273 results about "Frequency resolution" patented technology

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention relates to the technical field of fault diagnosis, in particular to a wind driven generator fault diagnosis method and system based on Mamba-ResNet. The method comprises the following steps: carrying out feature extraction and feature fusion by utilizing preprocessed data, namely constructing adaptive window short-time Fourier transform (AW-STFT) to carry out dynamic time-frequency resolution analysis, carrying out parallel feature extraction and constructing a multi-dimensional heterogeneous feature vector, and carrying out a cross-modal adaptive gating fusion mechanism based on a bidirectional cross gating unit; the method comprises the following steps: constructing a Mamba-ResNet hybrid deep network model architecture; performing model training on the constructed network model architecture; and performing fault diagnosis on the wind driven generator by using the trained model architecture. A tedious manual feature design process in a traditional method is avoided, and the automation level and adaptability of a diagnosis system are remarkably improved.

The invention discloses a power distribution network three-core cable fault distance measurement analysis method and system, and relates to the technical field of fault distance measurement, and the key points of the technical scheme are that the method comprises the steps: carrying out the empirical mode decomposition of three-phase voltage and current signals; generating a dynamic correction wave velocity based on the cable type, the environment temperature and the soil humidity parameter; determining the first arrival time of the wave head of the initial traveling wave according to the instantaneous frequency abrupt change point of the alpha-mode component, screening the second arrival time of the first reflected wave by using the amplitude gradient of the zero-mode component, and calculating the arrival time difference; and calculating an initial fault distance, and calling the impedance matrix to iteratively correct the initial fault distance. According to the method, the dynamic wave velocity is coupled with multiple factors, the limitation of single parameter correction is broken through, and the problem of transient drift of the wave velocity is solved; and the STFT high-frequency resolution and the wavelet low-frequency stability are fused, and the ridge continuity is optimized in combination with dynamic programming, so that the traveling wave head detection precision can be improved.

The invention discloses a wellhead data acquisition modeling system. The wellhead data acquisition modeling system comprises a data acquisition module, a hydraulic characteristic processing module, a well layer coupling modeling module and an interaction control module, the invention belongs to the technical field of well water monitoring management, based on an adaptive time-frequency analysis method, a dynamic window function enhanced by local frequency spectrum entropy is introduced, feature extraction and abnormal early warning are performed on transient hydraulic fluctuation signals, and the time-frequency resolution and change recognition capability of the hydraulic signals are remarkably improved; a dynamic coupling model between a water pumping system and an aquifer is constructed by combining a multi-scale parameter coordination mechanism, and accuracy and dynamic adaptability of well layer response modeling are realized by introducing a gap compression lag function and a joint parameter calibration method; and the interaction control module implements model prediction control according to the hydraulic feature vector, the hydraulic anomaly early warning signal and the well layer coupling prediction parameter combination, and outputs wellhead data acquisition decision support data.

A method and apparatus for acquiring aerodynamic noise of a compressor, a medium, and a product are provided. The method includes: performing inverse Fourier transform on a known frequency-domain noise spectrum to obtain known time-domain noise data, then predicting unknown time-domain noise using a time series neural network, obtaining finer time-frequency noise data in combination with the known time-frequency noise data, and finally, performing Fourier transform on the finer time-frequency noise data to obtain new frequency-domain noise data. Limitations of a limited time step and a total simulation time on the acquisition of aerodynamic noise data in traditional numerical calculation of aerodynamic noise can be overcome. Finer aerodynamic noise data can be acquired rapidly and accurately. The frequency resolution of the noise spectrum can be increased, and reducing the consumption of computing resources and saving manpower and material resources can be achieved.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

In order to solve the problem of inaccurate classification caused by fixed time-frequency resolution and lack of physical rationality in sample enhancement in a traditional classification method, the invention relates to a partial discharge signal classification method based on a physical constraint generative adversarial network, and the method comprises the following steps: extracting a partial discharge signal and a historical partial discharge signal, and carrying out the double-window time-frequency coding of the partial discharge signal and the historical partial discharge signal; correspondingly obtaining a fused time-frequency graph and a historical fused time-frequency graph; determining a corresponding discharge type label based on the historical partial discharge signal, establishing a physical constraint generative adversarial network, and training the physical constraint generative adversarial network in the historical fusion time-frequency graph based on the discharge type label; and inputting the fusion time-frequency graph into the trained physical constraint generative adversarial network to obtain a fusion feature graph corresponding to the fusion time-frequency graph, performing time sequence modeling on the fusion feature graph, mapping the fusion feature graph into a time sequence feature vector, and performing classification on the corresponding partial discharge signals based on the time sequence feature vector.

The invention provides an adaptive window direct sound extraction method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting an input audio signal, and carrying out the preprocessing of the audio signal; a self-adaptive window function is constructed based on the frequency difference of the audio signals, and the window length and the window coefficient of the self-adaptive window function can be automatically adjusted according to different frequency bands; the method comprises the following steps: converting an audio signal from a time domain to a frequency domain for representation through an adaptive window function, obtaining complete frequency domain data of the audio signal, using a longer window for a low-frequency signal by the adaptive window function so as to ensure the frequency resolution of a frequency domain signal, and using a shorter window for a high-frequency signal so as to improve the time precision of the frequency domain signal; and establishing a direct sound extraction tool according to the complete frequency domain data of the audio signal, and separating and extracting a direct sound signal from the audio signal. According to the method, the special window functions are designed for the signals with different frequencies, so that more details are reserved for the extracted direct sound, and the signal analysis precision is remarkably improved.

The invention discloses a method for selecting and triggering gamma instant radiation pulse events, and belongs to the technical field of nuclear radiation detection. The method comprises the following steps: carrying out normalization preprocessing on an original gamma pulse radiation time spectrum; s time-frequency transformation is carried out on the preprocessed data, and time-frequency features are extracted; optimizing parameters of the frequency threshold and the amplitude threshold after S time-frequency transformation by adopting a deep reinforcement learning model to obtain an optimal frequency threshold and an optimal amplitude threshold; performing filtering processing on the signal of the S time-frequency conversion result, and separating noise from an effective signal; and searching a pulse radiation event for the filtered signal by applying a morphology-based peak searching method, and positioning the position and the characteristic of a real gamma instant radiation pulse event. According to the method, time-frequency analysis is carried out through S time-frequency transformation, the problem that the frequency of an analysis window cannot be adjusted through short-time window Fourier transformation is solved, the method has better frequency resolution and phase retention characteristics, and gamma pulse signal characteristics can be represented more accurately.

The invention discloses an OTFS waveform modulation method based on DFrFT time-frequency analysis, solves the problems of fixed time-frequency resolution, signal fuzziness and cross term interference existing in Heisenberg (Wigner) transformation in an OTFS system in a high dynamic scene, and belongs to the technical field of information and communication. The method provided by the invention comprises the following steps of: modulating sending data to obtain a DD domain signal, firstly performing inverse symplectic Fourier transform on the DD domain signal, then converting the DD domain signal into a time domain signal block through IDFrFT transform, adding a cyclic prefix (CP) to the time domain signal block, performing row-column transform, and then transmitting the time domain signal block; a cyclic prefix (CP) is removed from a signal received by a receiving end, the signal is converted into a time domain signal block subjected to parallel processing, a time-frequency domain signal is obtained after the time domain signal block is subjected to DFrFT transformation, then the time-frequency domain signal is mapped back to a DD domain through the Sextile Fourier transform, a DD domain signal is obtained, the DD domain signal # imgabs0 # is demodulated, and recovered sending data are obtained. The IDFrFT provided by the invention obviously reduces the calculation complexity.

The invention belongs to the technical field of power quality disturbance detection, and particularly relates to a power quality disturbance detection method based on improved adaptive S transformation, which comprises the following steps of: designing window width control factors of two Gaussian windows, flexibly adjusting time domain window widths and frequency spectrum characteristics of the Gaussian windows, and forming a global adaptive Gaussian window; taking upper and lower limits of time-frequency two-domain resolution as constraint conditions, taking enhanced signal energy concentration ratio as a target function of an optimization scheme, and adopting an interior point method to carry out parameter solving; performing fast Fourier transform on the signal and setting a threshold value, and defining a part higher than a threshold value point as a characteristic frequency point ki; screening calculation at a non-characteristic frequency point, only performing time-frequency transformation at a characteristic frequency point ki, and constructing a fast algorithm for improving adaptive S transformation; and obtaining a final disturbance characteristic information value. According to the method, the global adaptive Gaussian window is constructed as a kernel function for improving adaptive S transformation, the effective window length and the frequency spectrum of the window function can be automatically adjusted along with the change of the detection frequency, window parameters are prevented from being frequently switched for improving the time-frequency resolution, and various power quality disturbances are efficiently and accurately detected.

The invention provides a frequency spectrum splicing method and device, equipment, a storage medium and a program product, and is applied to the technical field of signal processing. The method comprises the following steps: determining a target number of analog-to-digital converters according to a target sampling bandwidth and a sampling rate of a single analog-to-digital converter, and collecting initial broadband signals by adopting the analog-to-digital converters with the target number in different frequency bands; dividing the frequency spectrum of the initial broadband signal into a plurality of sub-bands, and performing target processing on the sampling rate of each sub-band in the plurality of sub-bands to obtain a target sampling rate; performing fast Fourier transform processing on the target sampling rate based on fast Fourier transform points to obtain frequency domain data; performing frequency spectrum splicing on the frequency domain data to obtain a target frequency spectrum; wherein the target processing comprises the steps of sequentially carrying out time domain filtering processing, and adjusting the sampling rate through decimation filtering or interpolation filtering according to the target frequency resolution.

The invention discloses an AD diagnosis method fusing electroencephalogram frequency spectrum standardization deviation and a brain network. The AD diagnosis method comprises the steps that S1, band-pass filtering and fragment segmentation are conducted on multi-channel EEG signals of five brain regions; s2, for each segment, calculating brain region-frequency band characteristics of five different brain regions; s3, performing standardized modeling by adopting a GAMLSS model, identifying individual deviation information of a disease group about a specific brain region rPSD, training a LightGBM classifier by utilizing the individual deviation information, and extracting a structured feature vector by utilizing an internal structure of the classifier; s4, dividing each segment into a plurality of non-overlapping frequency boxes according to frequency resolution, calculating an FCN graph in a matrix form of each frequency box, constructing a three-dimensional image according to the FCN graph, and extracting a visual feature vector by using a Swin transformer network; and S5, constructing a connection vector, and inputting the connection vector into a classifier for classification. The method provides a new technical approach for accurate diagnosis and differential diagnosis of neurodegenerative diseases.

The invention provides an inhaul cable self-vibration fundamental frequency automatic identification method and system based on a harmonic product spectrum, and relates to the technical field of structure monitoring, and the method comprises the steps: receiving vibration acceleration data of an inhaul cable, carrying out the windowing processing of the vibration acceleration data through employing a Hanning window, and generating a windowed signal; carrying out fast Fourier transform on the windowed signal, and improving the frequency resolution by implementing zero filling to obtain an initial frequency spectrum; smoothing the initial frequency spectrum to form a smooth frequency spectrum; performing passband clipping on the smooth frequency spectrum to zero the passband outer amplitude to form a clipped frequency spectrum; calculating the signal-to-noise ratio of the frequency spectrum after cutting, and comparing the signal-to-noise ratio with a preset threshold value; if the signal-to-noise ratio is lower than a preset threshold value, judging that the signal quality is unqualified and terminating the processing flow; and if the signal-to-noise ratio is equal to or higher than the preset threshold value, continuing the normal processing flow. According to the invention, automatic and high-precision identification of the self-vibration fundamental frequency of the inhaul cable can be realized.

According to embodiments, a method performed by a distributed unit (DU) may involve: transmitting, to a radio unit (RU), a first control plane message including section extension information for indicating the number of one or more signal-to-interference-plus-noise-ratio (SINR) values for each physical resource block (PRB) corresponding to a frequency resolution of an SINR report by the RU; and receiving, from the RU, a second control plane message including section type information for indicating the one or more SINR values according to the frequency resolution and the number of the one or more SINR values for each PRB.

The invention discloses a bearing fault diagnosis method based on wavelet time-frequency coding and a convolution visual converter. The method comprises the following steps: collecting vibration signals of a bearing under different working conditions; performing continuous wavelet transform on the one-dimensional vibration signal to generate a two-dimensional time-frequency diagram with high time-frequency resolution; carrying out coding and image enhancement on the time-frequency graph through an enhanced color mapping mode; inputting the time-frequency graph into a convolution visual converter model, and realizing local and global combined extraction of vibration features through convolution projection, a self-attention mechanism and a multi-stage feature fusion structure; the features are classified to identify a state of health or type of fault of the bearing. According to the method, the diagnosis precision and robustness in a complex working condition and high-noise environment are effectively improved, and the method is suitable for state monitoring and early fault early warning of various types of rotary mechanical equipment.

The invention discloses a power transformation equipment ultrasonic partial discharge intelligent identification and early warning system based on voiceprint diagnosis, and relates to the technical field of partial discharge detection. Comprising a data acquisition unit, a data preprocessing unit and a data processing unit. According to the intelligent recognition and early warning system for the ultrasonic partial discharge of the power transformation equipment based on voiceprint diagnosis, filtering and noise reduction are performed on ultrasonic signals through the data preprocessing unit, time-frequency analysis is performed on the ultrasonic signals of the power transformation equipment by introducing a matrix beam method, the frequency resolution of the ultrasonic signals is improved, multi-source monitoring data are fully utilized, and the accuracy of recognition and early warning of the ultrasonic partial discharge of the power transformation equipment is improved. Through combination with an advanced machine learning algorithm, comprehensive analysis and evaluation of partial discharge signals are realized, and the technical problem that other ultrasonic interference existing in the substation equipment partial discharge ultrasonic monitoring process is difficult to evaluate in the prior art is solved.

The invention discloses a self-adaptive broadband measurement method and system suitable for a power distribution automation terminal, and the method comprises the steps: carrying out windowing FFT calculation, determining a coarse frequency based on a frequency spectrum amplitude, carrying out the judgment of a dense spectrum, carrying out windowing ZoomFFT calculation under a dense spectrum condition, carrying out the multi-spectral-line interpolation calculation, and carrying out the calculation of the multi-spectral-line interpolation. And finally, extracting electrical quantity broadband frequency components needing to be measured and corresponding amplitudes and phases thereof through frequency spectrum sorting. According to the method, the frequency resolution is adaptively changed for calculation according to the spectral characteristics of the electrical quantity, the dense harmonic and inter-harmonic detection is realized, the operation cost is lower, and the engineering use value is good.

The invention discloses a ship signal adaptive time-frequency analysis method based on instantaneous frequency direction self-adjustment. The ship signal adaptive time-frequency analysis method comprises the steps of collecting ship signals to be subjected to time-frequency analysis and performing short-time Fourier transform; estimating the instantaneous frequency of the ship signal based on short-time Fourier transform time-frequency distribution in combination with time-frequency narrow-band features; calculating the instantaneous frequency and the optimal window length after smoothing; segmenting the smoothed instantaneous frequency in time, calculating time-frequency domain direction pre-guidance of each time period, and constructing a Chirp atomic direction over-complete dictionary; segmenting the ship signal in time, and calculating the matching direction of the ship signal in each time period so as to calculate and improve Chirplet transformation time-frequency distribution; and calculating the optimal short-time Fourier transform in combination with the optimal window length, and correcting and improving Chirplet transform time-frequency distribution. According to the method, the robustness of short-time Fourier transform and the anisotropy of improved Chirplet transform are utilized at the same time, and time-frequency distribution with high robustness and high time-frequency resolution is obtained.

The invention discloses an electric wire and cable partial discharge online detection and insulation defect evaluation system. The system comprises a distributed sensor array, a signal preprocessing unit, a feature extraction module, a defect evaluation unit, a positioning module and an early warning unit. Multiple sensors cooperatively detect, a distributed sensor array realizes full-line coverage, weak discharge signals are effectively captured, and the detection sensitivity reaches a picobank (pC) level. According to the dual positioning algorithm, an improved TDOA algorithm and a PSO algorithm are combined, and the positioning error is smaller than or equal to 0.5 m (laboratory environment) and smaller than or equal to 1.5 m (actual engineering environment) and is improved by 30%-50% compared with a traditional method. Time-frequency feature depth extraction: wavelet packet transformation and S transformation are combined to realize 0.1 microsecond-level time resolution and 5kHz-level frequency resolution of discharge signals, and different types of discharge are accurately identified.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

Methods and apparatus for audio identification during a performance are disclosed herein. An example apparatus includes at least one memory and at least one processor to transform a segment of audio into a log-frequency spectrogram based on a constant Q transform using a logarithmic frequency resolution, transform the log-frequency spectrogram into a binary image, each pixel of the binary image corresponding to a time frame and frequency channel pair, each frequency channel representing a corresponding quarter tone frequency channel in a range from C3-C8, generate a matrix product of the binary image and a plurality of reference fingerprints, normalize the matrix product to form a similarity matrix, select an alignment of a line in the similarity matrix that intersects one or more bins in the similarity matrix with the largest calculated Hamming similarities, and select a reference fingerprint based on the alignment.

The invention discloses a low-slow-small target micro-motion feature extraction method based on a GST-WT combination, and relates to the field of low-slow-small target micro-motion feature extraction, and the method comprises the steps: obtaining a multi-band radar echo signal, carrying out the preprocessing of the multi-band radar echo signal, carrying out the time-frequency analysis of the preprocessed signal through a GST-WT combination model, and carrying out the extraction of the micro-motion feature of a low-slow-small target. Obtaining time-frequency characteristics of improved generalized S transform and Morlet wavelet transform; carrying out linear interpolation on the time-frequency characteristics of the improved generalized S transform; fusing the time-frequency features by adopting a linear weighting strategy to obtain fused time-frequency features; the feature extraction effect is quantitatively evaluated in combination with the energy entropy and the energy concentration degree index, the advantages of GST global time-frequency analysis and WT local self-adaption are fully played, the micro-motion features of the target are effectively separated and accurately extracted, the time-frequency resolution, the anti-noise capability and the feature stability are improved, and the target tracking accuracy is improved. Technical support is provided for low-altitude, slow and small target detection in scenes of low-altitude security and protection, unmanned aerial vehicle supervision and the like.

The invention discloses an abnormal sound detection method, device and equipment and a storage medium, and relates to the technical field of industrial detection, and the method comprises the steps: determining the time-frequency resolution and signal characteristic information of a to-be-detected audio signal; generating a spectrogram to be detected according to the time-frequency resolution and the signal characteristic information; obtaining current classification information output by the target efficient classification model; and determining an abnormal sound detection result of the to-be-detected audio signal according to the current classification information. Through the above mode, rich input information is provided for the target efficient classification model by converting the to-be-detected audio signal from a sound form to the to-be-detected spectrogram in an image form, that is, the sensitivity of the model to low signal-to-noise ratio abnormal sound is improved by using a cross-stage collaborative attention mechanism; according to the method, the progressive feature pyramid is utilized to reduce information faults caused by resolution jump of the spectrogram, and then the abnormal sound detection result is determined by combining the current classification information, so that the accuracy of abnormal sound detection can be effectively improved, and the production efficiency and quality of products are further improved.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

The invention relates to the technical field of crossing of medical instruments and artificial intelligence, and provides a hearing threshold detection method based on multi-mode hearing electrophysiological signal fusion. Comprising stimulation frequency otoacoustic emission and auditory brainstem response induced by sweep frequency stimulation sound, distortion product otoacoustic emission induced by dual-tone stimulation and middle ear sound immittance / reflection signals, and a multi-task deep learning model is constructed to perform fusion analysis on the signals. According to the method, accurate quantitative regression prediction of hearing thresholds with any frequency resolution and automatic classification and discrimination of hearing loss properties are synchronously realized, the problems of single evaluation dimension and insufficient complex hearing loss analysis ability in the prior art are effectively solved, the comprehensiveness, accuracy and clinical operation efficiency of objective hearing evaluation are remarkably improved, and the method is suitable for popularization and application. And an innovative system solution is provided for fine diagnosis of the auditory function.

The invention provides a radar interference signal identification method and system based on a deep fusion neural network, and the method comprises the steps: obtaining an echo signal of a radar, carrying out the generalized S transformation of the echo signal to generate a time-frequency image, carrying out the preprocessing of the time-frequency image, and obtaining a preprocessed time-frequency image; and identifying the preprocessed time-frequency image by using a pre-trained deep fusion neural network model, and outputting an interference signal type. According to the method, the time-frequency image of the radar interference signal with high time-frequency resolution is obtained by adopting generalized S transformation, the time-frequency domain characteristics of the radar interference signal can be flexibly described, and the quality of a learning sample is ensured.

The invention discloses an ISAR imaging method based on a low-rank adaptive stable diffusion model, and the method comprises the steps: carrying out the motion compensation, range compression and short-time Fourier transform processing of an ISAR radar echo signal of a rigid target, and generating a low-resolution TFR; inputting the low-resolution TFR into a low-rank adaptive stable diffusion model (LoRA-SD) to carry out TFR super-resolution processing, and improving the frequency resolution by refining the curve characteristics of the TFR to generate a high-resolution TFR; wherein the low-rank adaptive model (LoRA-SD) is obtained by performing fine adjustment on a pre-trained SD-Turbo large model by using a LoRA adapter; and finally, generating a high-resolution ISAR image by using a distance-instantaneous Doppler algorithm. The method breaks through the frequency resolution limit of traditional time-frequency analysis, significantly improves the resolution and quality of ISAR imaging, and has good generalization ability and robustness.

The invention relates to the technical field of wireless carrier communication, in particular to a cluster unmanned aerial vehicle mixed signal separation and identification method, which comprises the following steps of: extracting a receiving strength time sequence from a received cluster unmanned aerial vehicle mixed wireless carrier communication signal, and calculating the sum of absolute values of difference values between continuous points of the sequence under a plurality of time scales, and linear fitting is carried out to establish a mixed channel fractal dimension. According to the method, difference absolute value accumulation and calculation under a plurality of time scales are carried out on a mixed wireless carrier communication signal receiving strength sequence, a mixed channel fractal dimension is established, and the fractal dimension is utilized to realize adaptive setting of an analysis window length and a stepping amount, so that the time-frequency resolution capability of subsequent segmented Fourier transform is improved; after a mixed signal time-frequency spectrogram is generated, a complete discrete time-frequency trajectory set is constructed through multi-frequency unit peak energy detection and association along a time axis, and unmanned aerial vehicle signals with closely overlapped frequencies can be distinguished.

The invention provides a direct drinking water equipment fault intelligent diagnosis and real-time alarm control method, and relates to the technical field of intelligent monitoring and fault diagnosis, and the method comprises the steps: collecting pressure, flow, conductivity, turbidity and temperature parameters through a multi-source sensor, and transmitting the parameters to a central processing unit; the method comprises the following steps: preprocessing data by adopting dynamic wavelet transform, optimizing a mother wavelet function and a decomposition scale based on a time-frequency resolution index, and filling an abnormal value in combination with sliding window detection and linear interpolation to generate a standardized data set; extracting significant frequency band characteristics of a time domain mean value, a variance and frequency domain energy, and performing dynamic dimension reduction and normalization through principal component analysis; inputting the feature vector into a support vector machine and random forest hybrid model, and outputting a fault type and probability; dynamically adjusting an alarm threshold according to the probability, introducing a water quality anomaly index to evaluate a conductivity and turbidity collaborative risk, triggering graded alarm and pushing the graded alarm to a terminal; model parameters are updated by adopting an incremental learning algorithm, and a response strategy is optimized in combination with equipment load and environment temperature.