45 results about "Time frequency decomposition" patented technology

The invention, which relates to the technical field of education recommendation, discloses a student portrait-driven education agent recommendation system based on a cognitive diagnosis map, comprising a time-frequency decomposition processing module, a weight rhythm matching module, a time sequence coupling damping module, a dynamic difference readjustment module and a time-frequency domain self-balancing control module. And the time-frequency decomposition processing module is used for establishing a time-frequency decomposition processing layer based on drifting characteristics of student portrait parameters in a time dimension, and performing energy distribution analysis on multi-source dynamic data streams from learning behaviors, test performance and emotion feedback according to time slices. Through time-frequency decomposition and non-resonance rhythm matching, dynamic coordination of weight adjustment and student portrait drifting is realized, characteristic fluctuation amplification is prevented, and the stability of a learning path is guaranteed; and a dynamic closed loop is formed through time sequence coupling damping and time-frequency domain self-balancing control, stable convergence of cognitive features is realized, and the accuracy and continuity of educational agent recommendation are improved.

The invention provides a postoperative bleeding risk monitoring method and system for a hepatobiliary patient. The method comprises the following steps: collecting real-time multi-modal data; constructing an LSTM-CNN hybrid model by using the time-frequency decomposition features, and obtaining a local tissue hypoxia index and a vasomotor function anomaly probability; the low-frequency impedance change rate and the albumin level are fused through a random forest algorithm, and the ascites occurrence probability and the effusion amount predicted value are obtained; and generating a bleeding point positioning coordinate and a thermodynamic diagram risk grade. And calculating a comprehensive bleeding risk probability and positioning a bleeding area. And generating graded early warning signals and recommending personalized treatment schemes or nursing suggestions. According to the invention, the LSTM-CNN hybrid model, the random forest algorithm and the three-dimensional convolutional neural network are adopted to deeply extract different data features, so that the limitation of single index evaluation is avoided. A causal relationship model is established through the Bayesian network, and the comprehensive risk probability calculation preciseness is improved; the early warning threshold is dynamically adjusted by combining the individual characteristics of the patient, and the traditional problem of easy misjudgment is solved.

The invention belongs to the technical field of equipment state monitoring, and discloses a relay state monitoring and diagnosis method and system. Vibration time domain signals of all monitoring points in the working process of a relay are collected, a time delay characteristic matrix is constructed through segmented cross-correlation operation, and a near-field sound source candidate area is analyzed and recognized in combination with the spatial divergence of a time delay gradient vector field. A sound-vibration coupling propagation model is introduced, near-field sound source excitation and structure conduction vibration components are distinguished, multi-component signal decoupling is realized through spatial constraint and frequency domain sparsity constraint, and a relay body vibration signal is effectively separated. The method comprises the following steps: performing time-frequency decomposition on a body vibration signal, extracting a transient impact feature of a contact action and a steady-state vibration feature of coil excitation, constructing a multi-dimensional state feature vector, and realizing state diagnosis through similarity matching with a preset feature library. According to the invention, the early fault detection sensitivity and diagnosis accuracy of the relay are improved, and a reliable basis is provided for preventive maintenance.

The invention relates to the technical field of audio signal processing, and discloses an industrial noise identification method and system, and the method comprises the steps: obtaining an audio signal, and carrying out the time-frequency decomposition, and obtaining preliminary decomposition data; classifying the coupling features of the preliminary decomposition data to obtain a coupling region boundary value; if the boundary value of the coupling region exceeds a preset boundary threshold value, performing filtering processing to obtain separated noise spectrum data; calculating an overlapping proportion of the separated noise spectrum data and a preset target sound; if the overlapping proportion causes extraction deviation, extracting dynamic change characteristics of the separated noise spectrum data and fusing the dynamic change characteristics into a spectrum extraction process to obtain an accurate noise spectrum; calculating an intensity value according to the accurate noise spectrum, and determining an intensity evaluation result; and comparing the intensity evaluation result with a preset pollution threshold, calculating a noise pollution degree and outputting a noise identification result. The method is suitable for industrial environment noise monitoring, and can accurately extract the noise spectrum.

The invention discloses a porosity real-time prediction method and system based on alkane gas and carbon isotope characteristics thereof and a storage medium, relates to the technical field of oil and gas reservoir development, and aims to solve the problem of insufficient precision in a heterogeneous reservoir due to neglect of dynamic characteristics of alkane gas carbon isotope in an existing method. The method is characterized by comprising the following steps: S100, collecting alkane gas concentration and carbon isotope data thereof in real time, and performing time-frequency decomposition on the data to obtain characteristic data on different time and frequency scales; s200, performing multi-scale feature extraction on the data to obtain multi-scale features; s300, performing feature fusion on the multi-scale features; s400, constructing a GNN network model, introducing an adaptive attention mechanism, residual connection, jump aggregation and time sequence dynamic fusion, constructing a graph structure by taking a reservoir unit as a node, and mapping a fusion feature to a graph node attribute; and S500, constructing a reservoir space structure based on the GNN network model, and combining physical constraint optimization to realize real-time prediction of the porosity.

The invention provides a fault detection and early warning method and system for an insulating layer of an oil-immersed high-voltage wiring harness, and relates to the technical field of power equipment detection, and the method comprises the steps: obtaining a multi-physical field coupling signal, carrying out the time-frequency decomposition, obtaining degradation feature distribution, extracting a feature response amplitude, calculating a gradient change rate, and recognizing an abnormal feature cluster; deducing residual life distribution of the insulating medium according to the degradation rate and generating a risk index; according to the risk index, the early warning level and the intervention time window are determined, early detection and early warning of the fault of the insulating layer of the oil-immersed high-voltage wire harness can be realized, and the operation safety of power equipment is improved.

The application relates to the technical field of analytical chemistry detection, and specifically discloses a rare earth ion chromatographic online analysis and detection method and system, which adopts a chromatography-mass spectrometry combined system, realizes multi-scale time-frequency decomposition of signals by constructing an adaptive wavelet base function library, accurately identifies and classifies interference types in combination with a convolutional neural network and vacuum degree coupling analysis; a U-Net generator with an attention mechanism and a multi-scale discriminator are designed for signal repair aiming at repairable interference; a state transition model containing a mass-to-charge ratio database is established, empirical mode decomposition and adaptive filtering technology are used to realize dynamic calibration of a mass axis; quantitative accuracy is ensured through double internal standard correction and a triple verification mechanism; when unrepairable interference is detected, a hierarchical self-checking program is started, and fault diagnosis is carried out in combination with spectrum fingerprint analysis.

This invention discloses a bearing remaining life prediction method based on frequency domain degradation perception. It constructs an integrated prediction system through a time-frequency decomposition dual-branch feature extraction module, a frequency domain degradation perception weight allocation module, and a life prediction loss function optimization module. First, the time-frequency decomposition dual-branch feature extraction module decomposes the original time series into high and low frequencies and inputs it into an sLSTM and mLSTM dual-branch structure for feature extraction. Second, the frequency domain degradation perception weight allocation module performs degradation perception processing on the fused features, generating dynamic weights and modulating the features to obtain the final fused features, which are then mapped to the prediction space. Finally, the life prediction loss function optimization module calculates the loss, optimizes the model output, and obtains the bearing remaining life prediction result. This invention comprehensively covers the full-stage features of bearings from healthy to severely degraded, improves the sensitivity of early fault detection, and still possesses excellent generalization and robustness under complex operating conditions.

The invention discloses an AI large model-based atmospheric water vapor content prediction method, system and device. The method comprises the following steps of: obtaining atmospheric water vapor content data and related meteorological variable data; performing time-frequency decomposition on the atmospheric water vapor content data and the related meteorological variable data by using wavelet transform, and extracting a multi-scale feature subsequence; constructing an AI large model based on a long short-term memory network, and inputting the extracted multi-scale feature subsequences into the large model for multi-factor nonlinear training; and performing space-time prediction on the future atmospheric water vapor content by using the trained large model to obtain a prediction result. Through the WT-LSTM mixed large model, the interannual prediction R2 is improved by 36%, the RMSE is reduced by 56.52%, the nonlinear bottleneck of a traditional model is solved, and high-precision space-time prediction is provided.

The application provides a multi-directional stress prediction method, device and equipment in a milling cutter milling process and a storage medium. It relates to the field of numerical control machine tool processing digital twin technology. The method comprises: obtaining small sample experimental data based on an orthogonal test method, time-frequency decomposition of the milling force test signal, and extraction of multi-dimensional features of the milling force dynamic characteristics; analyzing the correlation between the processing parameters and the features, screening the key features, establishing a physical mapping model of the process parameters to the key features and solving the cutting coefficients; constructing a time-varying signal prediction model based on a recurrent neural network, predicting the multi-directional dynamic milling force of the milling cutter with the key features in the small sample test data; and based on the cutting coefficient, designing an adaptive filter to post-process and optimize the predicted signal and inverse normalize it, and output the final prediction value. Based on small sample data, the application can accurately predict the multi-directional dynamic stress of the milling cutter only with the process parameters, and effectively improve the virtual-real mapping and dynamic optimization capability of the processing process.

The application discloses a CNN-LAE-LSTM hybrid network centrifugal pump cavitation diagnosis method and device, comprising: collecting different degree cavitation data, including cavitation pressure signal and cavitation visual image; performing Kalman filtering and modal time-frequency decomposition on the cavitation pressure signal; performing gray scale processing on the cavitation visual image; combining the processed cavitation data to form a training set and a test set; constructing a CNN-LAE-LSTM-based centrifugal pump cavitation diagnosis model, wherein the CNN is used for extracting local key features of the cavitation pressure signal and the cavitation visual image; the LAE is used for feature dimension reduction, redundancy elimination and noise suppression; the LSTM is used for capturing signal time sequence correlation and adapting to pump operation dynamic characteristics; the training set is used for training the centrifugal pump cavitation diagnosis model, and the test set is used for verifying the model accuracy; and the to-be-tested data is input into the trained centrifugal pump cavitation diagnosis model to obtain a fault diagnosis result.

This invention discloses an activity pattern recognition method and system based on wavelet transform and CNN / Transformer. First, wavelet transform is used to perform multi-scale time-frequency decomposition on the original signal to enhance the perception of subtle local features and high-frequency components. Then, convolution operations are used to further extract and fuse effective features in the time-frequency domain. Finally, leveraging the self-attention mechanism of Transformer, global dependencies are captured in the enhanced feature sequence, thereby achieving efficient collaborative modeling of local information and global context. This invention overcomes the insensitivity of Transformer models to local details and high-frequency information, effectively avoiding the neglect or smoothing of crucial subtle action patterns and transiently changing high-frequency signals during recognition, significantly improving the model's recognition accuracy and enhancing the reliability and interpretability of the entire system in practical applications.

The application provides a brain-computer interface cognitive function decline monitoring system, method, device and medium, which can be applied to the field of electroencephalogram signal analysis. The brain-computer interface cognitive function decline monitoring system comprises a signal monitoring module, a signal processing module, a result processing module and a medium. The signal monitoring module is used for collecting multi-channel electroencephalogram signals in multiple time periods. The signal processing module is used for performing time-frequency decomposition and global field power determination on the multi-channel electroencephalogram signals. The probability of the multi-channel electroencephalogram signals belonging to each microstate mode is determined, and a microstate feature vector is generated. According to the phase locking value of the electroencephalogram signals between nodes and the similarity between the microstate feature vectors corresponding to the nodes, a topological heterogeneous brain network is generated. The topological heterogeneous brain network is subjected to graph feature extraction and state classification to obtain an analysis result. The result processing module is used for generating a brain load change trend according to multiple analysis results, and generating an adjustment suggestion based on the brain load change trend.

The application discloses a power grid oscillation risk scene sample generation method, system, device and medium, and particularly relates to the technical field of oscillation scene generation, and the technical points are as follows: a time-frequency fusion generative adversarial network is constructed, the time-frequency fusion generative adversarial network comprises at least one generator and at least one discriminator; wherein the generator comprises a time-frequency decomposition module, a time domain generation module, a frequency domain generation module and a U-Net module; a random noise vector is input into the time-frequency decomposition module to obtain a time domain signal and a frequency domain signal; the time domain signal is input into the time domain generation module, and the frequency domain signal is input into the frequency domain generation module to generate a time domain waveform and a frequency domain feature respectively; the time domain waveform and the frequency domain feature are input into the U-Net module to generate a time-frequency fusion power grid oscillation signal; the generated power grid oscillation signal and an original real oscillation signal are input into the discriminator, and the power grid oscillation signal is iteratively optimized and trained in combination with a loss function to obtain a power grid oscillation scene sample.

A time-frequency aligned EEG signal analysis method, apparatus, device, and medium are disclosed, aiming to consider and reduce the temporal and frequency distribution differences of EEG signals, thereby more effectively improving the model's generalization ability across different subjects or experimental sessions. The method involves time-frequency decomposition of the EEG signal, calculating global statistics in each frequency band, and performing whitening transformation to calibrate the signal. Finally, the multi-channel EEG signal is reconstructed to obtain a time- and frequency-aligned multi-channel EEG signal. This invention's time-frequency alignment process comprehensively reduces the distribution differences between EEG data from different sources in both time and frequency dimensions. Compared to methods that only align the time domain, it more effectively improves the model's performance on new user or new session data, thus more effectively enhancing the model's generalization ability across different subjects or experimental sessions.

The invention relates to the field of reaction kettle stirring blade arrangement optimization, and discloses a reaction kettle stirring blade arrangement optimization method and system based on CFD fluid simulation, and the method comprises the steps: obtaining the structure and working condition data of a reaction kettle, carrying out the long-time-scale transient CFD simulation, separating a low-frequency flow field time sequence, obtaining a precession grid field, constructing a precession voxel octree, and carrying out the long-time-scale transient CFD simulation; paddle layer local vortex structure segmentation and time-frequency decomposition are executed based on a precession voxel octree to obtain a paddle layer local vortex shedding mode set, an interlayer coherent vortex atlas tree is constructed, and a stirring paddle arrangement multi-target optimization model is established based on the interlayer coherent vortex atlas tree to solve and obtain a candidate paddle arrangement solution set. Generating a stirring paddle mounting scheme, and performing high-fidelity verification and simulation in combination with a result to form an arrangement correction rule; according to the method, the problem that low-frequency large-scale circulation precession cannot be captured by the traditional steady-state CFD is solved, the arrangement optimization of the stirring blades is realized, and the batch stability and the product consistency in the reaction process are improved.

The invention discloses a lightning arrester resistive current prediction method and system, and relates to the technical field of lightning arrester current prediction, the system is composed of a plurality of function modules, and the system comprises a multi-source data acquisition module used for acquiring lightning arrester leakage current, environmental parameters and electrical parameters; wherein the environmental parameters comprise temperature, relative humidity and atmospheric pressure; the data processing and feature mining module is used for performing time-frequency decomposition on the leakage current signal by adopting an improved CEEMDAN algorithm, constructing a multi-physical field feature correlation matrix, acquiring the environmental parameters and the electrical parameters, screening out IMF components and environmental factors which are strongly coupled with resistive current, and verifying, correcting and outputting a high-quality feature set through a physical constraint isolated forest; according to the improved CEEMDAN algorithm, CEEMDAN is introduced into empirical mode decomposition, and adaptive Gaussian white noise is added into an original leakage current signal; and the hybrid model construction and optimization module is used for constructing a hybrid architecture to perform physical dimension pre-mapping on the input high-quality feature set.

The invention belongs to the field of fault diagnosis, and particularly relates to a bearing single-source-domain generalization fault diagnosis method based on wavelet adaptive causal decoupling, and the method comprises the steps: carrying out the time domain decomposition and adaptive feature extraction of a source domain sample based on a training set and a test set, and obtaining a global feature; in the second convolutional neural network, introducing a causal decoupling strategy based on a binary mask, and displaying and decoupling the extracted global features into causal features and non-causal features; and performing second convolutional neural network training, establishing a fault classifier, and determining causal purification loss based on the fault classifier. In the feature extraction stage, multi-scale time-frequency decomposition of mechanical vibration signals is achieved by adaptively optimizing wavelet filter parameters, low-frequency stable information and high-frequency impact features can be effectively separated, the stability and discrimination of feature expression can still be kept even in a strong-noise and multi-component coupling environment, and the method is suitable for large-scale popularization and application. Therefore, the accuracy and robustness of fault feature extraction are remarkably improved.

The invention provides a high-frequency transient response island detection method and device, equipment and a storage medium, and belongs to the field of island detection. The method comprises the following steps: acquiring high-frequency transient voltage and current waveform data, and performing time-frequency decomposition on the data to obtain a time-frequency coefficient matrix containing voltage and current wavelet packet coefficients; calculating a transient energy characteristic and a high-frequency transient impedance angle characteristic based on the matrix; inputting the transient energy characteristics into an energy identification model to obtain an energy classification confidence coefficient; inputting the high-frequency transient impedance angle characteristics into a transient impedance angle identification model to obtain an impedance angle classification confidence coefficient; and the decision model fuses the two confidence coefficients to generate a final island state judgment result and a power grid instability early warning signal. According to the invention, the detection blind area under power balance is overcome by using dual features. Multi-dimensional features are fused, and maloperation and refusal operation are avoided; and a passive detection method does not need to inject disturbance, so that the electric energy quality is guaranteed, and the safety and reliability of a power grid are improved.

The invention provides a sound source positioning method and system based on a multi-microphone array, and the method comprises the steps: carrying out the preprocessing of a first sound source signal collected by a microphone array, and outputting a second sound source signal and an acoustic physical parameter; performing time-frequency decomposition on the second sound source signal to obtain a plurality of first time-frequency regions; performing acoustic coherence analysis on the first time-frequency region according to the acoustic physical parameters to obtain a time-frequency coherence matrix; detecting a second time-frequency region dominated by a single source from the time-frequency coherence matrix based on sound wave multipath and diffraction characteristics, and constructing an output single-source time-frequency point index according to the second time-frequency region; generating a plurality of direction-of-arrival estimation values according to a preset frequency domain graph signal and the single-source time-frequency point index; and performing clustering and weighted fusion on the plurality of direction-of-arrival estimated values to obtain direction-of-arrival information of the sound source.

The application discloses a kind of intelligent interactive diagnosis methods based on transformer substation multidimensional information cloud computing power fusion, belong to fault diagnosis field.This application is applied to cloud computing power platform, first receive the high-frequency waveform data of transformer substation auxiliary power supply system and operating remote signaling data, according to load start-stop event mark positioning start-stop event window, and the waveform data in window is carried out multi-scale time-frequency decomposition to generate two-dimensional time-frequency spectrum;After that, the atlas is compared with the template by structure similarity, and the degradation index of each load is generated and displayed.Then receive the detection instruction of selected target load by user, trigger to the branch cable of this load and inject preset square wave pulse and collect reflected waveform data.Finally, the cloud computing power platform determines the polarity information of the reflected waveform data to obtain the defect property information, and according to the defect property information and the degradation index, the final fault diagnosis information is determined and displayed in the diagnosis rule knowledge base, which can improve the reliability and accuracy of diagnosis.

The invention provides an epilepsy clinical nursing early warning method and system, and relates to the technical field of medical information processing, the epilepsy clinical nursing early warning method comprises the following steps: obtaining multi-modal physiological signal data of an epilepsy patient, the multi-modal physiological signal data comprising electroencephalogram signal data and limb movement signal data; performing principal component analysis processing on the electroencephalogram signal data to obtain dimensionality-reduced electroencephalogram characteristic data; performing time-frequency decomposition on the dimensionality-reduced electroencephalogram characteristic data to obtain energy distribution of a plurality of frequency bands; calculating an information entropy value based on the energy distribution of the plurality of frequency bands; according to the information entropy and the limb movement signal data, the current state of the epileptic is judged through a decision tree model; determining a corresponding hidden danger level according to the current state and generating early warning information; according to the method, electroencephalogram artifacts are eliminated through principal component analysis, the state of the threatened period is recognized before epileptic seizure, and the problems of early warning lag and unstable response in the prior art are solved.

The present application relates to a rail transit catenary power supply load prediction and self-healing control method, and relates to the technical field of rail transit power supply prediction. Unfiltered high-frequency voltage signals, environmental stability indicators at corresponding time points and train position information are collected to screen stable sample data. The unfiltered high-frequency voltage signals are subjected to spatial mapping to generate fixed spatial point labels. Multi-scale time-frequency decomposition is performed to extract high-frequency structure feature vectors. A long-term feature baseline model is constructed, and a feature distance change rate is calculated to form a risk trend indicator. A spatial risk weight correction prediction load value is generated, and a self-healing control instruction is generated based on the corrected prediction load value. The dynamic coupling of structure state evolution and power supply load prediction is realized, the risk identification accuracy is improved, and the self-healing control capability of the power supply system is enhanced.

This invention proposes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing (OFDM) system, belonging to the field of non-destructive measurement. It solves the problems of balancing high resolution and anti-interference capability, and the trade-off between real-time performance and measurement accuracy. The method includes: receiving OFDM signals and performing time-frequency decomposition; decomposing the signal into multiple orthogonal subcarriers using a fast Fourier transform; dynamically disabling interfered subcarriers based on their signal-to-noise ratio (SNR); performing Bayesian phase compensation on the retained subcarriers; iteratively optimizing the subcarrier switching states and phase compensation parameters, dynamically adjusting the SNR threshold to separate signal and noise; reconstructing the sparse signal using compressed sensing technology; and improving ranging and imaging resolution using an orthogonal matched pursuit algorithm. It is mainly used in fields such as underground target detection and dynamic radar sensing networks.

The application relates to the technical field of audio signal processing, and discloses an industrial noise identification method and system.The method comprises the following steps: acquiring an audio signal and performing time-frequency decomposition to obtain preliminary decomposition data; classifying coupling features of the preliminary decomposition data to obtain a coupling region boundary value; if the coupling region boundary value exceeds a preset boundary threshold value, performing filtering processing to obtain separated noise spectrum data; calculating an overlap ratio of the separated noise spectrum data and a preset target sound; if the overlap ratio leads to extraction deviation, extracting dynamic change characteristics of the separated noise spectrum data and integrating the dynamic change characteristics into a spectrum extraction process to obtain an accurate noise spectrum; calculating an intensity value according to the accurate noise spectrum to determine an intensity evaluation result; comparing the intensity evaluation result with a preset pollution threshold value, calculating a noise pollution degree, and outputting a noise identification result.The method is suitable for industrial environment noise monitoring and can realize accurate extraction of a noise spectrum.

The invention discloses an intelligent interaction diagnosis method based on substation multi-dimensional information cloud computing power fusion, and belongs to the field of fault diagnosis. The method is applied to a cloud computing power platform, and comprises the following steps: firstly, receiving high-frequency waveform data and operation remote signaling data of an auxiliary power supply system of a transformer substation, marking and positioning a start-stop event window according to a load start-stop event, and performing multi-scale time-frequency decomposition on the waveform data in the window to generate a two-dimensional time-frequency map; and comparing the structural similarity of the atlas and the template, and generating and displaying the degradation index of each load. Then receiving a detection instruction of a user on a selected target load, triggering to inject a preset square wave pulse into a branch cable of the load, and collecting reflection waveform data; and finally, the cloud computing power platform obtains defect property information by determining polarity information of the reflection waveform data, and determines and displays final fault diagnosis information in a diagnosis rule knowledge base according to the defect property information and the degradation index, so that the reliability and accuracy of diagnosis can be improved.

This invention belongs to the field of equipment condition monitoring technology. It discloses a method and system for relay condition monitoring and diagnosis. By collecting vibration time-domain signals from various monitoring points during relay operation, a time-delay feature matrix is ​​constructed using piecewise cross-correlation calculations. Combined with spatial divergence analysis of the time-delay gradient vector field, candidate regions for near-field sound sources are identified. A sound-vibration coupling propagation model is introduced to distinguish between near-field sound source excitation and structurally transmitted vibration components. Spatial constraints and frequency domain sparsity constraints are used to decouple multi-component signals, effectively separating the relay body vibration signal. The body vibration signal is decomposed into time-frequency components to extract transient impact features of contact action and steady-state vibration features of coil excitation, constructing a multi-dimensional state feature vector. Condition diagnosis is achieved through similarity matching with a preset feature library. This invention improves the sensitivity and accuracy of early relay fault detection, providing a reliable basis for preventative maintenance.

The invention provides a method for calculating shale oil dessert attributes of multi-angle information. The method for calculating the shale oil dessert attributes of the multi-angle information comprises the steps of 1, rearranging seismic data; step 2, partially superposing the rearranged seismic data according to different azimuth angles and incident angles theta to obtain a partially superposed data volume; 3, performing frequency decomposition on the partially superposed seismic data by using a time-frequency decomposition method; 4, calculating frequency desserts according to different frequencies; 5, constructing a final dessert attribute AtrF; and step 6, calculating the AtrF value of the shale oil reservoir in the whole work area, wherein the AtrF value is used for oil and gas identification of the shale oil reservoir. According to the method for calculating the shale oil dessert attribute of the multi-angle information, the possibility of shale oil reservoir identification can be accurately calculated, the description precision of the shale oil reservoir dessert attribute can be improved, and the oil and gas identification capability of the shale oil reservoir can be enhanced.

The application discloses a rotating machinery fault diagnosis method based on dynamic adaptive continuous wavelet fuzzy entropy, and relates to the technical field of mechanical fault diagnosis. The method first collects a rotating machinery vibration signal and pre-processes, carries out second-order dynamic change enhancement processing on the signal, highlights fault impact features and suppresses low-frequency interference; then adaptively determines a continuous wavelet transform decomposition scale number according to a spectrum entropy of the enhanced signal, and completes multi-scale time-frequency decomposition; subsequently, calculates fuzzy entropy of each scale wavelet component, and splices to form a feature vector; finally, inputs the feature vector into a random forest classification model to complete fault recognition. The application combines dynamic change enhancement with adaptive scale decomposition, improves fault feature extraction precision of non-stationary and noisy vibration signals, gets rid of the limitation of artificial parameter setting, improves stability and generalization ability of the diagnosis method, and is suitable for efficient and accurate diagnosis of multiple types of rotating machinery faults.

The invention discloses a polishing track intelligent control method and system based on multi-sensor fusion. According to the method, a track displacement sequence is collected, frequency domain analysis is carried out, spectrum distribution characteristics are extracted, and high-frequency fluctuation is identified to determine a dynamic abnormal section; performing time-frequency decomposition on the abnormal segment data, extracting deviation direction and amplitude, and forming a fluctuation feature set; extracting dominant fluctuation characteristics in combination with historical trajectory data, analyzing the difference between the current trajectory and the historical trajectory, and predicting and optimizing trajectory adjustment parameters of the next period; and generating a control instruction according to the correction parameter, performing simulation execution, integrating fluctuation distribution and processing time constraint based on a simulation result, and outputting a final track adjustment scheme. According to the method, multi-source sensing, dynamic recognition and intelligent correction of the polishing track are achieved, and the real-time performance and the machining precision of track control are remarkably improved.