound source S in an area and a spherical harmonic spectrum of a sound field due to an interference wave coming from the outside of the area as elements on the basis of a separation matrix and a time frequency spectrum of a sound collection signal.SELECTED DRAWING: Figure 3

The invention discloses a badminton flying strength detection method based on deep learning, and relates to the technical field of intelligent detection of kinematics, and the method comprises the steps: converting ball hitting window data containing speed information into four recurrence plots and short-time spectrograms, splicing the four recurrence plots and short-time spectrograms into an unchecked strength image tensor, carrying out the phase consistency and edge checking, and carrying out the detection of the strength image tensor; obtaining a force image tensor; and inputting the force image tensor into a convolution and attention combined deep learning network to perform feature extraction, fitting air resistance and effective quality parameters in combination with a speed change curve, and performing constraint in a micro physical layer according to a momentum conservation relationship to obtain a physically corrected badminton hitting force result and generate a detection report. According to the invention, visual representation and depth feature extraction of the multi-source motion signal are realized, the accuracy of ball hitting force detection is improved, and the physical consistency and stability of a force result are realized.

This invention discloses a time-frequency energy resource allocation system based on an intelligent reflective surface for an underground Internet of Things (IoT) architecture. Belonging to the field of resource allocation, this invention designs a frequency-division duplex IoT architecture for underground spaces by jointly using distributed energy access points and a Resource Allocation System (RIS). To achieve efficient utilization of channel and energy resources, based on this IoT architecture, this invention models the resource allocation problem as a joint allocation of time, spectrum, and energy to meet QoS requirements, along with RIS phase shift configuration. This problem is a mixed-integer nonlinear programming problem, difficult to solve in polynomial time. To reduce the complexity of the solution, this invention constructs a multi-agent collaborative resource allocation algorithm based on near-end policy optimization. Overall, the purpose of this invention is to build an efficient monitoring IoT in underground spaces, achieving an energy-saving and highly efficient resource allocation scheme.

Computing systems and methods for estimation and correction of audio pitch variations resulting from eccentric rotation in the playback of phonograph records are disclosed. The methods include simulation of phonograph playback to generate eccentric and non-eccentric time bases for correction. Estimation of eccentricity parameters is done by a parameter optimization through evaluation of a spectral sharpness detection function on time-spectral content of audio data. Candidate audio data are generated using phonograph simulation while conducting an optimal parameter search. The disclosed technology further provides systems and methods to correct audio using such parameters to correct the induced pitch variations from eccentric phonograph record playback in order to improve the pitch stability of audio recordings. In addition, the physical properties of groove radius and playback speed variations in phonograph playback may be estimated from the eccentric record playback.

The invention discloses an unmanned aerial vehicle countering signal identification method and a signal identification device, and belongs to the technical field of unmanned aerial vehicle countering signal identification, and the method comprises the following steps: obtaining a high-precision three-dimensional building and terrain three-dimensional model of a task area, collecting the historical spectrum data of an existing monitoring node of the area, and carrying out the collection of the historical spectrum data; constructing an electromagnetic environment baseline model of the region through a machine learning algorithm; in the grouping operation process of the unmanned aerial vehicles, continuously receiving and monitoring communication signal states between all the unmanned aerial vehicles and a control station, and comparing the real-time frequency spectrum with a pre-established baseline model; according to the method, abnormal signals are accurately recognized through the baseline model, high-precision positioning of the interference source is realized by means of three-dimensional modeling and reverse tracking, calculation is simplified through path comparison, the real-time performance is improved, the problems of high interference recognition misjudgment rate, inaccurate positioning and response lag in a complex environment can be effectively solved, and the operation safety and reliability of the unmanned aerial vehicle are guaranteed.

The invention discloses a five-axis numerical control engraving and milling machine machining regulation and control method and system based on machine vision, and relates to the technical field of engraving and milling machine machining regulation and control. The method comprises the steps that three-dimensional point cloud data of a workpiece are obtained and subjected to registration comparison with a preset workpiece theoretical CAD model, and the clamping position deviation and the posture deviation of the workpiece are calculated; a preset numerical control machining track is corrected, and a first compensation machining path is generated; real-time vibration signals of a machine tool machining area are collected in real time through a vibration sensor, real-time spectrum analysis is carried out, and vibration related signal data are generated; and calculating and generating a vibration suppression driving signal, and outputting the vibration suppression driving signal to a vibration suppression execution device for vibration suppression control. The technical problems that in the prior art, due to workpiece clamping deviation, a five-axis numerical control engraving and milling machine is poor in machining path adaptability, cutting vibration is difficult to restrain accurately, and then machining precision, efficiency and reliability are affected are solved, and the technical effect of improving the machining precision, efficiency and reliability of the five-axis numerical control engraving and milling machine is achieved.

The present application relates to the technical field of motor control, and particularly relates to a motor control method for an electric bed and the electric bed, which comprises the following steps: at the moment of starting the motor, sampling a current ripple signal, performing frequency spectrum analysis on the current ripple signal, extracting a load resonance frequency, generating an anti-phase harmonic current, and injecting the anti-phase harmonic current into a motor winding; at a motor pre-braking stage, injecting a damping harmonic into the motor winding, and at a motor main-braking stage, injecting a phase-synchronous reverse current into the motor winding. The present application realizes low-impact control of starting and stopping of the electric bed through an electromechanical energy hedging mechanism, and is compatible with the existing electromechanical architecture of the electric bed. At the moment of starting the motor, the anti-phase harmonic current is injected to neutralize high-frequency resonance exciting force. At the pre-braking stage, the damping harmonic is used to dissipate kinetic energy, and at the main-braking stage, the current is reversed to lock, so that the residual amplitude at the destination approaches zero. Based on real-time frequency spectrum analysis, control parameters are dynamically tuned to ensure that the starting and stopping time is relatively constant under varying loads, and the problems of overshoot under light load and delay under heavy load are solved.

The application discloses an anchor rod machine self-adaptive damping control method and system and a medium, and relates to the technical field of intelligent control. The method comprises the following steps: an interactive monitoring device is used to collect vibration signals and working condition signals of multiple parts of an anchor rod machine from a drill bit to an operating room; real-time frequency spectrum analysis is performed according to the vibration signals, vibration type identification is performed on the obtained frequency spectrum characteristics and working condition signals, and favorable vibration and unfavorable vibration are distinguished; self-adaptive control is performed according to the favorable vibration and the unfavorable vibration, wherein a generation maintenance or conduction instruction is generated for the favorable vibration, and a reverse damping instruction is generated for the unfavorable vibration; the vibration state of the anchor rod machine after the control instruction is executed is monitored, the vibration amplitude of the operating room and the mast resonance peak amplitude are used as main performance indexes, and the control parameters are self-adaptively adjusted and optimized. The technical problem of poor damping effect of the anchor rod machine in the prior art is solved, and the technical effect of improving the damping control effect of the anchor rod machine by identifying the vibration type and self-adaptively adjusting the control parameters is achieved.

This invention discloses a low-power FFT chip for real-time spectrum analysis of electroencephalogram (EEG) signals and its usage method. The chip includes an analog front-end module, a digital processing module, an interface and control module, and a low-power management and clock control module. The analog front-end module amplifies, filters, performs power frequency notch filtering, and performs analog-to-digital conversion on the input microvolt-level analog EEG signals. This invention integrates a low-noise analog front-end, a dedicated digital processing module, and a fine-grained power management module into a single chip, constructing a dual anti-interference link that combines analog and digital processing. While ensuring signal purity, the digital processing module accelerates real-time processing speed, and the overall power consumption is controlled through fully custom circuitry and dynamic power management. This solves the three major problems of poor real-time performance, high power consumption, and weak noise immunity in general-purpose processor solutions for EEG spectrum analysis, thus meeting the demanding requirements of wearable devices.

The application belongs to the technical field of power electronics, and discloses a harmonic suppression optimization method and device of a power quality treatment device; the method comprises the following steps: synchronously collecting three-phase signals of a target node of a power grid, and obtaining normalized signals through preprocessing; current power spectral density, fundamental wave parameters and a real-time frequency spectrum map are obtained through analysis; harmonic blind separation is performed on the normalized signals, and a characteristic vector is extracted from the separated signals; spectrum holes are searched based on the real-time frequency spectrum map, the interference cost is evaluated in combination with the characteristic vector, the optimal suppression strategy is generated according to the maximum interference cost; modulation voltages are generated according to the suppression strategy, and IGBTs are injected as switching signals to realize harmonic suppression; the application effectively breaks through the limitation of the prior art that such exogenous harmonics cannot be suppressed, significantly improves the adaptability and harmonic suppression accuracy of the device in a complex power grid environment, ensures that the total harmonic distortion rate of the power grid meets the national standard requirements, and guarantees the stability of power quality.

The application discloses a heat dissipation unit control method and electronic equipment, relates to the technical field of heat dissipation unit control, and comprises the following steps: acquiring acoustic signals and vibration signals of a heat dissipation unit in a server under multiple operating states, determining baseline pressure mark characteristics and resonance frequency spectrum of the heat dissipation unit, and establishing a heat dissipation acoustic archive; acquiring and analyzing real-time acoustic signals and real-time vibration signals of the heat dissipation unit to obtain real-time pressure mark characteristics and real-time frequency spectrum, and judging whether the operating state of the heat dissipation unit is abnormal; in response to abnormal operation of the heat dissipation unit, judging whether the heat dissipation unit has resonance risk; and in response to the existence of resonance risk, generating a fusion adjustment instruction based on a feature comparison result and a frequency spectrum comparison result. The application can realize real-time sensing of the operating state of the heat dissipation unit and pre-control, avoid resonance risk and abnormal state, and reduce the generation of noise, so as to solve the problems of noise control lag, dependence on hardware structure modification and inability to avoid resonance risk in advance in the related art.

The invention discloses an adaptive modulation identification method based on time-frequency double flow and domain adversarial learning, and belongs to the technical field of wireless communication, signal processing and artificial intelligence. The problems that an existing automatic modulation recognition method is insufficient in generalization ability during domain migration and multi-modal feature fusion and joint domain adaptation are difficult are solved. The method comprises the following steps: inputting IQ signals of a source domain and a target domain, generating a one-dimensional time domain sequence through an autocorrelation function, and generating a two-dimensional time frequency spectrum through short-time Fourier transform; constructing a time-frequency double-current neural network model, wherein the time-frequency double-current neural network model comprises a time domain encoder, a time-frequency domain encoder, a fusion decoder based on cross attention, a modulation type classification head and a data domain discrimination head; adversarial training is carried out through a mixed loss function of joint classification loss, multi-classification domain adversarial loss and cross-modal comparison loss and a gradient inversion layer; and after training, the removal domain discrimination head classifies a target domain signal. According to the invention, the precision, robustness and adaptive capability of modulation identification under domain offset are improved.

The application provides a fusion method and device of an audio signal, a storage medium and a terminal, and relates to the technical field of audio processing. The method comprises the following steps: determining a short-time frequency domain representation of audio signals of a plurality of devices; determining a frequency response of a time-varying linear prediction error filter according to the short-time frequency domain representation of the audio signals, and calculating a short-time frequency spectrum of a linear prediction error; extracting low-frequency coefficients from the short-time frequency spectrum and determining a linear prediction error envelope; performing amplitude compensation and nonlinear compression on the linear prediction error envelope, and extracting features; determining weights corresponding to the audio signals of each channel according to the variance of the nonlinear compressed signals, and fusing the audio signals of each channel according to the weights. The scheme can improve the quality of the fused audio signals and the intelligibility and clarity of the speech by determining the weights of the multi-channel audio in a strong reverberation environment.

The application discloses a speech enhancement method and device based on energy spectrum depth modulation and a medium, relates to the technical field of speech signal enhancement, and comprises the following steps: constructing a frequency-time joint enhancement network and training the same, inputting a continuous enhancement condition tensor into the trained frequency-time joint enhancement network, combining a local refinement branch to perform recursion, and outputting a complex enhancement mask sequence and a depth filter coefficient sequence; based on the depth filter coefficient sequence and the complex enhancement mask sequence, performing complex weighting and multi-tap complex filtering on a complex short-time frequency spectrum sequence to generate a candidate enhanced frequency spectrum sequence, and through buffer locking and residual suppression, generating a consistent enhanced frequency spectrum sequence; and performing waveform reconstruction and steady-state constraint on the consistent enhanced frequency spectrum sequence to generate a steady-state enhanced speech sequence. The application achieves the effect of giving consideration to the enhancement continuity and local detail fidelity.