111 results about "Nonnegative matrix factorisation" patented technology

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention belongs to the technical field of sensor error correction, and discloses a data synchronization and error correction method based on multi-source gas detection, and the method comprises the steps: firstly achieving the time-space synchronization of a multi-source sensor through the combination of GPS / Beidou hardware time service and a gas diffusion transmission model; non-negative matrix factorization (NMF) is adopted to decouple sensor cross interference, and an LSTM neural network is utilized to predict a sensor drift amount caused by environmental temperature and humidity changes; and finally, carrying out dynamic weight fusion based on the real-time confidence of each sensor, and outputting a high-precision gas concentration value. The problem of data distortion caused by time sequence dislocation, cross sensitivity and environment drift in a multi-sensor system is effectively solved, the accuracy of detection data and the reliability of the system are remarkably improved, and the method is particularly suitable for multi-component gas detection scenes in the fields of chemical engineering, environment monitoring and the like.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention discloses a Chinese and college entrance examination student learning condition diagnosis method based on multi-source data fusion, and the method comprises the following steps: S1, collecting and standardizing multi-source data of examination, behavior, psychology and the like, and constructing a unified student data set; s2, non-negative matrix factorization is adopted to extract fusion factors of all dimensions, and student feature expression is formed; s3, multi-view clustering is carried out based on the fusion factors, and student learning condition category labels are generated; s4, constructing a student portrait model according to category labels, and depicting group feature distribution; s5, comparing the individual features with the portrait, identifying abnormal differences, and generating a diagnosis conclusion; and S6, according to a diagnosis result matching rule base, outputting personalized intervention suggestions to assist teaching intervention. According to the invention, accurate learning condition diagnosis and intelligent personalized intervention driven by multi-source data are realized, and the teaching decision-making efficiency is improved.

The invention relates to the field of retrieval enhancement, in particular to a multi-view knowledge-intensive retrieval enhancement generation system and method, which are characterized in that structural vectors and semantic topics are extracted from professional corpora through principal component analysis and non-negative matrix factorization technologies, and a multi-dimensional professional view set is constructed. After a user query is received, a potential intention is identified, a view angle weight vector is generated, the query is rewritten according to the view angle weight vector, multiple groups of view angle retrieval requests are constructed, targeted document retrieval is executed, and a structured prompt input language generation model is constructed based on a view angle weight reordering result and fusion of an original query and a multi-view angle retrieval result. The method is suitable for scenes of law assistance, intelligent diagnosis, academic questions and answers and the like, so that the accuracy, the interpretation and the reliability of retrieval and generation in the complex field are remarkably improved.

The invention relates to the technical field of optical detection, in particular to a notebook computer shell injection molding part surface quality detection method and system, and the method comprises the steps: irradiating a standard part with a preset wave band according to preset parameters through an infrared spectrometer to obtain a first spectrogram, and irradiating the injection molding part with the preset wave band according to the preset parameters to obtain a second spectrogram. Through the synergistic effect of a non-negative matrix factorization algorithm and a Transform fusion model, the problem of spectrum overlapping caused by addition of a flame retardant is effectively solved, through seven-degree polynomial and smooth second derivative processing, the carbonyl peak position measurement error is further reduced, and compared with a traditional Fourier transform infrared spectroscopy, the feature parameter extraction precision is further improved. The number of the carbonyl absorption peaks and the positions of the carbonyl absorption peaks are compared with the standard threshold values twice, so that the problem that some qualified products are mistakenly considered as non-qualified products easily due to deviation during judgment of the carbonyl content of the injection molding shell of the notebook computer is further solved.

The application discloses a cell heterogeneity analysis method, device and computer readable storage medium for large-scale single cell sequencing data. The application combines kernel non-negative matrix factorization with deep neural network to construct a clustering method called KNMF-DNN. The method selects some representative subsets from the overall data, and uses the kernel non-negative matrix factorization method to cluster the subsets; then, the representative subsets are used as a training set, the remaining data is used as a test set, the labels obtained by clustering are used to classify the remaining data through the deep neural network, so that the clustering of the whole data is realized. In addition, the application also uses the idea of stratified sampling to select the most representative samples, and the best division of the clustering samples and the classification samples is determined by the KL divergence. Experimental results show that, compared with other latest methods, the KNMF-DNN has more excellent results on three real scRNA sequencing data sets.

The invention provides a vibration signal identification method and system based on waterfall plot enhancement, and relates to the technical field of signal identification, and the method comprises the steps: obtaining a to-be-identified vibration signal, and generating an initial waterfall plot; calculating a gradient matrix to construct an edge feature map, and extracting a vibration feature region; feature sub-regions are divided, and an enhanced waterfall plot is obtained through non-negative matrix factorization and nonlinear transformation; extracting scene nodes based on the instantaneous energy value, and constructing a scene topological structure to generate a vibration mode descriptor; and carrying out vibration signal identification by using a preset classifier. According to the method, the distinguishability of weak vibration signal features can be effectively improved, and the recognition accuracy is improved.

The invention discloses a thin film spectrum door lock management system and method based on data analysis, and relates to the technical field of data analysis, and the method comprises the following steps: 1, collecting same-batch intact thin film spectrum data, disturbance spectrum samples and corresponding environment parameters; step 2, carrying out denoising smoothing, baseline correction, derivative operation and normalization processing on the spectral data; step 3, extracting spectrum key features to construct vectors, combining the vectors, and decomposing the vectors into a substrate and a coefficient matrix by using a non-negative matrix; step 4, respectively calculating intact and disturbance thresholds, and constructing a regression model by using environmental parameters and coefficient offset; and 5, collecting real-time spectrum and environmental parameters, carrying out projection solution on a coefficient vector after preprocessing, and dynamically adjusting a judgment reference and carrying out decision making. The method can effectively improve the situation that in the prior art, thin film spectrum door lock management mainly depends on a fixed threshold value and is difficult to adapt to disturbance and environmental changes.

The invention relates to the technical field of learning style recognition, and provides a learning style recognition method and system based on deep learning, a medium and equipment, and the method comprises the steps: obtaining learning behavior features of a learner, obtaining embedded features through preprocessing, gradually decomposing the embedded features into non-negative matrix factorization factor features through multi-level non-negative matrix factorization operation, and obtaining a non-negative matrix factorization factor features; the embedded features and the non-negative matrix factorization factor features are fused through an adaptive attention fusion mechanism to obtain fused features; wherein the multi-layer non-negative matrix factorization operation adopts a multi-layer factorization structure, and potential factors are gradually increased layer by layer; and on the basis of the fusion features, learning styles are predicted through a deep classification network. And the learning style identification accuracy is improved.

The invention discloses a gas detection precision improving method based on a multi-algorithm fusion framework. A photonic crystal resonant cavity and a tunable band-pass filtering structure are integrated in a Fourier transform spectrometer light path; collecting a wide-spectrum light intensity signal, and constructing a spectral signal database in combination with the enhancement characteristic and the filtering characteristic of the resonant cavity; zero calibration and dynamic baseline deduction operation are carried out, and self-adaptive variational mode decomposition and wavelet transform are adopted to carry out signal denoising on the spectral signals; an environment compensation model is established, and an Arrhenius type correction factor is adopted to suppress water vapor cross interference; constructing an RLS and fuzzy control combined hybrid adaptive filter; separating aliasing spectral signals by adopting a non-negative matrix factorization algorithm; establishing a quantitative relation model; in the online concentration prediction process, the initial concentration value is subjected to recursive optimization by using a hybrid adaptive filter, the deviation is continuously corrected through an environment compensation model, and an accurate concentration value is output. The method can significantly improve the accuracy and stability of multi-gas detection.

The invention relates to the technical field of territorial space planning, and discloses a territorial space planning environmental influence monitoring method, which comprises the following steps of: performing radiometric calibration and atmospheric correction on an original remote sensing image, converting the original remote sensing image into surface reflectance, screening a heavy metal sensitive wave band, eliminating scattering noise and estimating an inter-wave band noise covariance matrix; constructing an end member spectrum library, optimizing end members through non-negative matrix factorization, inverting abundance by using an alternating direction multiplier algorithm and meeting physical constraints, and calculating covariance of unmixing residual errors; and designing a deep neural network model, and inputting abundance and reflectivity characteristics. According to the method, the timeliness of pollution diffusion early warning is improved through construction of a multi-source data collaborative treatment platform, dynamic tracking of a pollution migration path, an intelligent prediction model and a real-time calculation algorithm, the spectrum interference bottleneck of multi-metal combined pollution is broken through through a spectrum analysis method, and decision support is provided for territorial space environment treatment.

The invention provides a damping and noise-reducing installation method for electromechanical equipment, and belongs to the technical field of electromechanical equipment installing.The damping and noise-reducing installation method comprises the steps that firstly, a multi-dimensional vibration noise collection system is constructed, and comprehensive vibration noise characteristic data is collected; then extracting vibration noise features through wavelet transform and non-negative matrix factorization; constructing a small-scale model, and analyzing the influence of the installation parameters by adopting an orthogonal test method; obtaining a vibration noise variation matrix by using a singular value decomposition method; constructing a multi-objective optimization model based on the optimal installation parameters, and solving the optimal installation parameters by adopting a particle swarm algorithm; the parameter rationality is verified by applying an elastic damping dynamics equation set; matched damper materials and installation positions are selected; actual installation is carried out according to the optimal parameters, and the effect is verified; and finally, a parameter correction model is established, a complete knowledge base is formed, and accurate optimization of damping and noise reduction installation of the electromechanical equipment is realized.

The application discloses a kind of frequency spectrum aliasing wireless signal depth identification method, including the following steps: to the M frequency spectrum aliasing signal monitored by non-negative matrix decomposition, construct objective function Q1 and solve, obtain the coefficient matrix A of the kind information that the mth frequency spectrum aliasing signal contains m , signal kind consensus matrix A * ;Objective function Q2 is constructed, and A m Further decomposition is carried out, and the position and transmitting power of each signal in the frequency spectrum aliasing signal are obtained, wherein the position of signal is the grid position obtained by grid division to monitoring area.The application integrates signal identification, signal position estimation and transmitting power estimation in a depth identification framework, and multiple parameters of frequency spectrum aliasing signal are decoupled and estimated by multiple NMFs;The application can perform depth identification on frequency spectrum aliasing signal, and identify the kind, position and transmitting power of unknown signal in frequency spectrum aliasing signal.

This invention relates to the field of resource scheduling, and proposes a task scheduling optimization method and system based on dynamic task profile modeling. The method includes the following steps: real-time collection of task metadata of the target task and computing node operation data of the intelligent computing center, and standardization processing; extraction of discriminative features from the standardized task metadata; input of the discriminative features into the dynamic task profile modeling model to generate a dynamic task profile; wherein the dynamic task profile modeling model is configured with a spatiotemporally coupled tensor model for multi-dimensional representation of the target task, and an online non-negative matrix factorization algorithm for dynamically updating the feature matrix of the discriminative features; based on the standardized computing node operation data and the dynamic task profile, a Pareto optimal solution set is generated based on a multi-objective optimization algorithm, and the task scheduling optimization scheme is output.

The application provides a non-negative matrix factorization community detection method based on latent structure and attribute, and relates to the field of community network detection. The method mainly solves the problem that the existing method does not sufficiently utilize community network information. The method comprises the following steps: firstly, preprocessing community network data, and representing information in the network; calculating an adjacency matrix, an attribute matrix and a latent structure matrix; establishing a community detection model mainly based on direct topological structure and attribute information and supplemented by latent structure information; calculating an iterative updating rule; setting the number of iterations, initializing a community member matrix, two community-community matrices and the latent structure matrix, and adjusting a weight parameter; performing iteration to obtain a target matrix; and finally discovering a community according to the iteration result community member matrix.

A radar system for vital signs monitoring such as at least heart rate or breathing and a method for mitigating random body movement and external interference effects in vital signs monitoring such as at least heart rate or breathing by employing a radar system that is configured for providing in-phase and quadrature signals from reflected and received radar waves are provided. The problem of random body movement effects and external interference affecting vital signs monitoring is at least mitigated by decomposing and filtering a reconstructed displacement signal using a time and frequency analysis technique such as Short-Time Fourier Transform (STFT), followed by a Non-negative Matrix Factorization (NMF) operation. This decomposition allows the identification of time and frequency basis components containing the random body movement interference. Hence, the filtered signal can be reconstructed after removing the random body movement components, thus enabling reliable and robust vital-signs parameter estimation.

The invention discloses a fitness quality evaluation method based on myoelectricity and images, which comprises the following steps: step 1, data acquisition: the acquired data comprises original myoelectricity data with time sequence information and time sequence image data of fitness actions; step 2, preprocessing the collected data; step 3, for the preprocessed myoelectricity data, extracting myoelectricity characteristics through a sliding window by adopting a time domain intermediate frequency value model, and then decomposing a muscle activation degree matrix into a collaborative structure factor matrix and an activation coefficient matrix through a non-negative matrix decomposition algorithm; for the preprocessed time sequence image data, adopting a BlazePose algorithm to extract 33 3D articulation points in the time sequence image data and calculating the Euler angles of the articulation points so as to obtain attitude angle features; and step 4, using a support vector machine to fuse the myoelectricity features and the attitude angle features to carry out action scoring. The method can effectively improve the fitness quality of the user.

The application discloses a coal water-structure state recognition method based on terahertz spectroscopy, and relates to the technical field of material property characterization. The method prepares a characteristic state coal sample containing raw coal, collects a terahertz frequency band transmission spectrum and constructs a difference spectrum; three typical mechanism spectra of water-induced polarization absorption, structure scattering enhancement and cavity polarization nonlinearity are extracted through non-negative matrix decomposition to construct a frequency spectrum-mechanism-structure mapping model; the spectrum of a coal sample to be measured is collected and a difference spectrum is constructed, and after solving an activation weight vector, the difference spectrum is matched with characteristic samples to inverse the water-structure state thereof. The method can realize nondestructive and accurate recognition of the water-structure state of coal, adapt to disaster precursor warning such as water inrush in coal mines, and expand the engineering practical value of terahertz technology.

This invention relates to the field of power load decomposition technology, and discloses a non-intrusive power load decomposition method and system based on multimodal feature learning. The method involves: synchronously collecting power parameter data from smart meters, environmental parameter data from environmental sensors, and user equipment usage behavior data to obtain multimodal load monitoring data; extracting cross-modal features through the non-negative matrix decomposition layer of a first equipment status recognition model to obtain a multimodal fusion feature vector; identifying load patterns through the first decomposition layer of the first equipment status recognition model to obtain a first decomposed load matrix; performing clustering optimization through the second decomposition layer of the first equipment status recognition model to obtain a second load decomposition matrix; and performing dynamic fuzzy decision-making based on the second load decomposition matrix to obtain the equipment operating status identification result. This invention overcomes the limitations of traditional methods that rely solely on a single power signal, achieving high-precision and highly robust non-intrusive power load decomposition.

The present invention relates to the technical field of land and space planning, and discloses a method for monitoring the environmental impact of land and space planning, comprising the following steps: performing radiation calibration and atmospheric correction on original remote sensing images, converting them into surface reflectance, screening heavy metal sensitive bands and eliminating scattering noise, estimating the noise covariance matrix between bands; constructing an end-member spectral library and optimizing the end-members through non-negative matrix decomposition, inverting abundance using an alternating direction multiplier algorithm and satisfying physical constraints, calculating the covariance of unmixing residuals; and designing a deep neural network model, inputting abundance and reflectance features. The present invention improves the timeliness of pollution diffusion warnings by constructing a multi-source data collaborative governance platform, dynamically tracking pollution migration paths, and using intelligent prediction models and real-time solution algorithms. Furthermore, a spectral analysis method breaks through the spectral interference bottleneck of multi-metal composite pollution, providing decision support for land and space environmental governance.

This invention discloses a method for detecting the aging state of asphalt pavement by integrating hyperspectral imagery and deep learning, relating to the field of deep learning technology. The method includes: acquiring and preprocessing hyperspectral images of the target road section to obtain surface reflectance images; dynamically identifying and masking vehicles and their shadows in the images using a spectral angle matching method to extract effective pixel spectral datasets; performing hybrid pixel decomposition on the effective pixel spectral datasets using a non-negative matrix factorization algorithm to obtain asphalt endmember spectra and aggregate endmember abundance maps; inputting the asphalt endmember spectra into a pre-trained oxidation degree assessment model to output asphalt oxidation degree values; overlaying the aggregate endmember abundance map with near-infrared band images and inputting it into a pre-trained exposure rate segmentation network to output a binary mask of aggregate exposure areas and calculate the aggregate exposure rate. This method solves the technical problem that existing technologies cannot quickly and non-destructively detect the aging state of asphalt pavement.

The invention discloses a semiconductor coating nondestructive testing method for heating clothes, and relates to the technical field of nondestructive testing, and the method comprises the following steps: converting a gray scale dot matrix, a multi-wavelength spectrum dot matrix and a wave intensity dot matrix into a gray scale field, a multi-wavelength spectrum field and a wave intensity field; pre-processing the gray field, and analyzing by adopting a multi-scale spectrum analysis auto-encoder to obtain a thickness fluctuation graph; decomposing and disassembling the multi-wavelength spectral field by adopting a sparse non-negative matrix, and processing the input spectral segmentation model to obtain a skip plating probability graph; the wave intensity field is preprocessed and input into a position self-encoder, and a bubble probability graph is generated by considering position deviation analysis; and adopting a Bayesian confidence estimation algorithm, taking the probability graph as prior probability distribution, and updating and obtaining posterior probability distribution based on a Bayesian principle on the basis of causal association of process parameters, skip plating, bubbles and thickness fluctuation and combined with joint conditional probability distribution in historical data so as to determine a skip plating area and a bubble area. And nondestructive testing which considers causal association and is beneficial to traceability analysis is realized.

This invention relates to the field of biomedical image processing technology, and discloses a method and system for quantitative analysis of hypothalamic immunofluorescence images. The method includes: performing spectral unmixing on multispectral fluorescence microscopy images based on a nonnegative matrix factorization algorithm to obtain a clean signal distribution map; using Gaussian Laplace filtering and watershed transform to achieve cell detection and segmentation; performing affine and B-spline registration between slice images and standard brain atlases to generate regions of interest masks for neural nuclei; using a local background adaptive correction strategy to perform fluorescence quantification and positive determination; and calculating Pearson correlation coefficient and Manders overlap coefficient to achieve colocalization analysis. The system includes a spectral unmixing module, a cell detection and segmentation module, an atlas registration and region recognition module, a fluorescence intensity quantification module, and a colocalization analysis and statistical output module.

This invention discloses a water pollution data monitoring method and system based on multi-objective analysis, belonging to the field of water pollution source tracing technology. The method includes: collecting a GIS map of the target watershed and time series data on rainfall, flow, and water quality; hydrologically segmenting the flow series according to the opening and closing status of dams to obtain surface runoff and baseflow components; constructing a two-dimensional water quality observation matrix, setting pulse and delay constraints in conjunction with rainfall and runoff components, and using a non-negative matrix factorization algorithm with ratio constraints for source analysis to obtain a source feature matrix and a source contribution time coefficient matrix; identifying pollution source emission attributes based on the ammonia nitrogen to total phosphorus ratio and the correlation between the source contribution coefficient and rainfall in the source feature matrix, and marking them on the GIS map. This invention integrates dam-controlled hydrological segmentation with matrix factorization based on physicochemical constraints to achieve refined source tracing, improving the accuracy and interpretability of pollution source identification.

The present application relates to the technical field of computer vision, and relates to a 3D Gaussian sputtering stylization method, system, device and storage medium; wherein the 3D Gaussian sputtering stylization method comprises the following steps: obtaining a style image and a three-dimensional Gaussian scene to be stylized; performing non-negative matrix decomposition on the style image to extract an initial color basis matrix and an initial coefficient matrix of the style image; constructing a color orthogonal decoding model according to the initial color basis matrix and the initial coefficient matrix, and recalculating color parameters of each Gaussian primitive of the three-dimensional Gaussian scene; performing style transfer optimization on the Gaussian primitive, updating trainable parameters until an iteration termination condition is reached; and determining a stylized three-dimensional Gaussian scene according to updated geometric attribute parameters and color attribute parameters. The present application can solve the problems of color incoordination and dirty color during 3D Gaussian sputtering stylization.

The present invention relates to the technical field of digital audio processing, and particularly to a digital audio processing method and system for a sound system. The system includes an audio signal acquisition module, a signal analysis module, an adaptive adjustment module, a howling suppression module, and a sound effect optimization feedback module. The audio signal acquisition module collects data such as the spectrum and amplitude of the sound system in real time. The signal analysis module generates a signal analysis report based on the non-negative matrix factorization algorithm and the generative adversarial network algorithm. The adaptive adjustment module automatically adjusts audio parameters through the fuzzy logic control algorithm. The howling suppression module uses the adaptive feedback cancellation algorithm to suppress howling noise. The sound effect optimization feedback module enhances the sound effect using a Bezier curve to achieve frequency, phase, and spatial positioning optimization. Through the collaborative processing of audio acquisition, precise analysis, adaptive adjustment, howling suppression, and spatial sound effect optimization, the present invention not only improves the quality and stability of the audio, but also enhances the spatial effect and user experience of the audio.

This invention relates to the field of intelligent identification technology for swine diseases, and discloses a method for identifying and making decisions about swine diseases by integrating body temperature and image features. The method separates the independent basis components of each disease in a mixed infection by performing non-negative matrix decomposition on the body temperature time spectrum. It then uses the Hungarian algorithm to establish a cross-time point correspondence between the basis components, and uses an optimal transmission algorithm to associate lesion instances with the body temperature basis components to generate a feature sequence of disease components. Finally, it uses a time-series perceptual graph neural network to perform independent inference on the symptom evolution time-series graph, outputting the disease type identification confidence and disease stage inference confidence for each pathogen. Based on a multi-pathogen joint decision rule base, it generates a coordinated treatment plan.

This invention discloses a method for identifying the physiological state of adult product users based on multi-sensor data acquisition, relating to the fields of intelligent health monitoring and biosignal processing technology. This invention uses non-negative matrix factorization (NMF) technology to decompose multi-channel non-steady-state electromyographic signals collected from the pelvic floor and core muscle groups into multiple muscle coercive elements and their temporal activation coefficients. Each coercive element represents a fixed pattern of muscle cooperating under neural drive, while the activation coefficient reflects the change of this pattern over time. Sexual arousal, as a specific neurophysiological process, induces the activation of coercive elements with specific spatiotemporal patterns. By tracking the activation and evolution of specific coercive patterns, the neural control fingerprint reflecting sexual arousal is extracted from the original signal contaminated by motion noise. This effectively distinguishes between ordinary muscle contractions caused by device use and physical activity and autonomous neuromuscular activities related to sexual responses, overcoming the defects of susceptibility to interference and insufficient specificity.