173 results about "Frequency spectrum analysis" patented technology

The invention relates to the field of cable monitoring, in particular to a high-voltage cable running state comprehensive monitoring method based on a TMR magnetic sensor. According to the method, firstly, a TMR magnetic sensor is adopted to collect sheath current and partial discharge pulse signals of a cable joint grounding wire, then amplification, filtering and AD change are carried out, Fourier transform is carried out, and low-frequency signals and high-frequency signals are separated out; carrying out frequency spectrum analysis on the high-frequency signal, preliminarily judging whether a discharge defect exists or not, carrying out Fourier inversion on the low-frequency signal, evaluating a grounding state and an insulation health state of a cable sheath, carrying out Fourier inversion on the high-frequency signal, and carrying out partial discharge fault type analysis in combination with a time domain partial discharge pulse signal and a power frequency current phase; and finally, comprehensively evaluating the operation state of the high-voltage cable by analyzing the grounding current of the sheath and the related parameters of partial discharge. According to the invention, the hardware cost is reduced, the system design and installation complexity is simplified, and the accuracy of partial discharge fault type identification is improved.

The invention discloses an unmanned aerial vehicle detection method based on multi-band radio spectrum analysis, and the method can recognize the flight control behaviors of a target, such as hovering, cruising or hovering, through the extraction of a signal track and the introduction of a behavior modeling mechanism, and effectively overcomes the defect that the prior art lacks the dynamic behavior analysis capability. A multi-node synchronization mechanism and a TDOA positioning method are combined, so that high-precision traceability of a target space position is realized; meanwhile, risk level assessment is carried out based on signal features, a track mode and a space region, so that the system has intelligent early warning capability. According to the whole scheme, a full-link unmanned aerial vehicle detection method from multi-frequency receiving, frequency spectrum identification, behavior judgment, target positioning to risk output is constructed, and the problems of single frequency band, rough identification means, lack of tracking and evaluation capability and the like in the prior art are effectively solved; and the practicability and the accuracy of the unmanned aerial vehicle detection system in a complex airspace environment are remarkably improved.

The invention relates to the technical field of sound quality enhancement, in particular to a sound quality improvement method and device of a Bluetooth loudspeaker box, equipment and a storage medium. The method comprises the following steps: collecting sound box environment acoustic signals, and carrying out peripheral audio time-frequency spectrum analysis and three-dimensional sound field fitting to construct a peripheral three-dimensional sound field model; based on the surrounding three-dimensional sound field model, environment noise source semantic deep analysis is carried out, and user real-time scene sound quality demand prediction is carried out, so that real-time scene sound quality demand features are generated; bluetooth sound box loudspeaker monitoring parameters are collected, and adaptive noise frequency suppression adjustment is carried out based on the surrounding three-dimensional sound field model rate, so that a loudspeaker dynamic noise suppression adjustment strategy is generated; and acquiring a Bluetooth audio output signal, and performing band frequency response characteristic optimization according to the real-time scene tone quality demand characteristics to obtain a frequency response tone quality optimized audio signal. According to changes of different environments and requirements, the output tone quality of the Bluetooth loudspeaker box is optimized in real time, and high-fidelity audio output is kept.

The invention provides a result judgment and use method of a portable borborygmus monitor, and relates to the technical field of medical monitoring equipment.The method comprises the steps that abdominal borborygmus is collected through a piezoelectric ceramic sensor array in multi-channel triangular distribution, and breathing and body movement interference is eliminated in combination with a dynamic noise filtering algorithm; extracting borborygmus outbreak duration, interval period and time-frequency energy indexes, and constructing an activeness and rhythm disorder two-parameter evaluation system; dynamically adapting a reference threshold value by adopting an age piecewise function, and introducing a feeding time compensation factor to optimize an individualized criterion; the intestinal obstruction risk is judged through a time sequence matching algorithm and a multi-parameter classification model dual-verification mechanism; a three-dimensional dynamic map is generated based on a sound source localization algorithm and spectral analysis, and data integrity is guaranteed in combination with a priority transmission protocol. According to the method, spatial-temporal feature analysis and an intelligent decision model are fused, sensor anti-interference, individual adaptability and pathological early warning precision are considered, and the core pain points of high misjudgment rate and poor clinical compatibility of traditional equipment are solved.

The invention discloses an intelligent temperature control mattress system based on multi-mode sensing and adaptive learning, and the system comprises a data collection module which is used for collecting a mattress surface vibration signal, a mattress surface temperature and a mattress surrounding environment parameter; the data analysis module is used for performing deep analysis on the time-frequency domain features according to a biological recognition model to obtain human body or non-human body classification, performing self-learning to obtain body movement changes, accurately recognizing various typical sleeping postures and filtering out error data; and the model prediction module is used for monitoring the physiological parameters of the user according to the spectral analysis, inputting the physiological parameters, the collected temperature and the parameters of the surrounding environment of the mattress into a temperature control model composed of three neural networks of LSTM, GNN and Transform for comprehensive analysis and decision making, and outputting an optimal temperature control strategy. According to heart rate changes or body heat fluctuations, the sleep stage and the sleep and waking time are intelligently recognized, and therefore sleep environment adjustment better conforming to the human body rhythm is achieved.

The invention discloses an intelligent control method and system for a motor driver, and the method comprises the steps: building an acoustoelectric incidence matrix through collecting harmonic current and sound pressure fluctuation data in the operation process of a motor, and forming a fault feature library; identifying an available fault mode to determine an energy collection point, and performing phase deviation processing on the collection point to generate an auxiliary driving signal; performing spectrum analysis on the sound pressure fluctuation data to obtain an acoustic characteristic spectrum, extracting an efficiency indication tone through characteristic decomposition, mapping the efficiency indication tone into a torque optimization parameter, and establishing a noise reduction control table; performing spectrum analysis on the enhanced control domain to identify a high-energy frequency band, executing a frequency shift operation to generate a safety frequency band signal, and forming a multi-frequency band management strategy in combination with a noise reduction control table; a management strategy is decomposed into parallel control flows for sequential staggered arrangement, and an energy-saving control matrix is generated; and generating a PWM control signal based on the matrix, carrying out edge reconstruction, and obtaining a softened edge waveform through an anti-resonance injection technology to complete intelligent control of the motor driver.

The invention belongs to the technical field of hydropower station monitoring and early warning, and particularly relates to a hydropower station intelligent inspection and fault early warning linkage method, which comprises the following steps: arranging a redundant sensor network at key equipment parts, synchronously acquiring pressure, flow, vibration and water turbidity data, establishing a flow-pressure loss reference relationship, and calculating real-time pressure-flow ratio deviation; performing spectral analysis on the vibration signals, extracting characteristic frequency band energy, performing collaborative study and judgment in combination with a turbidity change trend, and ensuring data credibility through data cross validation of main and auxiliary sensors; performing time sequence difference operation on the pressure-flow ratio deviation to obtain a change rate, fusing vibration-turbidity abnormal performance, and comprehensively diagnosing water diversion blockage or foreign matter invasion; early warning levels are divided according to the deviation and the change rate, corresponding response measures are triggered, response actions are executed according to a preset risk priority sequence, and linkage control of the whole process from state sensing, anomaly recognition, fault diagnosis to intelligent response is achieved.

The invention provides an iron tower voiceprint intelligent detection method and system, and belongs to the voiceprint detection technology. The method comprises the following steps: firstly, outputting a sweep frequency signal in a specific frequency range, collecting iron tower vibration response data, extracting candidate frequency through spectral analysis, and dynamically adjusting excitation frequency by using a gradient descent algorithm; hardware filtering is carried out on collected sound signals, frequency band signals related to excitation frequency are reserved, and space beam forming is carried out through a microphone array to enhance iron tower voiceprint signals. Separating iron tower vibration components from the mixed signals by utilizing independent component analysis, performing time-frequency analysis, extracting an energy ratio of a specified frequency band by adopting wavelet packet transformation, and performing deep learning processing in combination with a one-dimensional convolutional neural network to generate an energy ratio and zero-crossing rate feature vector; and finally, inputting the feature vectors into a support vector data description model, setting an initial threshold value and dynamically adjusting the initial threshold value, thereby realizing graded judgment of bolt looseness and improving detection efficiency and stability.

The invention discloses a voiceprint monitoring method, system, medium and equipment for rotation stall of an aero-engine compressor, and the method comprises the steps: determining the layout of a microphone array through the model parameters of the compressor of the aero-engine, and carrying out the synchronous collection of sound pressure time domain signals of multiple channels of the compressor of the aero-engine through the microphone array; arranging the multi-channel sound pressure time-domain signals in sequence to construct a time-domain signal matrix; performing spectral analysis on the sound pressure time-domain signals by adopting a fast Fourier transform algorithm to obtain a spectrogram of the sound pressure time-domain signals of each channel, monitoring whether an abnormal single-tone frequency peak value lower than one time of rotation frequency appears in the spectrogram or not, obtaining an acoustic mode spectrogram of the abnormal single-tone frequency peak value by adopting a single-frequency acoustic mode decomposition method, and obtaining an acoustic mode spectrum of the abnormal single-tone frequency peak value; and whether the rotating stall characteristic of the gas compressor occurs in the acoustic mode spectrogram is monitored to judge whether the rotating stall characteristic of the gas compressor occurs or not.

The invention discloses a double-earphone sound effect adjusting method and system, a storage medium and a program product, and relates to the field of loudspeakers, and the method comprises the steps: collecting an environment audio signal through a binaural microphone, and executing spectrum analysis to obtain a frequency distribution characteristic of the environment audio signal; based on a preset human voice frequency range, extracting an audio signal conforming to the human voice feature from the frequency distribution feature as a target audio; calculating the time difference and the intensity difference of the target audio between the left ear and the right ear to obtain the human sound source direction and the relative distance in the environment; acquiring head attitude sensor data, and calculating a matching included angle between the head orientation of the user and the human sound source direction based on the head attitude sensor data; when the matching included angle is smaller than a preset threshold value, increasing the audio signal gain of the head facing the direction; and when the conversation voice of the user is detected, the audio signal gain is kept unchanged. By implementing the application, the interaction efficiency when the double earphones are used can be improved.

The invention provides a motor multi-band vibration suppression method and a motor multi-band vibration suppression device based on electromagnetic-mechanical cooperative regulation and control. According to the method, vibration signals are collected in real time, and spectral analysis is carried out to identify vibration frequency distribution; dividing the vibration signal into a low-frequency band, a middle-frequency band and a high-frequency band according to a spectral analysis result, and respectively corresponding to a mechanical vibration source, an electromagnetic excitation source and a structural noise source; a control strategy is dynamically selected based on a vibration frequency band, a low-frequency band triggers a mechanical regulation and control module to output a reverse force to perform vibration suppression, a middle-frequency band triggers an electromagnetic control module to optimize a current waveform and a PWM modulation mode, and a high-frequency band starts a cooperative control strategy of the electromagnetic and mechanical control module; through real-time communication between the controller and each module, vibration feedback signals are continuously monitored, and control parameters are dynamically adjusted according to working condition changes so as to maintain the vibration suppression effect. According to the invention, through cooperative adjustment of electromagnetic control and a mechanical structure, active suppression of vibration of the motor in multiple frequency bands is realized.

The invention relates to the field of charging pile use, and discloses a charging pile use frequency analysis method based on charging frequency data, which comprises the following steps: acquiring charging frequency original data of a plurality of charging piles at different time periods and different dates, performing structured processing in combination with a time label, and constructing a charging behavior data set containing a time dimension label; introducing a sliding window algorithm and a periodic spectrum analysis method, and performing frequency feature extraction on the charging behavior data set; using a density peak clustering algorithm to classify the use frequency matrixes of different charging piles under the same time scale, and identifying use preferences and active features of the charging piles under various typical time scenes; based on a lightweight time sequence neural network structure, a frequency fluctuation sensing mechanism is introduced; and predicting the use condition of the charging pile in a future time period based on the trained frequency model in combination with regression prediction and an anomaly detection algorithm. The method has the advantage of improving operation scheduling.

The invention relates to the technical field of concrete detection, in particular to a concrete protective layer void detection method and system based on a detachable piezoelectric ceramic sensor and an electromechanical impedance resonance technology. The detection method comprises the following steps: temporarily fixing a detachable piezoelectric ceramic sensor on a detection point on the surface of a concrete structure; applying an alternating-current sweep-frequency voltage to excite the piezoelectric ceramic sensor, and obtaining an electromechanical impedance signal frequency spectrum; the electromechanical impedance signal frequency spectrum is analyzed, the frequency of a local defect formant is obtained, and according to the frequency of the local defect formant, geometric parameters of a void area are estimated through a defect size and frequency relation model; and moving the sensor and repeatedly detecting other detection points. The detection method has high efficiency and flexibility, and detection can be realized in any environment; the single-point detection speed is high, the efficiency is high, baseline data is not needed for detection, the anti-interference performance is high, and rapid positioning and quantitative evaluation of void damage can be achieved.

The invention provides a method and system for diagnosing high-frequency magnetic disturbance in a quasi-ring symmetrical star simulator, and relates to the technical field of star simulators, and the method comprises the steps: designing the three-dimensional size of a magnetic probe body in combination with a physical research target and a vacuum chamber space limiting condition, and preparing a magnetic probe entity adaptive to an experimental environment; designing a magnetic probe array space layout according to the triangular magnetic configuration characteristics of the CFQS star simulator to obtain a positioned magnetic probe array; a positioned magnetic probe array is utilized to construct a signal transmission link, and a calibration qualification system is formed; generating a multi-channel time domain voltage signal set according to a calibration qualified system; and performing frequency response correction on the multi-channel time domain voltage signal set in combination with a transfer function database, analyzing a disturbance mode structure and a propagation direction through time-frequency spectrum analysis and array phase difference, and finally outputting three-dimensional magnetic disturbance evolution characteristics. The three-dimensional high-frequency magnetic disturbance measuring device has the beneficial effects that the three-dimensional high-frequency magnetic disturbance measuring capability is realized, multi-channel high-fidelity signal acquisition can be realized, and the experimental data quality and the physical analysis capability are improved.

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

The invention discloses a coal mine tunneling roadway advanced geological exploration and ground stress inversion method, particularly relates to the technical field of seismic signal processing in geophysical exploration, and is used for solving the problems of geological exploration signal distortion and insufficient stress inversion precision caused by strong noise interference of tunneling equipment in the prior art. Rock mass vibration signals are collected through an optical fiber sensor arranged on a roadway wall, equipment noise characteristics are identified through spectral analysis, a quantitative mapping relation between working condition parameters and noise characteristics is established, a noise reference time period is determined according to the quantitative mapping relation, a spatial coherence characteristic template is constructed, high-coherence noise components are inhibited through template comparison, and the noise reference time period is determined according to the spatial coherence characteristic template. Finally, the geological structure and stress field distribution in front of the working face are inverted based on the purified seismic wave signals, a stress concentration area and a disaster risk area are identified, the signal-to-noise ratio of the seismic signals in the strong noise environment and the reliability of the geological inversion result are effectively improved, and accurate technical support is provided for safe mining of a coal mine.

The invention discloses an audio hash generation method based on deep learning. The method comprises the following steps: S1, performing spectral analysis on an input audio signal and extracting a frequency cepstrum coefficient; s2, performing principal component analysis dimensionality reduction on the audio feature vector obtained in the above step; s3, weighting each fragment on the spectrum energy dimension through a space attention mechanism to optimize feature expression; s4, aligning the audio features with different lengths with the full connection layer through the dynamic pooling layer; and S5, according to the feature vector after dimension reduction, generating a final audio hash value through a size relationship of adjacent elements. According to the method disclosed by the invention, the method has remarkable robustness on common interferences such as time offset, frequency change and noise increase of the audio, the stability and the accuracy of audio hash are ensured while efficient storage is ensured, and the method is suitable for retrieval and copyright protection of a large-scale audio database.

The invention discloses a gas-solid-sound coupled gas compressor acoustic resonance monitoring method and system, a medium and equipment, and the method comprises the steps: determining the installation number and angle of strain gauges based on a blade modal analysis result, or determining the installation number and position of blade end timing sensors according to the number of rotor and stator blades, and determining the installation position of a pressure probe; carrying out a compressor surge test, and synchronously collecting a sound field signal, a vibration signal and a pulsating pressure signal; frequency domain information of the sound field signal, the vibration signal and the pulsating pressure signal is obtained through spectral analysis; carrying out correlation analysis on the sound and vibration signals, and judging whether abnormal asynchronous peak values appear in the sound pressure and vibration signals or not; carrying out acoustic mode decomposition at the abnormal asynchronous peak frequency of the sound field signal, and judging whether the circumferential mode order is coupled with the vibration pitch diameter and the pressure mode or not; the method comprises the following steps: performing continuous broadband acoustic mode decomposition on an acoustic pressure signal to obtain a spectrogram, monitoring whether the spectrogram is matched with an acoustic resonance characteristic or not, and judging whether the compressor has an acoustic resonance fault or not.

The invention provides a multi-modal fusion unmanned aerial vehicle identification method and system, and the method comprises the steps: obtaining a radio signal, an image signal and a sound signal of an unmanned aerial vehicle; performing feature extraction on the radio signal based on a blind source separation algorithm and adaptive filtering to obtain radio features; performing feature extraction on the image signal based on image super-resolution reconstruction and a double-flow feature extraction network to obtain image features; performing feature extraction on the sound signal based on spectral analysis and harmonic feature extraction to obtain an audio feature; fusing the radio feature, the image feature and the audio feature to form a multi-modal fusion feature; and performing identification based on the multi-modal fusion features to obtain an unmanned aerial vehicle category identification result. By adopting the scheme, the accuracy, reliability and environmental adaptability of unmanned aerial vehicle identification can be remarkably improved.

The invention provides a die cutting equipment error optimization method and system based on group collaboration, and relates to the technical field of die cutting machines, and the method comprises the steps: carrying out the spectrum analysis of a frequency characteristic curve of each piece of die cutting equipment, so as to divide an initial interference group; establishing a virtual spring damping network in the initial interference group, regarding each die cutting device in the initial interference group as a mass point, and determining a spring stiffness coefficient and a damping coefficient according to an actual distance between adjacent mass points; determining vibration mode characteristics of each die cutting device according to the spring stiffness coefficient and the damping coefficient, and converting a control signal generated according to the vibration mode characteristics into a parameter adjusting variable by using a preset control algorithm; and determining a vibration coupling relationship according to the collected vibration energy data of the initial interference group after the initial processing parameters are adjusted according to the parameter adjusting quantity, and determining an optimal production takt and a parameter compensation value according to the vibration coupling relationship. The technical problem that in the prior art, due to vibration coupling of a die cutting equipment group, the machining error cooperative control difficulty is large is solved.

The invention relates to the field of electrical variable measurement, in particular to an insulating layer compactness detection method for a mineral cable, which comprises the following steps: firstly, acquiring a time domain reflection signal of the cable, and carrying out denoising pretreatment by utilizing synchronous average and wavelet transform; and then, carrying out spectrum analysis on the discrete signal segments, calculating a spectrum centroid offset rate to determine high-frequency energy attenuation, and constructing a dynamic compensation gain coefficient according to the high-frequency energy attenuation. And then, improving a traditional layer stripping algorithm by using the gain coefficient, eliminating calculation deviation caused by transmission loss, and accurately reconstructing characteristic impedance distribution along the cable. And finally, by comparing the deviation between the reconstructed impedance and the nominal impedance, positioning and judging the abnormities of the compactness of the insulating layer of the cable and the eccentricity defect of the cable core are realized.

The invention relates to an SVPWM (Space Vector Pulse Width Modulation) optimization method for charging pile test equipment and related equipment, and the method comprises the following steps: carrying out the real-time collection of electrical parameters of target charging pile test equipment, and obtaining working state parameters; performing spectral analysis on the working state parameters through a preset fast Fourier transform algorithm to obtain a frequency component distribution diagram; judging whether a harmonic component in a specific frequency range exists in the frequency component distribution diagram or not; if the frequency component distribution diagram does not exist, SVPWM mapping is carried out on the frequency component distribution diagram to obtain a space vector distribution diagram, and coding and modulation optimization are carried out on sector positions and switch states in the space vector distribution diagram to obtain a PWM control instruction sequence; according to the PWM control instruction sequence, an inverter circuit of the target charging pile test equipment is controlled, and output electric energy quality in a specific frequency range is obtained; the technical problem that the quality of output electric energy is reduced due to the fact that a traditional PWM modulation method is difficult to effectively suppress harmonic components is solved.

The invention provides a pressure sensor real-time fault detection method based on dynamic environment adaptation, and belongs to the technical field of sensor fault detection. The method comprises the following steps: establishing a time domain association data set of noise-sensor signals by synchronously collecting environmental noise signals and pressure sensor output signals in real time; performing spectral analysis on the environmental noise signal, dynamically dividing frequency bands, and calculating energy distribution of each frequency band; dynamically generating an operation delay threshold according to the energy distribution and the weight factor of each frequency band; calculating the reliability coefficient of the operation state of the sensor by combining the time domain characteristics of the output signal of the pressure sensor and the current noise energy distribution; when it is detected that the output signal of the pressure sensor is abnormal, the abnormal duration is corrected based on the operation state credibility coefficient, the corrected duration is compared with the dynamic operation delay threshold value, and whether the fault is a real fault or not is judged. According to the invention, environmental noise interference can be effectively suppressed, and the accuracy and reliability of pressure sensor fault detection are improved.

The invention relates to a false fire point data identification method and device, equipment and a storage medium. The method comprises the following steps: acquiring target fire point monitoring data of a target area within a preset historical duration; performing spatial clustering on multiple pieces of fire point data in the target fire point monitoring data based on a preset clustering mode to obtain multiple clustering objects of the target fire point monitoring data; performing time spectrum analysis on each clustering object to obtain power spectrum density distribution of each clustering object; based on the power spectral density distribution of the clustering objects, identifying fire point types of the clustering objects, and determining the clustering objects of which the fire point types are false fire points as target clustering objects; and determining the fire point data in the target clustering object as the false fire point data of the target area in the preset historical duration, so that the false fire point data in the fire point monitoring data can be quickly identified by effectively utilizing the spatial distribution rule and the time spectrum characteristics of the historical fire point data. The identification efficiency and the identification accuracy of the false fire point data can be improved.

The invention discloses a compressor multi-working-condition feature extraction method based on valve movement acoustic emission, and belongs to the technical field of compressors, and the method comprises the following steps: S1, synchronously collecting compressor valve acoustic emission signals and piston displacement data by using an acoustic emission sensor and a rotating speed encoder, and constructing a time alignment joint signal sequence; s2, calculating a real-time phase according to a piston displacement crankshaft angle, enveloping an energy change rate by combining an acoustic emission signal, and dividing four stages of a compression cycle; s3, extracting the multi-dimensional time-frequency characteristics of the acoustic emission signals in each stage; and S4, inputting the multi-dimensional features into a pre-training auto-encoder, and generating low-dimensional fusion feature vectors to represent operation features. According to the compressor multi-working-condition feature extraction method based on valve movement acoustic emission, acoustic emission and displacement combined modeling is adopted to improve the working condition sensing precision, multi-dimensional features are extracted by means of wavelet packet transformation and spectral analysis fusion to strengthen micro working condition recognition, and the method adapts to multiple typical working conditions and is good in robustness and adaptability.

The invention discloses a high-precision alternating current and direct current mixed leakage current protection circuit and method based on a fluxgate. The protection circuit is composed of an FPGA and a peripheral circuit thereof, a power supply and a conversion circuit thereof, a communication circuit, an electric leakage sensing and measuring circuit, and an AD conversion and sampling conditioning circuit. According to the invention, the measurement precision and accuracy are effectively improved through signal compensation calculation; the timeliness and effectiveness of protection are enhanced through mutual cooperation of two protection modes of an effective value and an instantaneous value; accurate instantaneous value protection is carried out by using the inverse time limit characteristic of the leakage current; classification threshold protection is carried out through spectral analysis and waveform identification, so that the probability of false operation and missing operation is effectively reduced; fFT transformation is adopted to convert to a frequency domain for discrimination, and an improved formula is utilized to calculate a frequency domain output variance to further refine and distinguish waveforms. According to the invention, high-precision measurement of the leakage current can be realized, complex waveforms can be effectively handled, and rapid and reliable protection of electrical equipment can be realized.

The invention provides a rapid positioning method and system for excitation system power cabinet draught fan faults, and the method comprises the steps: collecting the operation parameters of an excitation system power cabinet draught fan in real time in a preset time period, and the operation parameters comprise the draught fan rotating speed, the bearing temperature, the vibration frequency, the current value and the voltage value; analyzing the operation parameters by using a threshold value judgment method and a trend analysis method, and judging whether the excitation system power cabinet fan has a fault or not; when the excitation system power cabinet draught fan breaks down, sound signals of the draught fan are collected, spectral analysis is conducted on the sound signals of the draught fan, and a spectral analysis result of the draught fan is obtained; and determining the fault type and the fault position of the fan based on the spectral analysis result of the fan. According to the technical scheme provided by the invention, the fan fault monitoring precision and efficiency are improved.

The invention relates to an intelligent sound field calibration method and system, and the method comprises the steps: transmitting sweep frequency signals in a preset frequency range to a plurality of microphone collection positions in a sound field, collecting echo signals through each microphone, and constructing an acoustic feature database based on the echo signals; extracting a relative position relationship between microphones and corresponding echo signal features from the acoustic feature database, constructing a sound field spatial topological graph and dividing acoustic partitions based on the relative position relationship and the echo signal features, and determining response weights of the partitions; based on the acoustic characteristic database, a frequency spectrum analysis algorithm is adopted to identify and mark howling frequency points, and a preliminary frequency response curve is generated in combination with the howling frequency points and the response weights of the acoustic partitions; adjusting the initial frequency response curve according to historical preference data of the user to generate a target frequency response curve; and applying the target frequency response curve to the audio output path to output the audio signal. The method has the effect of improving the accuracy of sound field calibration.

The invention discloses a fault detection method and system for a permanent magnet synchronous motor, and relates to the technical field of permanent magnet synchronous motor fault detection.The method comprises the steps that stator three-phase current during operation of the permanent magnet synchronous motor is collected in real time, and zero-sequence current is calculated; carrying out multi-band frequency spectrum analysis based on the zero sequence current, respectively detecting a third harmonic amplitude and a sixth harmonic amplitude by adopting a parallel dual-channel FFT (Fast Fourier Transform) processor, and calculating a harmonic amplitude ratio; correcting the third harmonic amplitude and the sixth harmonic amplitude through a preset compensation coefficient matrix, and recalculating the corrected harmonic amplitude ratio; and performing fault classification on the corrected harmonic amplitude ratio based on a preset threshold value in combination with a time accumulation counter, and outputting a final fault code after continuous three times of consistent detection, thereby completing fault detection of the permanent magnet synchronous motor. The fault type can be rapidly positioned, the fault diagnosis precision is improved, it is ensured that an alarm is given after real faults are accumulated to the confirmable level, and the diagnosis reliability and stability are improved.

The invention belongs to the technical field of automobile collision safety, and particularly relates to a collision test dummy biological fidelity evaluation method and device based on frequency spectrum energy and a medium. Firstly, dummy collision response data and collision response data, stored in a database, of different body parts of a human body are collected; a preset spectral analysis method is called to analyze the collected dummy collision response data and the collision response data of different body parts of the human body, and spectral energy of collision response of the dummy and the different body parts of the human body is obtained through calculation; calculating the biological fidelity of the dummy based on the difference of the frequency spectrum energy of the collision response of the dummy and different parts of the human body; and finally, evaluating the dummy biological fidelity obtained by calculation according to a preset evaluation standard, and outputting a dummy biological fidelity grade. According to the method, the dynamic response characteristics of the dummy in different frequency spectrum energy ranges can be deeply analyzed, so that the biological fidelity of the dummy is accurately measured, and the simulation precision of the dummy to the real response of a human body is improved.