86 results about "Time–frequency representation" patented technology

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention relates to the technical field of intelligent building heat load prediction and heat balance regulation and control, and discloses an intelligent building heat balance dynamic regulation and control method and system based on load prediction, and the method comprises the steps: constructing a time-frequency causal double-flow analysis network, mutual enhancement of the time-frequency characteristics and the causal relationship is realized; designing a hierarchical causal discovery algorithm, and mining a multilevel causal structure; developing a causal enhanced time-frequency representation learning method, and fusing time-frequency and causal information; establishing an intervention decision framework based on anti-fact analysis, and evaluating an intervention effect; an abnormal mode self-evolution recognition system is realized, and new abnormal modes are continuously learned; an interpretable abnormity diagnosis mechanism is developed, and a clear diagnosis report is provided; according to the method, the thermal load prediction accuracy is improved, the abnormal early warning capability is enhanced, the intervention efficiency is improved, the system interpretability is enhanced, and the optimization of the energy utilization efficiency is realized.

The invention provides a sound wave signal self-supervised learning enhanced robust noise reduction processing method and system, and relates to the technical field of signal processing, and the method comprises the steps: carrying out the feature enhancement of an initial time-frequency representation through a dynamic adaptive mask strategy, constructing a self-supervised reconstruction task based on the mask time-frequency representation, separating noise and signal subspaces in a semantic manifold space, and carrying out the self-supervised learning enhanced robust noise reduction. And establishing a probability propagation network in combination with time domain continuity characteristics to model a local dependency relationship, and finally generating a noise reduction weight and realizing semantic fidelity optimization. The method can effectively improve the noise reduction effect and semantic integrity of sound wave signals in a noise complex environment.

The invention provides a children practice situation evaluation method, medium and system, and belongs to the technical field of deep learning models.The children practice situation evaluation method includes the steps that children and standard playing audio signals are collected and preprocessed into time-frequency representation, a time window sequence is constructed to calculate window similarity, beat mark points are extracted to form a rhythm sequence, and the rhythm sequence is obtained; a rhythm perception analysis model is input to generate a rhythm feature representation vector, a multi-dimensional deviation vector is formed by applying multi-level time model analysis, a rhythm deviation tolerance range matrix is constructed, deviation similarity is calculated, and finally a rhythm performance score is output through a rhythm evaluation neural network model. According to the method, a convolutional neural network, a long-short term memory network and an attention mechanism are fused, and comprehensive quantitative evaluation of the playing rhythm performance of the children is realized through large-scale data set training and expert scoring verification.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is 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.

In accordance with implementations of the subject matter described herein, a solution for speech enhancement is proposed. In this solution, a target time-frequency representation at least indicating intensities of an input audio signal at different frequencies over time is obtained. The input audio signal comprises a speech component and a noise component. Frequency correlation information and time correlation information of the input audio signal is determined based on the target time-frequency representation. A target feature representation is generated based on the frequency correlation information, the time correlation information, and the target time-frequency representation. The target feature representation is for distinguishing the speech component and the noise component. An output audio signal is generated based on the target feature representation and the target time-frequency representation. The speech component is enhanced relative to the noise component in the output audio signal. In this way, the performance of speech enhancement can be improved.

The invention discloses a uterus electromyographic signal generation method based on a deep generative model, which comprises the following steps: 1, acquiring uterus electromyographic signals of the abdomen of a pregnant woman through a multi-channel surface electrode, and carrying out filtering, noise reduction and normalization preprocessing; 2, performing short-time Fourier transform on the preprocessed signal to obtain time-frequency representation, and decomposing the time-frequency representation into low-frequency and high-frequency components; 3, respectively establishing reconstruction processes of the low-frequency component and the high-frequency component, and obtaining discrete potential representation through training; 4, on the basis of an encoder and a decoder which are subjected to reconstruction training, modeling is conducted on the low-frequency discrete sequence and the high-frequency discrete sequence through a bidirectional Transform prior model, and potential space distribution of the low-frequency discrete sequence and the high-frequency discrete sequence is learned; and 5, in a generation stage, sampling through a priori model to obtain a token sequence, and generating a high-quality uterine myoelectricity signal after decoding and inverse short-time Fourier transform. The signal generated by the method has high authenticity and stability while maintaining the consistency of the time sequence characteristics and the frequency spectrum, and can be applied to uterine contraction monitoring and premature delivery risk prediction.

The invention relates to the technical field of data processing, and discloses a speech enhancement method, device and equipment based on stream matching and a medium, which can be applied to the financial and medical fields, and the method comprises the following steps: obtaining noise speech data, and carrying out time-frequency conversion processing and standardization processing to obtain standardized time-frequency representation data; performing stream matching input construction processing based on the standardized time-frequency representation data, and inputting the data to a stream matching model for processing to obtain enhanced time-frequency representation data; performing anti-standardization processing on the enhanced time-frequency representation data to obtain anti-standardized time-frequency representation data; and performing time domain reconstruction processing on the destandardized time-frequency representation data to obtain enhanced voice waveform data. In the invention, aiming at the ubiquitous problems of poor fidelity and large reasoning time delay caused by discrete quantization in the existing speech enhancement method, enhanced speech waveform data can be obtained by introducing a stream matching modeling and time frequency-time domain consistent processing chain, so that the speech detail fidelity is improved and the time delay is reduced.

This invention provides a quantum-derived Newton-Raphson optimal fractional-order spectrogram generation method, comprising the following steps: Step 1: Acquire the original audio signal and construct a fractional-order spectrogram based on fractional Fourier transform (FRFT); Step 2: Perform nonlinear scaling compression on the fractional-order spectrogram using a Mel filter bank to generate a fractional-order Mel spectrogram; Step 3: Construct an adaptive optimization framework with information entropy minimization as the objective function to measure the information fidelity between the spectrogram and the original signal; Step 4: Use the quantum-derived Newton-Raphson optimization algorithm (QNRBO) to globally optimize the fractional-order order, frame length, and frame shift hyperparameters to generate the optimal fractional-order spectrogram; Step 5: Input the optimal fractional-order spectrogram into a downstream speech recognition model. This technical solution aims to systematically solve core problems such as insufficient traditional time-frequency representation capabilities, rigid hyperparameter configuration, limited optimization algorithm performance, and feature-task disconnect.

The application discloses a kind of CNN-based rotating machinery local fault signal sparse time-frequency representation method, comprising the following steps: step S1, utilize rotating machinery local fault signal response model and signal preprocessing to construct time-frequency spectrum data set for network model training;Step S2, construct a convolutional neural network model and train the model;Step S3, the rotating speed signal of acquisition equipment end is collected and the fault characteristic time-frequency ridge of each order when fault occurs in different positions is calculated, simultaneously the vibration signal of acquisition equipment end is collected and the time-frequency spectrum sample that can be used for the input of convolutional neural network model is obtained by signal preprocessing;Step S4, the trained convolutional neural network model is used to extract sparse feature to time-frequency spectrum sample, and the sparse time-frequency representation of the sample is obtained;Step S5, extract fault characteristic time-frequency ridge and compare each order fault characteristic time-frequency ridge one by one, further determine the fault occurrence position, to complete the high-precision fault diagnosis of rotating machinery.

A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm.

The present disclosure provides a physical information neural network-based pose estimation method, device and equipment, relating to the technical field of computer, the method comprising: collecting an original sensor sequence and preprocessing the original sensor sequence to obtain a time-frequency representation; determining a multi-scale feature and a residual attention weight based on the time-frequency representation; inputting the multi-scale feature and the residual attention weight into a physical information neural network model to obtain an angular velocity estimation and a pose quaternion update term; calculating a physical residual loss according to the angular velocity estimation and the pose quaternion update term and a physical kinematics constraint, and constructing a total loss function based on the physical residual loss and a measurement consistency loss; optimizing network parameters of the physical information neural network model based on the total loss function to obtain a trained pose estimation model; compressing and deploying the pose estimation model, and generating a pose estimation result based on real-time collected sensor data through the pose estimation model. Low-cost and high-precision pose estimation can be achieved.

The invention relates to the technical field of artificial intelligence and machine learning, and discloses a method and a system for realizing broadband noise point identification of an automobile audio based on time-frequency analysis, and the method comprises the steps: adding a variable-length window function of an audio analysis fragment, obtaining a windowed audio analysis fragment, analyzing an initial time-frequency spectrum of the windowed audio analysis fragment, and obtaining an initial time-frequency spectrum of the windowed audio analysis fragment; generating an instantaneous frequency estimation matrix of the windowed audio analysis segment; combining the instantaneous frequency estimation matrix with the initial time-frequency spectrum to obtain enhanced time-frequency representation of the windowed audio analysis fragment, analyzing noise time-frequency units of the enhanced time-frequency representation, and communicating the noise time-frequency units to obtain a broadband noise point candidate region of the windowed audio analysis fragment; and analyzing the real noise point probability of the broadband noise point candidate region, and determining broadband real noise points of the broadband noise point candidate region. According to the invention, the efficiency and accuracy of automobile audio broadband noise point identification can be improved.

The application relates to an intelligent production line rotating machinery health evaluation method based on a hypergraph convolution fuzzy network, which comprises the following steps: collecting time domain signals generated by various faults of a rotating machinery system during operation to obtain corresponding time-frequency representations; dividing the time domain signals and the time-frequency representations according to fault types, adding labels, and constructing vibration data; using a hypergraph model to construct hypergraphs with the time domain signals and the time-frequency representations as graph nodes to obtain two hypergraphs under each fault type; using a weight fuzzy module to fuse the nodes of the two hypergraphs under each fault type; inputting the fused hypergraph features into a multilayer perception hybrid module; calculating the probabilities of all fault classes through an output full connection layer and a classification function to realize training, optimization and updating of the hypergraph convolution fuzzy network. The application increases the interpretability of the model and can obtain more robust rotating machinery system health evaluation results.

The invention discloses a keyless phase-change rotating speed bearing fault diagnosis method and device, and belongs to the technical field of bearing fault diagnosis. The method comprises the following steps: acquiring a variable rotating speed bearing fault vibration signal; a pre-constructed TFRPET algorithm is applied to the vibration signals, and high-resolution time-frequency representation TFR is obtained; the TFRPET algorithm compresses the width of a TF ridge through a peak value extraction operator PEO, and the energy aggregation of the TF ridge is improved; detecting a TF ridge from a time-frequency representation (TFR) according to a ridge detection technique and selecting a reference instantaneous frequency (IF) from the TF ridge; and carrying out angular domain resampling and order spectrum diagnosis on the vibration signal by using the reference instantaneous frequency IF to obtain a fault diagnosis result of the keyless phase change rotating speed bearing. The method does not need to depend on a key phase device, can adapt to a variable rotating speed working condition, achieves high-resolution TFR through the TFRPET algorithm, and solves the problem that in the prior art, the TFR resolution is insufficient, and the bearing fault judgment accuracy is affected.

The application discloses a kind of water turbine cavitation degree detection methods based on cavitation perception multi-path deep learning network model, comprising: obtaining acoustic signal sequence collected from water turbine equipment and normalizing, construct including one cavitation feature extraction path, one periodicity feature extraction path, one multi-scale mel spectrum extraction path of extracting time-frequency representation feature, feature fusion module, main classifier and auxiliary classifier in the cavitation perception multi-path deep learning network model, feature fusion module is adaptively fused to the cavitation feature and periodicity feature extracted, and learns fusion weight;Again, the physical feature vector after fusion is input into auxiliary classifier and classified, and multi-scale mel spectrum feature is input into main classifier and classified;And based on including main classification loss, auxiliary classification loss and cavitation perception alignment loss in the target cost function, the model is trained.The application can provide interpretable physical feature representation while maintaining high classification accuracy.

According to embodiments of this disclosure, a method, apparatus, device, and storage medium for text-to-speech conversion are provided. The method includes generating a predicted speech representation of the target text read by a first speaker, based on a target text to be converted and a first timbre of a first speaker. The predicted speech representation indicates speech features that vary over time. The method further includes generating a predicted time-frequency representation of the target text read by a second speaker, based on the predicted speech representation and a second timbre of a second speaker. The predicted time-frequency representation indicates the speech signal strength that varies over time at different frequencies. The method further includes converting the predicted time-frequency representation into audio of the target text read by the second speaker. This reduces the difficulty of prediction and improves the sound quality of the generated audio.

The invention discloses an automatic detection method for an uplink of a ground measurement and control station, and relates to the technical field of aerospace measurement and control and satellite communication operation guarantee. The separability of the uplink signal in the aliasing environment is obviously improved; candidate components are obtained based on blind source separation with mutual information minimization as a target, modulation identification is carried out through a depth model driven by time-frequency representation, and robust distinguishing of different satellite service systems is achieved. The carrier frequency, the symbol rate, the modulation category and the task parameter set are subjected to weighted similarity matching in a tolerance range, and one-to-one constraint solution is carried out, so that mismatching and misjudgment caused by near-frequency, near-speed or fast switching can be effectively inhibited; when observation environment or orbit parameters change, separation and window configuration can be adaptively adjusted, and real-time performance and accuracy are maintained.

In some embodiments, systems, methods, and apparatuses incorporating an hTFR-based power characterization system are provided. The system in various embodiments comprises a constant bandwidth (CB) module to generate a first group of time-frequency representation (TFR) values for a power signal acquired for a device using a first set of frequency sub-bands over a first frequency range, a constant Q (CQ) module to generate a second group of TFR values for the power signal using a second set of frequency sub-bands over a second frequency range, a TFR array generator coupled to both the CB and CQ modules to combine the first and second TFR values into an array of resultant TFR values, and a power characterization module to identify an anomaly with the device based on the array of resultant TFR values.

The invention discloses a bearing fault diagnosis method based on boundary rearrangement Chirplet transformation. The method comprises the following steps: S1, collecting a bearing fault signal and a rotating speed signal by using an acceleration sensor and an encoder; s2, generalized matching Chirplet transformation is constructed to process the bearing fault vibration signal, and a corresponding time-frequency representation result is obtained; s3, searching a local amplitude maximum value represented by time frequency along the time direction, and defining a new time rearrangement strategy; s4, designing a compression boundary algorithm based on the energy distribution characteristics to reduce the influence of environmental noise; s5, energy in the compression boundary is redistributed according to a time rearrangement strategy, and the time interval of two adjacent fault impacts extracted is calculated based on the obtained time frequency representation result; and S6, comparing the time interval with the actually extracted time interval of the fault impact, and further judging the fault occurrence position of the bearing. According to the method, the energy aggregation of time-frequency representation is improved, the noise robustness is high, and a powerful guarantee is provided for extraction of fault features of the rotating machine bearing.

The application discloses a kind of self-adapting noise reduction filtering method, system, medium and equipment, the method includes to input dynamic signal and carry out direct current offset preprocessing;Time-frequency representation matrix is generated by time-frequency transform, and amplitude spectrum is calculated accordingly;Along time axis analysis amplitude spectrum, extract short time and long time noise amplitude and fusion generate basic noise amplitude;Signal noise amplitude ratio is calculated based on basic noise amplitude, and final noise amplitude is determined;According to final noise amplitude, calculate noise reduction spectrum amplitude, retain original phase information, reconstruct as noise reduction time domain signal by inverse transform;The system includes preprocessing module, amplitude spectrum generation module, double time scale noise estimation module, denoising module, noise reduction and signal reconstruction module;The application is accurately described by double time scale noise estimation Dynamic distribution of noise, combined with the adaptive noise reduction adjustment based on signal noise amplitude ratio, effectively suppress complex, non-stationary noise, while enhancing the relative intensity of characteristic frequency band.

The application discloses a kind of sparse generalized W transform gas reservoir characterization time-frequency analysis method, for the first time L1 norm is introduced to the mathematical relationship between generalized W transform and seismic signal for sparse constraint, and is solved using Bregman iteration algorithm, to obtain a kind of analysis result with higher time-frequency resolution.The method absorbs the advantage that generalized W transform highlights low-frequency information of seismic signal, avoids the problem of main frequency splitting, and provides a more sparse time-frequency representation for non-stationary seismic signals. When applied to the time-frequency analysis of actual seismic data, it can provide a high-precision seismic spectral decomposition result for gas reservoirs, thereby more accurately delineating the low-frequency abnormal area of gas reservoir.

The application discloses a bearing fault diagnosis method and system based on statistical time rearrangement synchronous compression transformation, comprising the following steps: collecting bearing vibration and rotating speed signals, and intercepting vibration signal segments under stable working conditions; calculating a group delay estimation operator by using different window function short-time Fourier transformation; determining a group delay period center and calculating a group delay period and a group delay time width where the group delay period center is located based on the above operator; constructing a modified group delay estimation operator based on the unbiasedness of the group delay estimation operator at the center; statistically analyzing the group delay time width distribution difference between signals and noise, constructing an adaptive noise reduction threshold, and calculating a robust noise standard deviation estimation; constructing a statistical group delay estimation operator and calculating statistical time rearrangement synchronous compression transformation according to the threshold and the standard deviation; generating a time-frequency envelope spectrum based on the time-frequency representation, using a fault characteristic energy proportion index to extract an adaptive enhanced time-frequency envelope spectrum; finally, taking the index as a bearing state characteristic index to complete fault diagnosis.

The invention provides a bearing fault diagnosis method and system based on sample enhancement and domain self-adaption, and belongs to the field of industrial intelligent manufacturing, and the method comprises the steps: employing wavelet transform, converting an original vibration signal into time-frequency representation, taking the time-frequency representation as an image and a time sequence, extracting image features and time sequence features, and carrying out the recognition of the image features and the time sequence features; carrying out feature fusion on the image features and the time sequence features through a weight network; inputting the fused features into a classification network to obtain a classification result of fault diagnosis; constructing an enhanced sample as a new time-frequency representation according to the size of the first attention score; a plurality of bearing data sets disclosed in other fields are used as a source domain, collected original vibration signals of the bearing are used as a target domain, and further training is performed on an image encoder, a time sequence encoder and a gating mechanism based on the source domain and the target domain. According to the method, efficient and accurate bearing fault diagnosis is realized under the condition of limited samples.

The invention discloses an intelligent opportunity signal positioning method based on time-frequency representation attention downsampling, belongs to the technical field of positioning navigation, and solves the problem that the complexity of an existing opportunity signal positioning method is relatively high. The method comprises the following steps: respectively acquiring time-frequency representations of a plurality of signal segments of each opportunity signal sample; constructing a training set and a verification set according to the time-frequency representation of each signal segment and the coordinate label of the corresponding opportunity signal sample; training an opportunity signal intelligent positioning model based on time-frequency representation attention downsampling by using the training set, and verifying the opportunity signal positioning model by using the verification set; and inputting the time-frequency representation of the plurality of signal segments of the real-time opportunity signal into the opportunity positioning model passing verification, and predicting to obtain a positioning estimation result of the corresponding real-time opportunity signal. According to the method, downsampling is carried out by applying an attention mechanism in time-frequency representation, and the scale of input data is remarkably reduced while important features are reserved, so that the calculation complexity is reduced.

The application provides a robust denoising processing method and system enhanced by acoustic wave signal self-supervised learning, relates to the technical field of signal processing, and comprises the following steps: performing feature enhancement on an initial time-frequency representation through a dynamic self-adaptive mask strategy; constructing a self-supervised reconstruction task based on the mask time-frequency representation; separating noise and signal subspaces in a semantic manifold space; combining time-domain continuity features to establish a probability propagation network to model local dependent relationships; and finally generating denoising weights and realizing semantic fidelity optimization. The application can effectively improve the denoising effect and semantic integrity of acoustic wave signals in a noise complex environment.

A method includes receiving an audio input and generating a noisy time-frequency representation based on the audio input. The method also includes providing the noisy time-frequency representation to a noise management model trained to predict a denoising mask and a signal presence probability (SPP) map indicating a likelihood of a presence of speech. The method further includes determining an enhanced spectrogram using the denoising mask and the noisy time-frequency representation. The method also includes providing the enhanced spectrogram and the SPP map as inputs to a keyword classification model trained to determine a likelihood of a keyword being present in the audio input. In addition, the method includes, responsive to determining that a keyword is in the audio input, transmitting the audio input to a downstream application associated with the keyword.

The application discloses a radar human behavior recognition method based on multi-domain feature fusion, and is applied to the problem that the existing human behavior recognition method based on a radar sensor and a deep learning technology only adopts the features of one domain or only adopts one time-frequency analysis method in a time-frequency domain, thereby causing insufficient expression of human behavior features; three time-frequency analysis methods with different time-frequency resolutions, namely, a short-time Fourier transform, an adaptive optimal kernel time-frequency representation method and a Hann kernel reduction cross-term interference distribution, are selected; three types of frequency spectrum diagrams are obtained; then, the three types of time-frequency spectrum diagrams are combined and used by using a SE Net and a 3DCNN network in the time-frequency domain, so that human behavior features are more fully expressed; in a distance domain, a key feature is extracted by using the SE Net; and the features of two domains are combined, the mutual relationship is found, and the recognition accuracy of human behaviors is effectively improved.

The invention discloses a lightweight human body behavior recognition method and system based on an ultra wide band radar in a complex environment, and the method comprises the steps: building an echo matrix of human body behaviors through collecting through-wall human body behavior experimental data; preprocessing the echo matrix to obtain a processed echo matrix; obtaining a TDM data set based on the processed echo matrix; and inputting the TDM data set into the constructed recognition model to complete human body behavior recognition in a complex environment. According to the method, wall clutters in echo signals can be effectively removed, the adaptability in a complex environment is improved, and meanwhile, the time-frequency representation capability of human behavior characteristics can be enhanced; and an improved ShuffleNetV2 feature extraction module is adopted. The model of the invention can improve the accuracy and robustness of human behavior recognition, thereby realizing a lighter and more efficient radar behavior recognition system.