40 results about "Independent component analysis algorithm" patented technology

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention discloses a differentiated precise current distribution control method in a multi-workpiece batch electroplating process. The method comprises the following steps: acquiring multi-workpiece feature data and preprocessing the multi-workpiece feature data; workpiece difference characteristic analysis and grouping are carried out; modeling and analyzing the electric field distribution of the electrolytic cell; plating layer growth dynamic monitoring and prediction are carried out; and generating a differential current distribution strategy. A multi-frequency differential resistance spectrum acquisition system is adopted to realize real-time monitoring of plating layer growth, an improved independent component analysis algorithm fused with spatial constraint is applied to perform workpiece group resistance spectrum decoupling, and micro-area accurate current modulation is realized through a self-adaptive waveform library of an electric field distortion area. The problems that dynamic characteristics of plating layers are difficult to monitor and differentiation control of workpieces with similar characteristics is insufficient in multi-workpiece electroplating are solved, and the quality consistency of the plating layers is improved.

The invention discloses a data detection method and system based on gravitational-magnetic-electric-seismic signal joint gridding, and relates to the technical field of geological prospecting, and the method comprises the steps: determining a research region according to an exploration target and geological conditions, and dividing the research region into grids with the same size; on each grid node, gravity, magnetic, electric and seismic signals are collected; the collected gravity, magnetic and electric shock signals are preprocessed; performing feature extraction on the preprocessed gravity-magnetic-electric-seismic signals by using an independent component analysis algorithm, and performing data fusion by using a convolutional neural network in deep learning; establishing an initial value estimation incidence relation model, and obtaining an initial value prediction result based on the fused data; according to the initial value prediction result, geological parameters are obtained through solving by using a joint inversion algorithm. According to the invention, efficient combined acquisition of gravity, magnetic, electric and seismic signals is realized, the precision and quality of acquired data are improved, and unified management and fusion analysis of the data are realized.

The invention discloses a radar intermittent sampling interference resisting method based on VMD and sparse Bayesian blind separation, and the method comprises the steps: determining an intermittent sampling forwarding interference signal based on a transmitting signal model and an echo signal model of a radar, so as to determine a mixed signal received by a receiver under the condition of a single channel; decomposing the mixed signal into P modal components by using a VMD algorithm; selecting a part of modal components from the P modal components and raising the dimension of the mixed signal to obtain a new mixed signal; estimating the number of source signals by using an entropy minimum source number estimation algorithm according to the new mixed signal; reconstructing a multi-channel signal by using the number of estimated source signals and the mixed signal; and recovering a source signal by using a multi-channel signal and a positive definite independent component analysis algorithm based on sparse Bayesian learning. The method can be used for resisting intermittent sampling interference of the linear frequency modulation pulse pressure radar in a complex electromagnetic environment, has good interference suppression capability, and can effectively suppress intermittent sampling interference and recover interfered radar frequency modulation signals.

The invention belongs to the technical field of life information monitoring, and particularly relates to an FMCW radar-based non-contact breathing and heartbeat combined detection method, which comprises the following steps of: identifying the chest of a target to be detected by utilizing a target detection algorithm to obtain a target area; controlling a steering engine to enable the FMCW radar to move to a corresponding target area, transmitting a linear frequency modulation signal, receiving a reflection signal, and performing frequency mixing to obtain an intermediate frequency signal; performing distance fast Fourier transform on the sampling data of the intermediate frequency signal to obtain a distance feature, and determining a distance unit corresponding to the target according to an extreme value of the distance feature; a phase value of a distance unit where a target is located is extracted to perform phase unwrapping and phase difference operation, then a phase difference signal is subjected to signal decomposition by using a variational mode decomposition (VMD) algorithm, and a respiration and heartbeat signal is processed by using an independent component analysis (ICA) algorithm to obtain a respiration signal and a heartbeat signal; and S4, estimating the respiratory frequency and the heart rate through the frequency spectrums of the respiratory signal and the heartbeat signal. According to the invention, the signal processing efficiency and accuracy are improved.

This application discloses a method, apparatus, device, and storage medium for classifying users of a motor imagery brain-computer interface. The method includes using independent component analysis (ICA) to decompose the user's target EEG signal, obtaining multiple initial independent components. Feature data is extracted from each initial independent component. Based on the feature data, a first target independent component corresponding to contralateral event-related desynchronization and a second target independent component corresponding to ipsilateral event-related synchronization are determined from the initial independent components. Finally, the classification result of the motor imagery brain-computer interface user is obtained. This scheme improves the accuracy and reliability of classification by using ICA-related neurodynamic modeling and classifying users based on multi-dimensional neural indicators obtained from feature data. Furthermore, by combining multiple feature data for automated selection of independent components, the subjectivity issues caused by manual intervention are avoided, improving the applicability and scalability of the classification method.

According to the thermal power plant equipment fault early warning method provided by the invention, the array type vibration sensor group and the infrared thermal imaging matrix are deployed to construct the three-dimensional space-time sensing network, and the independent component analysis algorithm is adopted to carry out edge denoising processing, so that the fault feature extraction accuracy under a complex working condition is improved, and the fault early warning efficiency is improved. According to the method, geometric coordinate mapping, rotor dynamics simulation and real-time data indexing are fused based on an equipment digital twinning model, part-level positioning of bearing faults is achieved, twinning derivative parameters are injected through a transfer learning mechanism to predict target equipment, the fault missing report rate of the equipment is reduced, and a safe-economical-sustainable triple gain closed loop is formed.

The present invention provides a sound source localization method based on a polyhedral microphone array, comprising: S1: collecting multi-channel data collected by multiple microphones forming a polyhedral structure; S2: iteratively processing the multi-channel data using an independent component analysis algorithm to obtain noise-reduced target sound source data; and S3: determining the orientation of the target sound source based on the attenuation of the target sound source by each microphone. The advantages of the present invention are that by collecting sound data using a polyhedral microphone array, the microphone array receives signals that form a non-singular matrix, thereby enabling the extraction of the sound source using an independent component analysis algorithm, and then determining its orientation relative to the microphone array based on the attenuation of the sound source. This further achieves sound source localization based on sound source separation, and can meet the needs of scenarios such as abnormal sound source localization and fault detection.

The present invention discloses a vibration and current feature fusion monitoring device for the wear state of a screw pump rotor, which belongs to the technical field of screw pump monitoring. The device comprises: obtaining the real-time vibration and current signals of the screw pump rotor, constructing a variational mode decomposition objective function with energy constraints, initially dividing the vibration signal into intrinsic mode functions, screening the correlation coefficients, obtaining the vibration subsets of the rotor and hull structure, and then separating the rotor vibration signal by an independent component analysis algorithm. A hierarchical feature fusion strategy is used to synchronize the pre-processed signals, construct a multivariate time series matrix, extract the wear feature set and perform phase space reconstruction to accurately identify sudden wear points. Once identified, the probability of sudden wear is calculated, the wear rate is corrected in combination with the normal wear degree, the remaining life is predicted and the maintenance level is divided. The device aims to solve the problems of existing screw pump monitoring technology being susceptible to interference, delayed response, difficult to adapt to complex working conditions and insufficient model generalization, and to ensure the stable operation of the ship's power system.

The application relates to a kind of incremental time-varying structure operating modal parameter real-time identification method and system. By determining the modal shape matrix and modal response matrix at initial time i-1 according to multi-channel vibration response data and the time length of sliding window, it is judged whether the sum of current time i and the time length L of sliding window is less than or equal to the end time of sliding window. If yes, the modal coordinate response matrix at initial time i-1 is substituted into the calculation of the sliding window at the i-th time, and the incremental independent component analysis algorithm is used to determine the mixing matrix and source signal at the i-th time. According to the mixing matrix and source signal, the modal shape matrix and modal response matrix corresponding to the i-th time are determined, and the modal shape matrix and modal response matrix at the i+1-th time, the i+2-th time,..., the i+L-th time are determined in turn. The application can reduce the time of identifying time-varying structure operating modal parameters and improve the accuracy of the identification result.

The invention relates to the technical field of intelligent sensing systems, and discloses a method and a device for detecting the bearing capacity of a pavement engineering construction material, and the method comprises the steps: synchronously collecting a mixed signal and a pure noise signal through a main measurement sensor and a reference noise sensor, constructing a multi-dimensional observation matrix, and carrying out the centralization and whitening preprocessing, thereby obtaining the bearing capacity of the pavement engineering construction material. And blind source separation is carried out by adopting an independent component analysis algorithm based on maximized non-Gaussian property, a target bearing capacity response signal with a super Gaussian characteristic is identified according to a kurtosis value of each source signal component, and then a dynamic deformation modulus is calculated. The system comprises a sensor array module, a synchronous acquisition module, a data processing module based on FPGA hardware acceleration and a result output storage module. According to the invention, strong coherent vibration noise can be effectively suppressed, signal details are completely reserved, and high-precision and real-time bearing capacity detection is realized.

The application provides a magnetocardiogram automatic noise reduction method based on independent component analysis, which comprises the following steps: collecting multi-channel resting-state magnetocardiogram signals by using a sensor, removing high-frequency interference and baseline drift, and performing timing segmentation and standardization processing on the magnetocardiogram signals; calculating each independent component of the magnetocardiogram signals by using an independent component analysis algorithm, and obtaining labeled information by visual inspection; performing sliding window clipping processing and spectrum analysis on each independent component; performing feature extraction based on the time-domain and frequency-domain independent component data sets; inputting the time-domain independent component data set and the extracted features into a time series classification network, training the labeled information as a label, and obtaining an automatic independent component analysis model. The method provides a standardized and automatic preprocessing procedure for magnetocardiogram signals, can be applied to complex use scenarios, has a simple processing procedure, high automation degree, good robustness, strong flexibility, and high medical application value.

The present invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, which relates to the field of fault diagnosis technology. The system includes a data acquisition module, an adaptive preprocessing module, a diagnostic engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the adaptive preprocessing module uses adaptive variational modal decomposition based on an intelligent optimization algorithm for preprocessing; the diagnostic engine module includes a multi-physical quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit. The multi-physical quantity deep fusion unit deeply fuses heterogeneous data through a physical information autoencoder to generate a high-dimensional feature matrix. The dynamic source number estimation unit uses a three-layer hierarchical structure to estimate the number of source signals online. The online blind source separation unit uses an independent component analysis algorithm driven by the operating status to achieve a complete diagnostic process. It solves the problem of low diagnostic accuracy of traditional methods under strong noise, multi-source coupling and dynamic working conditions.

The invention relates to a superparamagnetic effect identification and separation method in time domain electromagnetic response. The method comprises the following steps: establishing a double-feature superparamagnetic effect identification model based on a fuzzy support vector machine algorithm; fitting the induction-magnetization effect in power law attenuation in the late period by adopting a Levenberg-Marquardt algorithm to obtain a quasi-superparamagnetic effect, and performing de-equalization and whitening on the quasi-superparamagnetic effect and the total effect as input signals of an independent component analysis algorithm; and selecting a mutual information minimization criterion and a natural gradient algorithm to update and optimize the separation matrix according to the statistical characteristics of the transient electromagnetic signal until the convergence condition is met, and finally separating to obtain the induction response and the superparamagnetic effect. According to the method, the superparamagnetic effect can be effectively identified and accurately separated, the inversion interpretation precision of the time-domain electromagnetic method in superficial superparamagnetic layer exploration is effectively improved, and technical support is provided for the development of the time-domain electromagnetic method in refined detection.

This invention discloses a device for evaluating the therapeutic effect of sleep modulation based on cortical brain energy. It utilizes an EEG acquisition system to obtain EEG and electrooculography (EOG) data of sleep following both true and false stimulation. Independent component analysis (ICA) is used to preprocess the acquired data to filter out signal artifacts. Brain energy imaging is used to calculate the distribution of neural activity and extract the time series of sleep-related cortical brain energy. The YASA algorithm is used to segment sleep based on the time series of sleep-related cortical brain energy and the EOG data, and the difference in sleep efficiency after true and false stimulation is calculated. A correlation model between stimulation parameters and the difference in sleep efficiency is established. The device includes a data acquisition module and a processing module. This invention provides a scientific basis for evaluating the therapeutic effect of sleep modulation and is of great significance for the optimal selection of stimulation parameters, which can improve the therapeutic effect of sleep modulation.

This invention relates to a method for separating vibration and flow noise in the underwater noise spectrum of a ship propeller, comprising: constructing a "frequency label" for vibration noise by fabricating two propeller models with identical geometry but significantly different material properties; a dual-model difference stage: calculating the absolute difference in the noise spectra of the two models and generating a vibration noise mask based on modal analysis results; a blind source separation stage: for the residual signal after difference, using an improved Independent Component Analysis (ICA) algorithm to further separate residual interference by utilizing the statistical independence of vibration noise; introducing a convolution kernel function in this stage to enhance the algorithm's adaptability to time-delayed signals; and introducing Cepstrum analysis technology to perform phase correction on the complex spectrum of the replacement frequency band. This method solves the significant shortcomings of existing noise control technologies in the coupling separation of vibration and flow noise, enabling the separation of the components of underwater noise from ship propellers, obtaining the noise components caused by blade vibration and the flow noise components separately.

The invention discloses a motor imagery brain-computer interface user classification method, device and equipment and a storage medium, and the method comprises the steps: carrying out the decomposition processing of a target brain wave signal of a user through employing an independent component analysis algorithm, obtaining a plurality of initial independent components, respectively extracting the feature data of each initial independent component, and carrying out the classification of a target brain wave signal based on the feature data; and determining a first target independent component corresponding to the correlation desynchronization of the opposite-side event from the initial independent components, determining a second target independent component corresponding to the correlation synchronization of the same-side event, and finally determining a motor imagery brain-computer interface user classification result. According to the scheme, the users are classified through the independent component related brain dynamics modeling and the multi-dimensional neural indexes obtained based on the feature data, so that the classification accuracy and reliability are improved; besides, automatic screening of independent components is carried out in combination with various feature data, the subjectivity problem caused by manual intervention is avoided, and the applicability and expandability of the classification method are improved.

The invention discloses a hydrogen source rock in-situ identification method and device which can be used in the field of oil-gas exploration and new energy development, and the method comprises the steps: obtaining multi-dimensional drilling data collected in situ in hydrogen source rock drilling, and obtaining a multi-dimensional real-time data flow; separating independent signals of different alteration processes from the multi-dimensional real-time data stream by adopting an independent component analysis algorithm to obtain a plurality of independent alteration components; an entropy weight method is adopted to calculate the weight of each independent alteration component, according to the obtained ground temperature gradient, each independent alteration component and the weight of each independent alteration component, a continuous hydrogen generation index of each stratum of the hydrogen source rock is determined, and the hydrogen generation index is used for quantifying the hydrogen generation capacity of the hydrogen source rock; and mapping continuous hydrogen generation indexes of all stratums of the hydrogen source rock in the three-dimensional geological network model around the well of the hydrogen source rock drilling. According to the method, cross interference in hydrogen source rock identification can be eliminated, and the hydrogen source rock exploration efficiency and accuracy are improved.

The application provides a business analysis method and system based on big data, wherein the method comprises: obtaining user click stream data, payment flow data and device electromagnetic interference signals; based on the user click stream data and the payment flow data, generating multi-source heterogeneous business time series data; converting the device electromagnetic interference signals into device vibration frequency domain features, and based on the device vibration frequency domain features and the multi-source heterogeneous business time series data, generating collaborative data streams, wherein the collaborative data streams comprise mixed business features; using an independent component analysis algorithm to decouple the mixed business features to obtain a traffic density index and a bandwidth index; dynamically correlating the traffic density index and the bandwidth index to construct a business independent feature matrix; based on a stream processing engine, converting the business independent feature matrix into a business feature analysis graph, and generating a decision strategy corresponding to the business feature analysis graph in real time. The application improves the real-time response capability of abnormal fluctuations in a high-concurrency business scenario.

The invention provides an on-line monitoring method for abnormal vibration of thermal energy storage equipment, and belongs to the technical field of thermal energy storage equipment. A multi-source sensor array comprising low-frequency and high-frequency vibration sensors is arranged at key parts of the thermal energy storage equipment, and noise interference is suppressed in a differential arrangement mode; a multi-source coupling vibration signal is separated by using an independent component analysis algorithm, time-frequency analysis is performed by combining wavelet transform to extract vibration characteristics, and a phase change vibration characteristic mechanism equation is established to calculate material physical parameter changes in a phase change process. A neural network identification model based on dynamic topology reconstruction sparse connection learning and probability graph model structured prediction is constructed, and a vibration anomaly discrimination threshold system is established to realize anomaly early warning and adaptive optimization. The technical problem that the abnormal vibration mode is difficult to accurately separate and identify by the multi-source coupling vibration signal in the phase change process of the thermal energy storage equipment is solved.

The invention discloses a method for monitoring the total suspended solid concentration in a real-time sewage treatment process based on an artificial intelligence technology, belongs to the technical field of artificial intelligence, and solves the problems that a TSS index monitoring method is high in time cost and low in monitoring precision. In the research, a stack width learning system (OSBLS) model based on an over-complete independent component analysis (OICA) algorithm is adopted, the model preprocesses data by using the OICA method, non-Gaussian features in the data are extracted, and the advantages of high precision, low calculation complexity, convenient network updating and the like of the stack width learning system (SBLS) model are retained, so that the method has the advantages of high accuracy, high calculation complexity and high efficiency, and the method is suitable for large-scale popularization and application of the stack width learning system in the large-scale popularization and application of the stack width learning system in the large-scale popularization and application of the stack width learning system in the large-scale popularization and application of the stack width learning system in the large-scale popularization and application of the stack width learning system in the large-scale popularization. The problems of high time cost and low monitoring precision in the current sewage treatment process can be solved.

This invention belongs to the field of geological disaster monitoring technology, specifically disclosing a method and system for quantitative inversion of ground fissures based on optical fiber and independent component analysis. The method includes: acquiring distributed optical fiber time-series strain monitoring data to construct an observation matrix; determining the number of components through principal component analysis; using spatial location as the observation dimension, employing independent component analysis to separate blind sources, extracting spatial independent components and corresponding time score matrices, thus decoupling the ground fissure signal from the background deformation signal; selecting components exhibiting continuous high amplitude as ground fissure strain anomaly components, and setting a threshold based on these components and the background noise level, defining intervals where the amplitude continuously exceeds the threshold as anomaly intervals; within these intervals, numerically integrating the anomaly components along the spatial direction to obtain the quantitative change in ground fissure width. This invention achieves automatic and accurate positioning of ground fissures and highly reliable quantitative inversion of their width under complex geological conditions.

This invention belongs to the field of umami perception evaluation and provides a method for recognizing umami-induced EEG signals in the taste perception process. The method includes the following steps: acquiring taste EEG data induced by umami stimulation using an EEG signal acquisition system; establishing a umami EEG dataset using the acquired data; then cleaning the raw data using an independent component analysis algorithm, including filtering, removing electrooculograms, artifacts, and rereferences, and calculating the frequency (Hz) and power spectral density; vectorizing and regularizing the 0–13 Hz signals according to brain regions; subsequently, augmenting the dataset using minority class oversampling (MCB) and dividing it into training and test sets; constructing an ensemble model for signal classification; and finally, using the trained model for umami recognition. The ensemble model method proposed in this invention significantly improves the accuracy and stability of the umami EEG recognition model, effectively achieving umami recognition.

The present invention discloses a differential precise current distribution control method in the process of batch electroplating of multiple workpieces, including: acquiring and preprocessing multi-workpiece characteristic data; analyzing and grouping the differential characteristics of the workpieces; modeling and analyzing the electric field distribution of the electrolytic cell; dynamically monitoring and predicting the growth of the coating; generating a differential current distribution strategy. The present invention uses a multi-frequency differential resistance spectrum acquisition system to realize real-time monitoring of coating growth, applies an improved independent component analysis algorithm with fused spatial constraints to decouple the resistance spectra of workpiece groups, and realizes precise micro-region current modulation through an adaptive waveform library in the electric field distortion region, solving the problems of difficult dynamic characteristic monitoring of coatings and insufficient differential control of workpieces with similar characteristics in multi-workpiece electroplating, and improving the consistency of coating quality.

The present application belongs to the technical field of fault diagnosis, and relates to a GIS partial discharge fault-oriented SF6 decomposition product multi-component spectrum detection method and system. The method comprises the following steps: firstly, obtaining initial spectrum data of SF6 decomposition mixed gas in a GIS three-phase gas chamber, and obtaining corrected spectrum data through support vector machine baseline correction; obtaining independent spectrum data through independent component analysis algorithm blind source separation; obtaining enhanced spectrum data through baseline drift calculation, partial least squares concentration inversion reconstructed spectrum and residual determination; extracting absorption peak characteristic value and completing matching verification to determine the actual concentration of each characteristic gas; obtaining the screening concentration of the target characteristic gas through diagnostic contribution degree threshold screening, inputting the random forest model to calculate the diagnostic weight data; finally, combining the weight and the concentration to calculate the insulation life decay amount, and outputting the GIS equipment fault state judgment result. The present method can adapt to complex operation conditions of GIS on site, can eliminate multi-component spectrum cross interference, and can realize accurate quantification.

The application belongs to the technical field of industrial automatic control, and discloses a leakage rate evaluation method and system suitable for constant pressure water supply pipe network. The leakage rate evaluation method suitable for constant pressure water supply pipe network comprises the following steps: according to the characteristics of the constant pressure water supply system, using the Lagrange proportional load method and the Newton iteration method, the water consumption signal reference vector and the leakage signal reference vector are separated from the total table flow signal and the pressure signal of the pressurizing equipment; and taking the separated water consumption signal and the leakage signal as the constraint conditions, the constraint independent component analysis algorithm is combined to carry out iteration, so that the separated water consumption signal and the leakage signal are obtained. Even if the current industrial park, station and other field sections do not have automatic flow and pressure collection remote devices, the application only needs to install a common pressure gauge at the water pump during the shutdown and maintenance time, install a common water gauge at the water pump outlet pipe, and send personnel to record the reading at regular time, so that the collection cost of observation data is extremely low.

The invention provides a transformer fault diagnosis method and device. The method comprises the following steps: acquiring a first vibration signal and a first electric quantity signal of a transformer; calculating the first vibration signal by using an independent component analysis algorithm to obtain a plurality of mutually independent source signal components; calculating the correlation between each independent source signal component and the first electric quantity signal, performing physical tracing on the independent source signal components according to the spectrum characteristics and correlation of the independent source signal components, and classifying the source signal components; and respectively extracting time domain features and frequency domain features of the source signal components of different classifications, comparing the time domain features and the frequency domain features with a preset health reference model, and judging whether the transformer component corresponding to the source signal components has a fault or not. Through the mode, the collected transformer signals are decoupled, transformer fault tracing is achieved, and the false alarm rate and the missing report rate are reduced.

The invention discloses a demagnetization fault detection method and system based on high-precision torque distribution, and the method comprises the steps: calculating flux linkage training data according to a plurality of electrical parameters of a permanent magnet synchronous motor at a plurality of rotating speeds, and constructing a flux linkage distribution model; based on differential data corresponding to the flux linkage training data, establishing a data-driven iron loss compensation model, and compensating the flux linkage distribution model to obtain flux linkage mapping data; based on an independent component analysis algorithm, torque matrix distribution data composed of the flux linkage mapping data are processed, and demagnetization fault parameters are obtained; and judging whether the demagnetization fault parameter is greater than a preset signal threshold, and if so, determining that the permanent magnet synchronous motor has a demagnetization fault. Therefore, fault parameter extraction and accurate demagnetization fault diagnosis of high-precision torque distribution data and independent component analysis based on the flux linkage distribution model and iron loss compensation can be realized, and the operation reliability and maintenance efficiency of the motor are improved.

The application provides an oil and gas pipeline pulse eddy current response signal denoising method based on FastICA, which comprises the following steps: collecting signals generated during pulse eddy current detection of an oil and gas pipeline to obtain original signal data, wherein the original signal data comprises an oil and gas pipeline pulse eddy current response signal and electromagnetic noise; using an FIR adaptive filtering algorithm to pre-process the collected original signal data to obtain pre-processed signal data; using a MEMD algorithm to decompose the pre-processed signal data, and screening the decomposed components to obtain a final decomposition result; using an MDL criterion to estimate the total number of signal sources of the pre-processed signal data; and separating the oil and gas pipeline pulse eddy current response signal and the electromagnetic noise from the final decomposition result according to the estimated total number of signal sources and using a FastICA independent component analysis algorithm. The application adopting the above scheme can significantly improve the detection accuracy and provide more reliable technical support for the safety monitoring of the oil and gas pipeline.

The invention provides a business analysis method and system based on big data, and the method comprises the steps: obtaining user click stream data, payment stream data and an equipment electromagnetic interference signal; generating multi-source heterogeneous business time sequence data based on the user click stream data and the payment stream data; the device electromagnetic interference signal is converted into a device vibration frequency domain feature, a collaborative data stream is generated based on the device vibration frequency domain feature and the multi-source heterogeneous service time sequence data, and the collaborative data stream comprises a mixed service feature; decoupling the mixed service features by using an independent component analysis algorithm to obtain a flow density index and a bandwidth index; dynamically associating the traffic density index with the bandwidth index to construct a service independent feature matrix; and based on a streaming processing engine, converting the service independent feature matrix into a service feature analysis graph, and generating a decision strategy corresponding to the service feature analysis graph in real time. According to the invention, the real-time response capability of abnormal fluctuation in a high-concurrency service scene is improved.