195 results about "Source separation" patented technology

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention relates to the related technical field of vibration noise control, in particular to an intelligent vibration noise control method and system using frequency spectrum characteristics, and the method comprises the steps: collecting rail transit vibration noise signals, extracting time-frequency domain mixed characteristics, carrying out multi-source separation, generating a noise source contribution degree matrix, configuring a strategy set, and monitoring changes to generate feedback. And the active damper is adjusted in real time for noise reduction. The technical problems that the noise suppression effect is limited and the noise reduction requirement of a complex time-varying scene cannot be accurately matched due to the fact that vibration noise control is difficult to adapt to a dynamically changing vibration noise environment are solved, time-frequency domain mixed features are extracted, overlapped noise sources are identified and separated more accurately, complex characteristics of vibration and noise are comprehensively considered, and the noise reduction effect is improved. And the active damper is dynamically adjusted so as to adapt to changes of vibration and noise, and the accuracy and effectiveness of dynamic adjustment are improved.

The invention relates to the technical field of voice recognition, in particular to a multi-channel voice conversion and synchronous transmission system applied to simultaneous interpretation. Multi-language voice signals are collected through a multi-microphone array, background noise is dynamically eliminated by adopting a noise suppression technology combining spectral subtraction and deep learning, and a dynamic information source verification symbol is generated to ensure data synchronization integrity; a blind source separation technology is combined with time-frequency analysis and time delay estimation to realize multi-language signal separation and synchronization, and a time sequence is dynamically adjusted through voice activity detection; an end-to-end ASR-NMT-TTS model is constructed to realize voice real-time translation and synthesis, and low-delay transmission is carried out based on a 5G network; the real-time monitoring module is adopted to dynamically adjust the output delay and the signal-to-noise ratio, and the translation delay and the synchronization precision are optimized in combination with user feedback. According to the method, dynamic noise reduction, multi-source synchronization and a self-adaptive feedback mechanism are integrated, the problems of distortion and delay of multi-language simultaneous transmission in a complex noise environment are solved, and the obvious technical synergistic effect and practicability are achieved.

Systems and methods includes receiving a single-track audio input stream having a mixture of audio signals generated from a plurality of sources, training an audio source separation model using, at least in part, the received single-track audio input stream, and separating audio sources, using the audio source separation model, from the audio input stream in accordance with one or more processing recipes to generate a plurality of source separated output stems. The audio separation model is trained to receive the single-track audio input stream and generate a plurality of audio stems corresponding to one or more audio sources of the plurality of sources.

The invention provides a GIS equipment detection system and method based on partial discharge-vibration signal fusion, and relates to the technical field of GIS equipment detection. The method has the flexibility of adapting to different data conditions, and vibration signals and partial discharge signals at a contact and a basin-type insulator are respectively obtained through blind source separation of collected multi-source signals for GIS equipment fault detection; the respective sequential relationship of the vibration signal and the partial discharge signal is considered, the multi-scale fuzzy entropy of the vibration signal and the partial discharge signal is calculated, the richness represented by the vibration characteristic and the partial discharge characteristic is further enhanced, the problems of poor reliability and large detection error of a fault detection method based on a single technology are effectively solved, and the fault detection efficiency is improved. The problems that the characteristics of an existing vibration diagnosis system and the characteristics of an existing partial discharge diagnosis system are prone to omission and low in accuracy are solved. Meanwhile, a three-dimensional digital twinborn visual model of the GIS equipment is also established, and the operation of the GIS equipment can be monitored in real time in combination with fault information.

The invention discloses an acoustic automatic recognition system and method for birds in a wetland environment, relates to the technical field of ecological monitoring and acoustic signal recognition, and provides a four-step flow of multi-source noise modeling, blind source separation and noise reduction, multi-label recognition and multi-modal fusion feedback for a wetland complex acoustic environment. The method comprises the following steps: 1, constructing an acoustic dictionary and mixing noise and twitter by using a contribution coefficient; 2, performing multi-sound-source unmixing by using a self-supervision method, and outputting a quasi-pure orbit; step three, identifying and labeling overlapped twitter by combining multi-label classification and dynamic spectrum enhancement time enhancement factors and frequency enhancement factors; and 4, judging the noise type through the microphone array and external environment parameter fusion, updating the noise template and the birdsong template to form closed-loop iteration, maintaining high recognition precision in a noisy and multi-species chorus scene, and providing efficient support for wetland ecological monitoring and protection decision.

The invention discloses a music source separation method and wearable equipment, and relates to the technical field of signal processing, music source separation is performed by adopting a neural network model of a UNet architecture, the neural network model at least comprises an encoder and a decoder, and the method comprises the following steps: obtaining an original signal of a to-be-separated music source, and converting the original signal to a frequency domain to obtain a frequency domain signal; performing feature coding processing on the frequency domain signal through an encoder to obtain coded feature data, the feature coding processing including feature extraction, and the encoder performing feature extraction processing at least by using a TFC-TDF block of causal convolution; and performing feature decoding processing on the coded feature data through a decoder, and outputting to obtain a music source separation result. According to the method and the device, low-delay music source separation is realized in the end side equipment with limited resources.

The invention relates to the technical field of signal reconstruction, in particular to a complex electromagnetic environment-oriented millimeter wave signal blind source separation and reconstruction system, which comprises a frequency spectrum trend division module, a path fading construction module, an initial cluster label generation module, a multi-solution path screening module and a fusion reconstruction execution module. According to the method, power spectral density sequence processing is carried out on millimeter wave frequency domain data, a multi-dimensional feature group is formed according to a path loss factor, an angle of arrival and the like, and an initial feature cluster is screened through an Euclidean distance, so that the accuracy of signal source classification is effectively improved; after the frequency domain response and the phase contour are continuously subjected to point comparison, path screening is completed by integrating a mean square error residual value, the path misjudgment probability is reduced, time domain resampling and phase frequency offset standardization are executed in fusion reconstruction, the consistency and fidelity of signal reconstruction are enhanced, and the reconstruction precision is improved. The whole process improves the separation accuracy and reconstruction precision of mixed signals in a complex electromagnetic environment.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention discloses a robot motion control method and system based on voice interaction, and relates to the technical field of robot motion control, and the method comprises the steps: extracting each segment of independent audio signal and keywords conforming to a preset voice library from mixed multi-channel audio based on a blind source separation technology; and selecting the keyword with the highest confidence coefficient as an execution command word, obtaining a standard track of an action corresponding to the execution command word, generating an executable simulation track of the execution command word in combination with real-time environment information, and controlling the robot to execute motion according to the simulation track after judging that the simulation track is safe and feasible. According to the method, the multi-channel mixed audio is separated into the independent signals, the execution command word is determined after the voice sources of different users are distinguished, the motion trail generated by simulating the conditions of the corresponding preset action and the enforceable environment is utilized, and the multi-dimensional characteristics of the motion trail and the standard trail of the preset action are compared and analyzed; and evaluating the feasibility of the motion trail corresponding to the execution command in the environment.

Various embodiments of the present disclosure provide methods, apparatuses, systems, and / or devices that are configured to separate multi-source audio signal samples into discrete source separated channel audio samples using trained multi-modal audio source channelization models. Multi-source audio signal samples are difficult to separate because they can include multiple target audio sources (e.g., individual speakers) that are often inter-mixed and overlaid with other audio sources such as noise, music, reverberations, and other audio artifacts. The multi-modal audio source channelization models discussed herein are trained to generate source separated channel audio samples based on audio signal samples and on video signal samples.

The invention provides a GIS partial discharge positioning method based on deep learning and acoustoelectric combination, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring an original ultrahigh frequency signal and an original ultrasonic signal of a target detection entity, constructing an acoustoelectric signal matrix, and performing short-time Fourier transform to obtain a complex spectrum tensor and a power spectrum tensor; and then, extracting a high-dimensional feature embedding tensor by using a double-branch deep learning network, and generating a time-frequency masking tensor and a source confidence tensor through a multi-source sub-ion network to complete source-by-source spectrum reconstruction. And a time difference of arrival matrix is calculated by using a phase transformation generalized cross-correlation method, a sensor space coordinate is combined to be input into the depth regression positioning sub-network, and a discharge source is output to predict three-dimensional coordinates and regression uncertainty. And finally, fusing the regression uncertainty and the source confidence tensor to construct a confidence interval, and generating a visual positioning result with an ellipsoid confidence boundary. By implementing the technical scheme provided by the invention, the accuracy of partial discharge positioning is improved.

The invention relates to an array microphone noise reduction recording method based on cascade noise reduction and blind source separation, which belongs to the technical field of voice signal processing and recording, and comprises the following steps: configuring a multi-channel array microphone, ensuring that the amplitude and phase of a channel signal are consistent, and collecting an original multi-channel voice signal; a weighted kernel function blind source separation algorithm is adopted, signal-to-noise ratio distribution characteristics of signals are extracted, kernel function weights are given, and target voice and interference signal components are obtained through decoupling of an independent component analysis model; executing target-oriented adaptive cascade noise reduction, locking the voice of a keynote speaker through directional pickup, reducing noise, filtering out reverberation, and enhancing the voice of a far-field target by combining a voice mask neural network with a far-field pickup algorithm in sequence; and processing the target voice through voice feature perception lossless coding and storing the target voice. According to the invention, stable acquisition of multi-channel signals, accurate separation of mixed signals and layered suppression of noise reverberation are realized, the signal-to-noise ratio and definition of far-field voice are significantly improved, and the method is suitable for single-person speaking or multi-person dialogue scenes.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention relates to the technical field of electric power operation and maintenance, in particular to a transformer fault diagnosis method, device and equipment and a storage medium, and the method comprises the steps: collecting a transformer acoustic monitoring signal based on an acoustic microphone array, carrying out blind source separation and decoupling, and constructing an acoustic signal feature set; transformer operation condition parameters are extracted, mechanical state degradation evaluation is carried out according to the acoustic signal feature set, and a mechanical state health degree index is generated; and calculating an acoustic signal receiving timestamp of the acoustic microphone array, performing sound source distribution mapping, and constructing a sound source spatial distribution diagram. According to the method, blind source separation decoupling, mechanical state degradation evaluation, sound source distribution mapping and fault situation diagnosis are performed by acquiring the acoustic signals and the operation condition parameters, so that tiny mechanical faults in the transformer can be accurately identified, intelligent maintenance decision is realized, and the method has the advantages that the tiny mechanical faults in the transformer can be detected earlier; the real-time performance and reliability of fault diagnosis are improved, and the accident risk caused by diagnosis lag is reduced.

A conferencing system includes a plurality of microphones and an audio processing system that performs blind source separation operations on audio signals to identify different audio sources. The system processes the separated audio sources to identify or classify the sources and generates an output stream including the source separated content.

The invention provides a monitoring method and system for a cutting penetration state in a water-jet guided laser processing process based on sound signal blind source separation, and the monitoring method comprises the steps: S1, in the water-jet guided laser processing process, collecting a plurality of observation sound signals in real time, the observation sound signals being mixed sound signals composed of a plurality of independent sound source signals; s2, estimating and solving a plurality of independent sound source signals by using a blind source separation algorithm according to the plurality of collected observation sound signals; s3, the frequency spectrums of the multiple independent sound source signals are compared with sound signals generated during material laser processing, so that sound signals generated during water-guided laser processing due to material removal are determined from the frequency spectrums; and S4, the water-jet guided laser machining process is judged according to the relation that sound signals generated due to material removal change along with time. Compared with a traditional coaxial visual monitoring scheme, the method is not affected by factors such as water flow and light intensity, and can be applied to monitoring of the water-guided laser machining process.

The embodiment of the invention discloses a music source separation method and device, a network training method and device, equipment and a storage medium. The method comprises the steps that a to-be-processed first mixed music source is acquired; performing music source separation on the first mixed music source by adopting a target music source separation network to obtain prediction results of at least two sound components separated from the first mixed music source; wherein the target music source separation network is obtained by taking the first music source separation network as a teacher model, taking the second music source separation network as a student model, and performing knowledge distillation on the second music source separation network; a parameter amount of the first music source separation network is greater than a parameter amount of the second music source separation network. According to the method, knowledge distillation is carried out on the second music source separation network with smaller parameter quantity by using the first music source separation network with larger parameter quantity through a knowledge distillation technology, so that the target music source separation network with better performance and lower time delay can be obtained.

A method comprises: obtaining softmask values for frequency bins of time-frequency tiles representing an audio signal; reducing, or expanding and limiting, the softmask values; and applying the reduced, or expanded and limited, softmask values to the frequency bins to create a time-frequency representation of an estimated target source. An alternative method comprises, for each time-frequency tile: obtaining softmask values; applying the softmask values to the frequency bins to create a time-frequency domain representation of an estimated target source; obtaining a panning parameter and a source concentration estimates for the target source; determining, using the panning parameter estimate and the softmask values, a magnitude for the time-frequency representation of the estimated target source; determining, using the panning parameter estimate and the source phase concentration estimate, a phase for the time-frequency representation of the estimated target source; and combining the magnitude and the phase.

The invention relates to the technical field of power equipment fault monitoring, in particular to a distribution box fault monitoring method, and aims to solve the problem that a traditional monitoring method in the prior art depends on a single filtering or fixed threshold strategy and is difficult to effectively separate fault features. According to the method, mechanical vibration and electromagnetic interference signals can be effectively separated through an adaptive blind source separation algorithm, the problem of early fault signal identification in a mixed interference environment is solved by combining multi-scale feature fusion and a dynamic environment compensation mechanism, and the method has the advantages of improving monitoring accuracy and realizing accurate quantification of fault energy.

The invention discloses a leakage monitoring method based on an LNG fuel gas system. The leakage monitoring method comprises the steps that a self-adaptive frequency modulation continuous wave active acoustic scanning network is established; blind source separation is carried out on mixed signals in acoustic scanning network abnormal events by adopting self-adaptive kernel independent component analysis; constructing a leakage feature mapping model of the physical information neural network; leakage source accurate positioning and quantification based on acoustic tomography and Bayesian reasoning are carried out; multi-modal decision fusion is carried out based on the multi-dimensional data sources received in parallel, a false alarm suppression mechanism is set, and time continuity verification and space consistency verification are carried out; establishing a reinforcement learning model for autonomously optimizing a monitoring strategy according to environment change and system state, and realizing adaptive optimization; the strategy network after self-adaptive optimization is deployed at the cloud, actions are generated regularly according to the current state, the actions are issued to the regional gateway and the edge node for execution, and iterative updating is carried out, so that the monitoring accuracy in a complex environment is improved, and the false alarm rate is reduced.

An audio processing system and method are provided, the audio processing system comprising: an input interface that receives an input audio mix and transforms it into a time-frequency representation defined by values of time-frequency units; a processor that maps values of the time-frequency units into a hyperbolic space by executing an embedded neural network trained to associate the respective time-frequency units to high-dimensional inserts and projecting the respective high-dimensional inserts into the hyperbolic space; and an output interface that accepts a selection of at least a portion of the hyperbolic space and renders a selected hyperbolic embedding that falls within the selected portion of the hyperbolic space.

The invention discloses a sound compatibility-oriented audio frequency integrated management method, which comprises the following steps of: when any receiving end receives an external sonar signal, determining an enabled audio frequency function based on the working state of the receiving end, and searching an audio frequency characteristic under the corresponding audio frequency function from an audio frequency data management library; performing signal separation on the external sonar signals based on the audio frequency characteristics to obtain first-class audio frequency signals under audio frequency integrated management, and performing subtraction of the first-class audio frequency signals on the received external sonar signals to obtain second-class audio frequency signals; and carrying out signal filtering and separation on the second type of audio signals by adopting a blind source separation technology in a prior communication mode to obtain third type of audio signals sent by the friend sonar equipment which does not establish communication connection. According to the invention, the integrated management technology of audio frequency characteristics is utilized, and the accurate audio frequency characteristic matching and signal separation technology is adopted, so that the interference of external sonar signals is effectively reduced, and the definition and reliability of the sonar signals are improved.

The invention discloses a de-noising system based on adaptive beam forming and ICA (independent component analysis). The de-noising system comprises a multi-channel sensor array, an adaptive beam forming unit, an independent component analysis unit and a cooperative control module, the multi-channel sensor array is used for collecting a mixed signal containing a target signal and noise; the adaptive beam forming unit suppresses noise in an interference direction through spatial filtering and outputs a preliminary enhanced signal; the independent component analysis unit is used for receiving the preliminary enhanced signal and separating residual independent noise components through a blind source separation algorithm; the cooperative control module dynamically adjusts a beam forming weight and an ICA separation parameter, and balances voice distortion and noise suppression effects; through cooperation of beam direction nulling and blind source separation, reverberation, multipath scattering and Gaussian / non-Gaussian mixed noise are effectively processed, the system is suitable for scene coverage speech enhancement, medical signal processing and radar anti-interference, and the denoising effect of the system is more excellent comprehensively.

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.

The invention discloses an underwater structure low-frequency radiation sound power forecasting method based on blind source separation, and the method comprises the following steps: (1), setting a housing sound source in a shallow sea environment, and obtaining discrete sound pressure; (2) obtaining an estimation signal of the source signal through a blind source separation algorithm; (3) windowing the estimated signal space data of the source signal in the z direction to obtain the sound pressure propagating outwards after windowing; (4) the sound pressure propagating outwards is converted into a wave number domain through discrete space Fourier transform, and a holographic surface sound pressure cylindrical wave spectrum is obtained; (5) filtering evanescent waves in a wavenumber domain by using a wavenumber domain filter to obtain propagation waves on a holographic surface; (6) transmitting the filtered sound pressure to a far field in a spherical shape through a near-field acoustical holography technology by means of stable-phase estimation; (7) deducing the sound intensity by using the far-field sound pressure so as to obtain the radiation sound power; according to the invention, the measurement workload is reduced; measurement is carried out on a holographic surface close to a sound source, the influence of boundary reflection is small, and a calculation result is accurate.

The invention provides a method for determining a noise component demarcation point in an electric signal of power transformation equipment, which belongs to the technical field of power transformation equipment, and comprises the following steps of: firstly, carrying out time domain sampling and standardization on the electric signal through a data preprocessing module of a hardware unit, and then extracting a frequency domain feature by utilizing fast Fourier transform; blind source separation is carried out through independent component analysis, and an adversarial neural network model is constructed to carry out deep analysis on independent component signals. The generator network generates a noise feature vector, and the discriminator network evaluates a noise component proportion and sets an adaptive threshold to mark a noise dominant signal. And finally, time-frequency energy density distribution characteristics are obtained through wavelet transformation, the energy concentration degree and the frequency bandwidth are calculated, noise component demarcation points are accurately determined, accurate recognition and processing of the electric signal noise of the power transformation equipment are achieved, and the problems that in the prior art, an artificially designed characteristic extraction algorithm is often depended on, self-adaptability is lacked, and the noise is poor are solved. And the effect is not good in a complex and changeable noise environment.

The invention discloses an improved SVMD combined SOBI bridge dynamic strain signal adaptive noise reduction method, which comprises the steps of obtaining an original signal of bridge dynamic strain through a strain sensor, improving an SVMD algorithm by taking kurtosis and a decomposition error as stopping criteria, and further decomposing the original signal; classifying the intrinsic mode function components through a K-means clustering algorithm so as to reconstruct a noise component signal and an initial noise reduction signal; constructing a three-channel hybrid observation matrix containing an original signal, a noise component signal and an initial noise reduction signal; performing blind source separation on the hybrid observation matrix by using an SOBI algorithm, and estimating to obtain a separated source signal and a hybrid matrix; noise components are identified based on kurtosis clustering, signals are reconstructed after frequency points are set to be zero, and high-precision noise reduction is achieved. According to the method, a deep collaborative architecture is constructed by introducing an adaptive decomposition mechanism, intelligent clustering recognition and accurate reconstruction are adopted, and efficient suppression of noise and high-fidelity retention of signal features are realized.

The invention relates to a water turbine flow channel discharge noise monitoring and fault identification system and method, interference type optical fiber hydrophone arrays are arranged at a top cover between a rotating wheel and a movable guide vane, a volute mandoor and a draft tube straight cone section mandoor, discharge noise in a flow channel is directly measured, and after photoelectric conversion, signal amplification, analog-to-digital conversion and band-pass filtering, the discharge noise in the flow channel is detected. Blind source separation is carried out by using FastICA, features are extracted in combination with empirical mode decomposition (EEMD) and an energy method, audible sound and ultrasonic frequency bands are monitored at the same time, and feature type identification is carried out in combination with an existing fault feature database based on dynamic time warping (DTW) and a support vector machine (SVM) algorithm; the output signal-to-noise ratio and the anti-interference capability of the system are improved, multi-directional analysis of discharge noise characteristics is achieved, the monitoring range is wide, the fault recognition speed is high, the accuracy is high, and the system is mainly used for monitoring the running state of the water turbine in real time, finding and early warning faults in time and improving the running maintenance efficiency of the water turbine.

The invention relates to an underdetermined blind source separation optimization method based on sensor signal correlation suppression. The method comprises the following steps: carrying out self-adaptive whitening processing on a measurement signal, and utilizing a dynamic covariance matrix reconstruction technology to greatly reduce the correlation between sensor signals, effectively compress the feature space dimension and reduce the coupling degree between acquired data. A clustering optimization framework based on L1 norm constraint is constructed, a sparse induction regularization strategy is introduced, and the source signal estimation precision is remarkably improved. Compared with a traditional method, the algorithm greatly improves the signal-to-noise ratio after the source signals are separated, so that clearer source signal representation is obtained. The method can adapt to the number of source signals and the uncertainty of a hybrid matrix, can reduce noise interference in the decoupling process, effectively extracts useful information from a complex signal environment, and provides effective technical support for system state monitoring, instrument fault diagnosis and the like.