197 results about "Dominant frequency" patented technology

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention discloses a multivariate monitoring data fusion method and system for a slope tunnel model test, particularly relates to the technical field of geotechnical engineering physical model test, and aims to solve the problem that dynamic stress waves in an existing model test integrating static and dynamic measurement cause static monitoring signal distortion and further influence the model state evaluation accuracy. Coupling interference in static signals is identified by synchronously collecting dynamic excitation and static response signals; obtaining signal samples before and after a model state sudden change event to perform frequency domain comparative analysis, identifying characteristic frequency components related to sudden change, and evaluating a coupling interference dominant frequency band; separating an effective static response component from the original static signal based on the frequency band; and finally, fusing the effective component with a soil dynamic parameter derived from dynamic excitation to generate comprehensive stability evaluation data. Dynamic interference is effectively suppressed, the authenticity of static signals is improved, and the reliability of model stability state evaluation is improved.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention relates to a cable joint resonance real-time diagnosis method and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the following steps: obtaining a high-frequency current signal collected by a high-frequency current sensor and an ultrasonic vibration signal collected by an ultrasonic sensor array; performing signal synchronization on the high-frequency current signal and the ultrasonic vibration signal, and extracting a high-frequency current feature and an ultrasonic array feature; calculating a dominant frequency synchronization index, a normalized phase stability index and a nonlinear coupling index based on the high-frequency current characteristics and the ultrasonic array characteristics, and performing weighted summation to determine a fusion confidence coefficient function; and judging whether resonance exists or not based on the fusion confidence coefficient function, and if so, carrying out resonance source positioning. Compared with the prior art, the method has the advantages of high precision, low false alarm and high adaptability, and is suitable for key power system scenes such as new energy grid connection, urban smart power grids and wind power.

The invention discloses a control method and device of a wind driven generator, a terminal and a storage medium, particularly relates to the technical field of operation control of the wind driven generator, and is used for solving the problem that a supporting structure bears violent asymmetric loads due to dynamic response differences of wind and wave load directions in a marine environment in an existing control method. Wind direction and wave direction data are synchronously obtained in real time, the wind direction change rate is calculated, supporting structure vibration signals are analyzed to recognize wave excitation dominant frequency components, and when it is detected that wave excitation energy suddenly changes and the wind direction change rate exceeds the limit, the wind wave direction difference is analyzed; meanwhile, a wave spectrum energy concentration frequency band is determined by analyzing the tower top movement track, the load asymmetry risk level is evaluated by combining the direction difference and the wave energy concentration frequency band, finally, the yaw speed and the variable pitch speed are adjusted in a self-adaptive mode according to the risk level, and accurate recognition and early warning of the wind wave load direction mismatching working condition are achieved. And the operation reliability of the wind driven generator in extreme weather is improved.

The invention discloses a new energy missing data completion method and system based on multi-model fusion. The method comprises the following steps: performing linear and nonlinear correlation two-stage feature screening on a collected new energy data set; normalizing the data set, constructing a missing data mask according to a missing proportion parameter, and dividing the data set; the designed AFMFormer model is constructed and trained; a self-adaptive frequency domain feature extraction module constructed in the model utilizes a data-driven dominant frequency extraction and frequency spectrum noise suppression mechanism to enhance the modeling capability of the model for a long-sequence dominant mode structure; the multi-granularity time sequence coding module realizes semantic consistent feature fusion through short-term features and long-term trends of a multi-scale modeling time sequence and through a learnable weight and an adaptive alignment mechanism, and generalization ability training of the model under a complex time sequence structure is further improved. Compared with a traditional method, the method shows higher precision, robustness and generalization ability in a high-fluctuation and high-missing-rate data scene.

The invention relates to the technical field of electrical sensor measurement, and provides a fault test method, system and equipment suitable for a fan converter, and the method comprises the steps: collecting current data in an IGBT module of the fan converter, and taking the current data as an original current signal; screening a plurality of dominant frequencies of the original current signal; decomposing the original current signal to obtain a plurality of component signals, and corresponding to each dominant frequency to obtain a plurality of dominant frequency signals; screening to obtain a reference signal; according to the variation trend difference between each component signal and the reference signal, the variation abnormity of each component signal is obtained; analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix to obtain the energy difference index of each component signal; obtaining an anomaly index of each component signal; and performing fault detection on the fan converter based on the anomaly index of each component signal. The invention aims to solve the problem that high-frequency electromagnetic noise affects signal performance of a current sampling loop and covers real fault features.

The invention discloses a sound source localization method based on multi-frequency separation and Newton optimization deconvolution, relates to the technical field of array acoustic signal processing, and is used for solving the problem that weak sound sources and multiple sound sources are difficult to identify. According to the method, dominant frequency is extracted through multichannel frequency domain analysis, a cross-spectrum matrix is constructed in combination with a near-field propagation model and a guide vector, delay summation beam forming is executed to obtain sound source preliminary distribution, then the distribution is regarded as a convolution result, a maximum likelihood model is introduced, and a two-stage deconvolution strategy of coarse estimation and Newton method fine optimization is adopted to obtain a high-resolution sound source. According to the multi-sound-source positioning method, subgrid-level analysis of sound source positions and amplitudes is achieved, finally, all frequency results are fused, continuous sound source images are smoothly output through a two-dimensional Gaussian kernel, the resolution and real-time performance of multi-sound-source positioning are remarkably improved, and the multi-sound-source positioning method is suitable for high-precision acoustic imaging in a complex sound field.

The invention relates to the technical field of digital signal processing, and particularly discloses a mixed precision low-resource digital control oscillator implementation method and system based on an FPGA (Field Programmable Gate Array), and the method comprises the steps: dividing a 32-bit phase accumulator into a high-bit 24-bit dominant frequency control section and a low-bit 8-bit dynamic compensation section, and carrying out the phase accumulation in parallel through double carry chains, the high-order section receives high 24 bits of a frequency tuning word FTW to control a dominant frequency, and the low-order section receives low 8 bits of the FTW and overlaps an overflow remainder to dynamically compensate for a phase error. According to the implementation method of the mixed precision low-resource digital control oscillator based on the FPGA disclosed by the embodiment of the invention, through collaborative innovation of mixed precision phase accumulation, hierarchical mixed storage, high-order interpolation reconstruction and dynamic phase compensation, the core problems of a traditional NCO in resource occupation, phase continuity, waveform precision and time sequence performance are systematically solved.

The invention discloses a transformer substation AC power supply residual current detection method and device, and the method comprises the steps: carrying out the vector synthesis of three-phase current data and zero line current data, forming a current difference sequence, exporting a residual current reference curve, and carrying out the fluctuation analysis of the residual current reference curve, and judging the leakage characteristics. After effective leakage features are formed through harmonic elimination, dominant frequency components are locked through frequency spectrum decomposition; calibrating a leakage type identifier according to the dominant frequency component and forming segmented leakage distribution; a deviation degree is formed by means of segmented leakage distribution and residual current reference curve deviation analysis, a correction deviation value is formed through load fluctuation compensation, and a standard exceeding coefficient is formed according to threshold value comparison to detect abnormal nodes to form hidden danger data; establishing a judgment rule based on the hidden danger data and the leakage type identifier to form a detection evaluation index; and executing path tracking on the detection evaluation indexes to form leakage position information, forming a disposal priority through intensity analysis to generate a residual current detection report, and realizing leakage current classification identification and self-adaptive evaluation.

The invention relates to the technical field of industrial automation, and discloses an energy consumption optimization control method and system for an automatic industrial production line. The method comprises the steps of collecting an equipment energy consumption gradient sequence through a fixed period and extracting a load demand, and identifying a high-load section exceeding a threshold value; performing frequency domain transformation on the segment to obtain a standard feature vector, extracting a dominant frequency, and mapping the dominant frequency into a discrete control instruction; if the concurrent change of multiple devices is detected, starting a distributed protocol to collect node feedback, constructing a power equation set to solve active, reactive and phase angle corrections, and generating a power supply adjustment parameter set; screening the subset corresponding to the high load to update the power distribution strategy, outputting an optimization control signal, and performing closed-loop iteration with an efficiency threshold, and finally forming a cooperative adjustment instruction sequence, thereby improving the power supply stability and energy-saving robustness of the industrial production line in a load sudden change scene.

The invention relates to a method for obtaining credible blasting vibration dominant frequency, which solves the problem of dominant frequency prediction under complex geology through EMD-HHT multi-scale decomposition and statistical confidence interval analysis and is difficult to adapt to dynamically changing construction conditions. EMD decomposition adaptively eliminates low-frequency noise caused by nonlinear response of the rock mass, and the signal-to-noise ratio is increased; a main frequency determination strategy is automatically switched through confidence interval width (RW) to adapt to complex geological conditions; 95% confidence coefficient ensures that a dominant frequency predicted value accords with actual energy distribution; compared with a traditional Samsu formula, the main frequency prediction error is reduced by more than or equal to 40%.

The invention discloses a battery mechanical damage monitoring method and system in the transportation process of an electric vehicle based on acoustic emission spectrum analysis, and the method comprises the steps: carrying out the multi-scale decomposition of an original signal sequence through wavelet transform, separating out a high-frequency transient component and low-frequency background noise, and obtaining a denoised elastic wave signal; calculating time domain features including peak amplitude and duration according to the denoised elastic wave signal, and combining frequency domain features such as a main frequency component to obtain a comprehensive feature vector; if the peak amplitude of the comprehensive feature vector exceeds a preset threshold value, judging that the event is a potential damage event, and extracting a damage related subset from the feature vector to obtain a damage candidate feature; training the damage candidate features through a support vector machine classifier to obtain damage type labels; and aiming at the damage type label fusion transportation environment data, a sliding window is adopted to analyze and track the signal change trend, and the damage evolution degree is determined. According to the invention, accurate identification, classification and dynamic monitoring of transportation damage of the battery pack are realized.

The invention discloses a partial discharge signal feature extraction method and system, and the method comprises the following steps: collecting a current signal on a grounding loop cable of detected electrical equipment, and carrying out the preprocessing of the current signal; converting the preprocessed signal into a two-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain a principal component; matrix reconstruction is carried out based on the principal component to generate a de-noising matrix, and the de-noising matrix is restored to a time domain signal sequence; performing discrete wavelet transform on the time domain signal sequence to obtain a multi-level approximation coefficient and a detail coefficient; the discrete wavelet transform selects a wavelet basis function from the candidate wavelet basis set based on a preset adaptive wavelet basis selection mechanism, and adaptively determines the number of wavelet decomposition layers based on the signal dominant frequency characteristics; and inputting the approximation coefficient and the detail coefficient into an inverse wavelet transform module to obtain a final partial discharge signal. According to the method, efficient, accurate and universal partial discharge signal feature extraction can be realized.

The invention provides a power distribution network line fault monitoring and early warning method, which comprises the following steps of: acquiring line vibration data through a sensor array, analyzing vibration frequency distribution by utilizing fast Fourier transform, extracting dominant frequency, comparing the dominant frequency with an inherent frequency database, judging a resonance risk, and further calculating an amplitude change trend by adopting a sliding window algorithm. And evaluating the fatigue accumulation degree by combining a material fatigue model. A Bayesian probability model is innovatively introduced, the influence of current load and wind action is fused, the resonance risk posterior probability is calculated, a support vector machine algorithm is adopted to classify and evaluate the risk, accurate early warning is achieved, finally, an early warning signal is transmitted through wireless communication, a vibration suppression control signal is generated to adjust a damping device, and the vibration suppression effect is achieved. And stable line operation is realized. According to the invention, accurate identification, real-time early warning and active suppression of the resonance risk of the power transmission line are realized, and the operation safety and reliability of a power grid are improved.

The invention belongs to the technical field of intelligent oil field and oil and gas yield prediction, and particularly discloses an oil well yield prediction method and system based on a frequency domain enhanced BiLSTM-Transformer hybrid model, and the method comprises the steps: collecting oil well historical production time sequence data, and carrying out the abnormal value elimination and normalization preprocessing; constructing a multi-scale feature system, and integrating a time domain lag term, a rolling statistic, and frequency domain dominant frequency and amplitude features extracted through Fourier transform; introducing an oil well embedding vector to explicitly compensate the inter-well static difference; the features are input into a BiLSTM-Transform hybrid model, a local dynamic state is captured by the BiLSTM, and a global trend is modeled by the Transform; an oil well level adaptive optimization strategy is adopted, sample imbalance is relieved through sample dynamic weighting and adaptive learning rate scheduling, and the generalization ability is improved in combination with gradient cutting and an early stop mechanism; and finally outputting an oil well yield prediction value. According to the method, the problem of prediction deviation caused by yield sequence non-stationarity, inter-well difference and sample imbalance is effectively solved, and the prediction precision is remarkably improved.

The invention relates to the technical field of power system state monitoring and fault diagnosis, and discloses a fault detection method in the operation state of power distribution equipment. Arranging current acquisition channels at a bus and a branch end, and acquiring a current sequence at a sampling frequency in a time period; a multi-scale primary function family based on sine and exponential attenuation envelope is constructed, attenuation parameters and center frequency are set according to scales and frequency bands and normalized, sliding window segmentation is combined, window subsequences are projected to a multi-scale primary function set, and an energy spectrum is calculated; in a stable stage, averaging energy spectrums to construct an energy reference, calculating an energy offset rate for a subsequent window and extracting a maximum offset index, forming a robust fault threshold by using a median and an absolute deviation thereof, positioning a fault time period and a dominant frequency band of an abnormal window, and outputting a fault channel and characteristics; therefore, misjudgment of sampling and fixed threshold values on transient feature recognition is reduced, and the fault detection accuracy is improved.

The invention discloses a settlement degree measuring method and system for bridge design, and belongs to the field of bridge engineering. Geological profile data in a bridge site range is collected, a settlement influence function is constructed based on geological parameters, and a bridge pier load matrix is extracted in combination with bridge design load working conditions; multiplying the settlement influence function by the load matrix to obtain a design settlement initial value of each pier; further calculating a settlement difference value between adjacent piers, constructing a bridge floor elevation deviation function, performing discrete Fourier transform on the bridge floor elevation deviation function, and extracting a dominant frequency characteristic value to judge whether a high-frequency settlement fluctuation behavior exists or not; if the dominant frequency exceeds a preset threshold value, marking the corresponding pier as a settlement sensitive area, and outputting a load adjustment parameter and a foundation elevation correction suggestion; the method can realize prospective identification and parameterized regulation and control of the non-uniform settlement trend in a bridge design stage, has the advantages of high structural adaptability, high prediction precision and the like, and is suitable for bridge foundation optimization design in a complex geological environment.

The invention discloses a grid distribution type massage air bag intelligent inflation and deflation method and system, and relates to the technical field of massage nursing, and the method comprises the following steps: constructing a space-time distribution matrix based on a bed surface air pressure fluctuation field in a mattress, converting the pressure change of each air bag in an inflation and deflation period into a continuous air pressure disturbance sequence, fourier transform is carried out on the barometric disturbance sequence to extract a dominant frequency component, and an air bag alternating frequency spectrogram is formed to serve as an input base line. Through frequency spectrum mapping and coupling measurement, dynamic interaction identification of air bag inflation and deflation and human body micro-vibration is achieved, early warning is conducted in advance, the resonance risk is reduced, and local stress fluctuation and bedsore occurrence are effectively reduced. Meanwhile, when the coupling coefficient is close to a set threshold value, a self-adaptive frequency deviation mechanism is triggered, the inflation and deflation rhythm is adjusted in real time, the sensitive frequency band is actively avoided while the pressure reduction and massage effects are guaranteed, and the safety, comfort and long-term stability of the nursing process are improved.

The invention discloses a bridge dynamic deformation monitoring method and system based on multi-mode joint calculation, and relates to the technical field of structural deformation monitoring, and the method comprises the steps: obtaining acceleration data and Beidou / GNSS data obtained through bridge response, and carrying out the parameter transformation; frequency domain parameter transformation and noise parameter solving are carried out on the time domain parameters obtained through parameter transformation; carrying out bridge structure real-time dominant frequency solution on the frequency domain parameters obtained through transformation; according to the frequency threshold value and the noise characteristics of the sensor, carrying out weight distribution on time domain parameters; and performing time domain parameter weight fusion according to a weight distribution result to obtain a bridge dynamic deformation monitoring result. According to the method, weight parameter fusion is carried out by considering the dominant frequency and noise which change in real time, the fusion strategy and parameters are dynamically and adaptively adjusted according to the signal quality, the carrier dynamic characteristics and the sensor error characteristics which change in real time, and the robustness and precision of the fusion system can be maintained under the complex deformation working condition of the bridge environment.

The present application relates to the technical field of frequency adaptive switching, and discloses a frequency adaptive switching method in a distributed wireless network, comprising: setting a target time period, a switching trigger holding time and an upper limit of false trigger probability, combining the center frequencies of a service frequency band and an adjacent frequency band, obtaining and calculating a cross-frequency band signal quality difference sequence and an intra-band standard deviation; then, according to the terminal position change and the average moving speed in the target time period, selecting a dominant frequency and calculating an effective Doppler frequency, establishing a monotonic relationship between the effective Doppler frequency and the intra-band standard deviation; finally, combining the holding time and the false trigger probability constraint, solving a dynamic hysteresis threshold to realize adaptive frequency switching. The method can flexibly adjust the switching parameters according to the dynamic changes of the user moving speed and the wireless environment, effectively reduce the risk of excessive switching and ping-pong switching, ensure that the false switching probability is within a controllable range, and thus significantly improve the accuracy, stability and resource utilization efficiency of network switching.

The application relates to the field of production and manufacturing supervision, and particularly relates to an industrial production and manufacturing intelligent supervision system based on the Internet of Things, which comprises a data collection module, a feature extraction module, a finite element simulation module and a generative adversarial network model, wherein the data collection module is used for collecting vibration information of equipment, the feature extraction module is used for obtaining a trajectory of a dominant frequency changing over time, the finite element simulation module is used for simulating a stress nephogram according to input energy distribution and the trajectory of the dominant frequency changing over time, and the generative adversarial network model is used for generating a crack distribution diagram, and the authenticity of an image is judged by comparing the crack distribution diagram with the simulated stress nephogram. The application can prepare in advance before equipment damage by collecting vibration information of equipment and performing frequency band analysis on the vibration information, so that economic losses caused by failure to handle in time when a fault occurs can be avoided.

The invention relates to movable self-excitation vibration detection equipment, and a method, a device and equipment for calculating the reliability of the vibration dominant frequency of an inhaul cable. The method comprises the following steps: acquiring the excitation time of steady-state excitation and the frequency range and the step length of step excitation; performing excitation on the inhaul cable according to the frequency range and the step length to obtain a vibration acceleration signal of the inhaul cable; analyzing and identifying the vibration acceleration signal to obtain a vibration dominant frequency of the inhaul cable; determining excitation frequency data according to the vibration main frequency and the step length, and performing excitation on the inhaul cable according to the excitation frequency data and the excitation time to obtain an excitation signal corresponding to each excitation frequency in the excitation frequency data; processing each excitation signal to obtain a corresponding steady-state vibration amplitude; and calculating according to all the steady-state vibration amplitudes to obtain the peak reliability of the vibration dominant frequency of the inhaul cable, thereby realizing the reliability calculation of the vibration dominant frequency of the inhaul cable. And the movable self-excitation vibration detection equipment moves to a preset installation position to obtain a vibration acceleration signal or an excitation vibration signal.

This invention provides a non-destructive testing method and apparatus for defects in ferromagnetic pipes, belonging to the field of pipe inspection. The method includes: acquiring a voltage signal, which is obtained by converting the capacitance change signal continuously collected by the bipolar capacitor as the ferromagnetic pipe and the upper and lower semi-circular bipolar capacitor move at a constant axial velocity relative to each other; extracting time-domain and frequency-domain features from the voltage signal; and determining the defect type and location based on the voltage signal and the extracted features. The extracted features include time-domain and frequency-domain features; the time-domain features include the signal standard deviation, coefficient of variation, and density of local extrema; and the frequency-domain features include the dominant frequency component and harmonic energy ratio. This method utilizes the physical property that the dielectric constant of a ferromagnetic pipe differs depending on whether it has defects or not, enabling the detection of defects deep within the component. It effectively avoids interference from various environmental electromagnetic noises, ensuring the accuracy of the detection results, and eliminates the need for point-by-point scanning, resulting in higher detection efficiency.

The invention discloses a water pumping compressed air energy storage efficiency optimization method, and aims to solve the problem that in the prior art, it is difficult to accurately predict and efficiently suppress aerodynamic instability of a compressor. The method comprises the steps that an original pressure signal and a velocity field slice of a compressor inlet flow field are obtained, and a dominant unstable mode set representing the flow field instability characteristic is generated through vortex-pressure coupling analysis; based on the dominant unstable mode set, a radial basis function neural network embedded with eddy current physical characteristics is adopted, a nonlinear relation between the flow deflector angle and eddy current induced impedance is constructed, and a three-dimensional control mapping table is generated; and according to the pressure pulsation dominant frequency monitored in real time, querying and solving are carried out in the three-dimensional control mapping table, and an optimal flow deflector angle instruction used for suppressing surge is generated. According to the method, the surge precursor can be accurately predicted and actively inhibited, the stable working range of the compressor is widened, and the overall operation efficiency and safety of the energy storage system are improved.

This invention discloses a method and system for monitoring mechanical damage to batteries during electric vehicle transportation based on acoustic emission spectrum analysis. The method includes: performing multi-scale decomposition of the original signal sequence using wavelet transform to separate high-frequency transient components and low-frequency background noise, obtaining a denoised elastic wave signal; calculating time-domain features, including peak amplitude and duration, based on the denoised elastic wave signal, and combining this with frequency-domain features such as the dominant frequency component to obtain a comprehensive feature vector; if the peak amplitude of the comprehensive feature vector exceeds a preset threshold, it is judged as a potential damage event, and damage-related subsets are extracted from the feature vector to obtain damage candidate features; training the damage candidate features using a support vector machine classifier to obtain damage type labels; and fusing transportation environment data with the damage type labels, using sliding window analysis to track signal change trends and determine the degree of damage evolution. This invention achieves accurate identification, classification, and dynamic monitoring of battery pack transportation damage.

The present application relates to the technical field of marine winch metering and control in marine operation equipment, and discloses a compensation method for dynamic error of marine winch metering. By synchronously sampling the angular displacement and the acceleration along the rope direction, the rope length sequence is converted to obtain the rope length vector and the acceleration vector in the window; the discrete angular frequency is set according to the sampling point and the period, the sine cosine base function is generated to form the harmonic orthogonal base, and the rope length sequence is projected and reconstructed to obtain the harmonic component of each frequency; the power spectrum analysis is performed on the acceleration sequence, the dominant frequency of vibration is identified according to the power spectrum, and the upper limit of high-frequency disturbance is determined, and the harmonic component is accumulated in the frequency not higher than the upper limit to obtain the low-frequency trend rope length sequence; the rope length is first reduced by the low-frequency trend value and then reduced by the high-frequency error component to form the error vector and the compensated rope length sequence, the double compensation of the winch metering error is realized, the rope length change and the vibration disturbance are separated under the time-frequency structure, and the metering precision and the real-time performance are improved.

This application relates to a wheel detection method, apparatus, computer equipment, and storage medium. The method includes: acquiring noise time-domain data of the target wheel at a preset acquisition frequency using a noise acquisition component corresponding to the target wheel; determining the dominant noise frequency data of the target noise time-domain data in a preset frequency band based on the target noise time-domain data, and determining the target wear order of the target wheel based on the dominant noise frequency data; determining the target sound pressure level density threshold corresponding to the target wear order based on the target wear order and a preset first correspondence between the wear order and a sound pressure level density threshold; and generating detection information indicating wheel abnormality if the dominant frequency sound pressure level density in the noise frequency data is greater than or equal to the target sound pressure level density threshold. This solution allows for real-time detection of the target wheel.

The present application relates to the technical field of slurry concentration detection, and particularly relates to a mine filling slurry concentration detection method, device and system. According to the time domain distribution of the frequency domain amplitude of each frequency in the spectrum diagram in the ultrasonic echo signal, the particle size of each frequency domain amplitude and whether the dominant frequency needs to be adjusted are obtained. If it is judged that the dominant frequency needs to be adjusted, the new dominant frequency of each frequency domain amplitude is obtained according to the dominant frequency and the particle size mismatch of the sound wave. According to the particle and the sound wave mismatch of each frequency domain amplitude, and the ultrasonic wave transmission distance, the new ultrasonic wave transmission distance of each frequency domain amplitude is obtained. The new ultrasonic echo signal is obtained. Until it is judged that the dominant frequency of each frequency domain amplitude does not need to be adjusted, the optimal dominant frequency of each frequency domain amplitude is obtained. The concentration of the filling slurry is detected. The present application improves the accuracy of the filling slurry concentration detection by analyzing the effective dominant frequency corresponding to the particle size corresponding to each frequency amplitude.

The present disclosure provides a method and an apparatus for assessing a physiological signal quality based on an IABP device, and relates to the technical field of medical instruments. The method includes: acquiring, in the operation process of an IABP device, physiological signal of a patient treated by the IABP device, where the physiological signal include an electrocardiographic signal and / or an arterial blood pressure signal; extracting signal parameter values of the physiological signal, where the signal parameter values include: a first kurtosis, a skewness, an effective signal ratio, a second kurtosis, and a dominant frequency; performing signal quality assessment on the physiological signal based on the signal parameter values; and implementing, based on the physiological signal, intra-aortic balloon counterpulsation in response to a signal quality assessment result represents that the signal quality of the physiological signal satisfies a preset signal quality requirement.