201 results about "Frequency spectrum analysis" patented technology

The invention discloses a cable joint health state assessment method and system based on multi-dimensional data fusion, and relates to the technical field of cable state assessment, and the method comprises the steps: collecting cable joint operation parameters, unifying a time sequence, and forming a two-dimensional matrix; performing spectral analysis on the operation parameters, and calculating energy characteristics of each sub-band by applying multi-stage wavelet decomposition; and constructing a Bayesian network structure, and calculating probability distribution according to the energy characteristics of the sub-bands. According to the method, spectral analysis is carried out through Fourier transform, the effect of identifying main frequency components and noise characteristics in signals is achieved, the effect of extracting energy characteristics of the signals in different frequency ranges is achieved by decomposing a two-dimensional matrix into a plurality of sub-bands by applying multi-stage wavelet decomposition, and the method has the advantages of being simple in structure and convenient to operate. By constructing a Bayesian network structure and defining feature nodes and state nodes, the health state evaluation of the cable can be dynamically updated according to data collected in real time.

The embodiment of the invention provides a mining lithium battery thermal runaway acoustic signal processing algorithm based on machine learning. The mining lithium battery thermal runaway acoustic signal processing algorithm comprises the steps of synchronously obtaining a target acoustic signal and a battery environment parameter of a mining lithium battery in a thermal runaway process; preprocessing the target acoustic signal to obtain a preprocessed acoustic signal; performing spectral analysis, time domain analysis and wavelet transformation on the preprocessed acoustic signals, extracting thermal runaway related features, and correspondingly obtaining thermal runaway related key frequency features, vibration features and local frequency changes of the mining lithium battery; fusing the key frequency characteristic, the vibration characteristic and the local frequency change related to the thermal runaway and the battery environment parameters to obtain a thermal runaway fusion characteristic vector; and based on the thermal runaway fusion feature vector, performing thermal runaway monitoring on the mining lithium battery to obtain a thermal runaway monitoring result, and when an abnormal signal is monitored, automatically sending out early warning information and starting corresponding safety measures by the system.

The invention discloses a wind driven generator oil leakage fault early warning method, which comprises the following steps: arranging vibration, temperature and oil sensors at a monitoring part, collecting multi-dimensional data for comprehensive analysis, analyzing a vibration signal by using a frequency spectrum to judge a mechanical fault, and combining a temperature trend and an oil parameter abnormal condition to realize early warning of an oil leakage fault of a wind driven generator. And carrying out classified early warning on the oil leakage fault by adopting a support vector machine algorithm. Furthermore, a Bayesian network algorithm is used to carry out accurate reasoning on the oil leakage position. According to the invention, the early warning result and the oil leakage position are uploaded to the monitoring center, and the processing suggestion is generated, so that intelligent and precise early warning of the oil leakage fault of the wind driven generator is realized. According to the multi-sensor fusion method, the accuracy and timeliness of oil leakage fault diagnosis are remarkably improved, the equipment loss and the maintenance cost are effectively reduced, and the method has important significance for guaranteeing safe and stable operation of the wind driven generator.

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 provides a sound quality-based ventilated sound insulation metamaterial design method, which comprises the following steps of: acquiring target noise for spectral analysis, and determining a main sound insulation frequency band of a metamaterial; determining the annoyance degree of a sound sample of the target noise, and calculating psychoacoustic parameters of the sound sample; analyzing the annoyance degree and the psychoacoustic parameters to obtain an annoyance degree correlation model; modeling the acoustic metamaterial structure and calculating transmission loss, and adjusting the sound insulation frequency band of the metamaterial to a specified target frequency band by adjusting the parameters of the acoustic metamaterial structure; the transmission coefficient of the acoustic metamaterial structure is calculated, psychological acoustic parameters are obtained according to the transmission coefficient, an annoyance degree correlation model is established, and the noise annoyance degree is obtained; and adjusting parameters based on the noise annoyance degree under different acoustic metamaterial structures to obtain the acoustic metamaterial structure with the optimal sound quality. According to the invention, the sound quality annoyance degree is used as the basis of the metamaterial design effect, so that the sound quality annoyance degree is greatly reduced under the limited size.

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 discloses an electromagnetic field observation data-based sea wave characteristic analysis method, which comprises the following steps of: processing electromagnetic field data by adopting a time-frequency spectrum analysis method, identifying distribution characteristics of electromagnetic field signals caused by sea waves at different time and frequencies through time-frequency decomposition, and extracting periods and time-varying characteristics of the sea waves; preprocessing the electromagnetic field data by using dominant frequency analysis and band-pass filtering technologies to obtain sea wave induced magnetic field time domain data; time domain and frequency domain induction electromagnetic field polarization direction analysis technologies are combined, and the movement direction of sea waves and the change characteristics of the sea waves along with time are inferred by measuring the change of the electromagnetic field polarization direction; the quantitative relation between the sea wave induction electromagnetic field intensity and the sea wave height is established, and the sea wave height change of the place is effectively estimated through the sea wave induction electromagnetic field intensity. By comprehensively analyzing the electromagnetic field data, the sea wave period, the time-varying characteristics, the motion direction and the sea wave height are evaluated, and a new way is provided for marine environment monitoring.

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.

RADAR-based occupancy detection and estimation is provided that is unobtrusive and does not introduce privacy issues. Time-frequency spectral analysis signal processing techniques are used for counting the number of people utilizing microwave Doppler RADAR. A RADAR-based hardware system determines the frequency at which highest amplitude occurs of a segmented portion of the signal obtained. The time-frequency spectral analysis on the signal is used to determine the number of occupants in a room.

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 discloses a self-adaptive bandwidth ultra-high-order harmonic detection method. The method comprises the following steps: S1, filtering: carrying out band-pass filtering on an original signal to obtain an ultra-high-order harmonic signal of 2-150kHz; s2, spectral analysis: performing spectral analysis by using 0.5 ms data to obtain frequency distribution of ultra-high-order harmonics; s3, adaptive filter detection: the initial center frequency of the adaptive filter is adjusted according to the frequency distribution of the ultra-high harmonic, and the adaptive filter tracks the frequency change and bandwidth change of the ultra-high harmonic; s4, ultra-high-order harmonic amplitude demodulation: the ultra-high-order harmonic amplitude is demodulated according to the amplitude output of the adaptive filter; and S5, ultra-high-order harmonic effective value calculation: aggregating the ultra-high-order harmonic amplitude demodulated in the step S4 into an effective value of a standard time length.

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 an intelligent sound box audio enhancement method based on a magnetic attraction position and environment characteristics. According to the method, the current magnetic adsorption position type and magnetic adsorption angle of the sound box are detected, and the surrounding environment characteristic data and environment noise data are acquired, so that sound effect optimization is realized. And determining material characteristics of the reflecting surface according to the type of the magnetic adsorption position, calculating a sound wave reflection path in combination with room structure characteristics, adjusting reverberation time and reflection gain of the audio signal, and generating a sound diffusion mode for space sound field reconstruction. Meanwhile, different frequency bands are compensated according to the distribution of the sound absorption materials, and a frequency response compensation curve is generated to enhance the sound effect of the attenuation frequency band. Calculating a sound wave radiation path according to the magnetic adsorption angle, and adjusting the phase difference and gain ratio of the sounding unit to realize a directional sound field; and the high-frequency gain and the propagation attenuation are dynamically adjusted in a darker light or high-humidity and low-temperature environment. In addition, a counteracting signal opposite to the noise is generated through spectral analysis and is superposed into an audio signal, so that an active noise reduction effect is achieved.