409 results about "Wavelet thresholding" patented technology

The invention discloses a composite power quality disturbance identification method based on a time-frequency feature fusion classification network. The method comprises the following steps: carrying out noise reduction processing on a noise-containing disturbance signal based on self-adaptive noise complete mode decomposition in combination with a wavelet threshold method; obtaining frequency domain data of the noise reduction signal based on fast Fourier transform; based on a TCN-Transformer deep network, deep feature extraction is carried out on the denoised signals in parallel from a time domain and a frequency domain; and carrying out weighted fusion on the extracted signal time domain and frequency domain features based on a cross attention mechanism to finish classification and identification. The method is simple in principle, the average recognition accuracy rate in a 30dB noise environment can reach 97.86%, and compared with other mainstream deep learning recognition models, the method shows better recognition precision, recognition stability and generalization ability.

The invention relates to the technical field of production debugging control, and discloses a production debugging control method and system for a plastic container. The method comprises the following steps: arranging a plurality of different sensors in plastic container blow molding equipment, and simultaneously collecting blow molding process parameter data; performing wavelet threshold denoising and anomaly identification on the blow molding process parameter data to obtain a process parameter anomaly identification result; establishing a transfer function matrix between the process parameters and quality indexes according to the process parameter anomaly identification result, and obtaining a target process parameter set through multi-target optimization; and inputting the target process parameter set into a double-integral enhanced recurrent neural network for parameter regulation and control calculation to obtain a process parameter adjustment amount. According to the method, early detection and accurate positioning of process abnormity in the blow molding process are realized, the quality fluctuation risk and the defective product rate are greatly reduced, and the technical problem that different response characteristic parameters are difficult to coordinate is solved.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

The invention discloses a new energy station harmonic emission level evaluation method and system based on recording data, and the method comprises the steps: carrying out the wavelet threshold de-noising processing of the recording data of line current and bus voltage in a power grid, and then carrying out the Fourier analysis, and obtaining the amplitude and phase angle of each harmonic current and voltage; on the basis of the amplitude and phase angle of each harmonic current and voltage, the harmonic order to be analyzed is determined through the harmonic voltage content rate and the harmonic distortion rate, an abnormal bus is screened as a concerned bus, and suspicious harmonic source users in the power grid are identified; establishing an equivalent model of the power grid, and solving harmonic transfer impedance between each suspicious harmonic source user and the concerned bus in the equivalent model by using a blind source separation algorithm; and quantifying the harmonic responsibility of each suspicious harmonic source user to the concerned bus. According to the invention, the accuracy of harmonic evaluation and the accuracy of tracing are improved, and the improvement of the electric energy quality of a new energy station and the stable operation of a power grid are facilitated.

A leakage sound signal denoising method based on a combination of optimized VMD and an improved wavelet threshold, for use in solving the problem in existing noise processing methods of low identification accuracy in processing leakage sound signals of water supply pipe networks. The present invention comprises: acquiring leakage sound signals of a real water supply pipe network, and analyzing noise components and ranges of the leakage sound signals; on the basis of a goshawk optimization algorithm, performing parameter optimization on the number K of decomposition modes and a penalty factor α of VMD to obtain optimal parameters, and using the optimal parameters to construct a variational model; using the variational model to decompose the leakage sound signals to obtain a plurality of intrinsic mode components; using a correlation coefficient method to screen the plurality of intrinsic mode components to obtain high-frequency components and low-frequency components; performing wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; and reconstructing the low-frequency components and the denoised high-frequency components to obtain denoised leakage sound signals. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the identification accuracy is improved.

The invention provides a multi-index self-adaptive fusion grading early warning method for coal rock gas dynamic disasters. The method comprises the following steps: (1) multi-source data collaborative preprocessing: collecting multi-source data, identifying and marking abnormal values by using a semi-supervised collaborative training model based on xLSTM, denoising acoustic emission and electromagnetic radiation signals through a PSO-wavelet threshold-OPTICS model, and interpolating missing values in real time through a BayOTIDE model; (2) multi-disaster feature fusion and risk prediction: inputting the preprocessed data into an MTAFM model, processing different data by a core expert module and an auxiliary expert module respectively, and dynamically fusing features through a gating unit to generate a risk probability; and (3) dynamic grading early warning and composite disaster judgment: determining an early warning threshold value based on Bayesian dynamic optimization, grading by combining with a quantile method, and judging coal rock and gas dynamic disasters as composite disasters during early warning. The method is high in adaptive data preprocessing capability, accurate in multi-index fusion early warning and reliable in dynamic grading early warning, and the dependence on professionals is reduced.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention relates to the field of image processing, in particular to an automobile part processing burr detection method, which comprises the following steps of: positioning an automobile part, acquiring an edge image to define an ROI (Region of Interest), performing sub-pixel extraction on an edge in the ROI, and performing multi-scale decomposition by applying discrete wavelet transform to obtain detail coefficients. The fractal dimension of the edge local contour is calculated, the expected detail energy of the edge local contour under the corresponding wavelet scale is deduced, then the adaptive threshold adjustment amount is obtained through calculation, the final local adaptive threshold is generated in combination with the basic threshold, and the threshold is used for judging the actual detail coefficient to accurately position burrs. And then carrying out feature quantification on the positioned burrs. And finally, comparing the quantized burr characteristics with a preset quality standard, judging whether the part is qualified or not, and outputting a result. According to the method, the wavelet threshold is adaptively adjusted by introducing fractal features, so that the accuracy and robustness of burr detection under different edge conditions are improved.

The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention discloses a high-voltage power switch fault diagnosis method based on wavelet threshold correction and noise reduction. Acquiring a voltage signal of the high-voltage power switch by using a sensor, and performing synchronous sampling; a wavelet threshold value correction noise reduction method is adopted to carry out noise reduction processing on the collected signals, wavelet detail coefficients are calculated through multi-scale decomposition, a threshold value is adaptively corrected based on the peak sum ratio, and the noise removal effect is optimized; thirdly, performing normalization processing on the denoised signal, mapping the signal to a polar coordinate system, constructing a two-dimensional Gramer angle field containing an included angle cosine value and amplitude information, and realizing time sequence-space conversion of the signal; and finally, generating two-dimensional image data of the voltage signal of the high-voltage power switch, and providing feature input for subsequent state evaluation and fault detection. According to the method, wavelet transform and two-dimensional feature mapping are combined, noise interference can be effectively reduced, the signal distinguishability and the information retention capacity are improved, and the method is suitable for state monitoring and intelligent diagnosis of a power system.

The invention discloses a flying dust noise monitoring data intelligent analysis method based on deep learning, and the method comprises the steps: obtaining initial multi-source monitoring data, carrying out the noise reduction of flying dust data in the data through employing a wavelet threshold value, carrying out the noise reduction of non-environmental interference in the data through adaptive frequency band filtering, and carrying out the noise reduction of the non-environmental interference in the data; the method comprises the following steps: extracting multi-scale time sequence features by using a 1D-CNN (Convolutional Neural Network) to establish a flying dust branch, extracting long-range frequency spectrum dependence through a Transform encoder to establish a noise branch, and performing cross-modal feature interaction through an attention fusion mechanism to obtain a double-flow deep neural network model; inputting the noise reduction monitoring data into a model for identification, and outputting an event classification probability and a decision factor; and performing intelligent early warning according to an evaluation result. The recognition accuracy of complex environment events is effectively improved, and the recognition accuracy of construction dust raising events is improved.

The invention relates to the technical field of medical rehabilitation monitoring, in particular to a rehabilitation cloud platform system for cardiopulmonary function monitoring. According to the system, an intelligent monitoring terminal collects physiological data; the cloud computing platform adopts wavelet threshold denoising and an index moving average method to remove noise, combines dynamic time warping to achieve multi-physiological signal time sequence alignment, constructs a double-flow model, generates a health state index and a comprehensive health risk index, introduces a multi-scale branch and differential attention mechanism, and achieves the multi-scale health state index and the comprehensive health risk index. High-precision anomaly detection is realized through prediction deviation analysis, multi-target reinforcement learning is adopted to balance health improvement and risk control, and a personalized training scheme is generated; the remote medical support module supports a doctor to monitor patient data in real time and intervene in an adjustment scheme; the user interaction end provides a real-time monitoring data visualization and individuation rehabilitation plan. Through multi-modal data fusion, time sequence synchronous optimization and adaptive reinforcement learning, accurate monitoring, dynamic rehabilitation optimization and remote medical collaboration are realized.

The invention relates to an air coupling ultrasonic signal processing method based on a COA-VMD combined improvement wavelet threshold, and the method specifically comprises the following steps: 1, employing a meta-heuristic algorithm Coati, and introducing a minimum envelope entropy as a fitness function of the algorithm, and taking the minimum envelope entropy as an evaluation criterion of an optimal solution; 2, decomposing a noisy part in the air coupling ultrasonic signal into a plurality of intrinsic mode function components by using the optimized VMD decomposition parameters; and 3, on the basis of the step 2, distinguishing an effective ultrasonic signal component f1 and a noise component f2 in the detected IMF components by adopting a correlation coefficient method. And step 4, processing the noise component f2 by adopting an improved wavelet threshold function algorithm to obtain a noise-reduced air coupling ultrasonic signal. According to the method, COA-VMD and an improved wavelet threshold noise reduction method are combined, the signal-to-noise ratio is effectively improved, and a technical basis is provided for high-precision detection of defects in the honeycomb sandwich structure.

The invention relates to the field of power distribution network ground fault accurate judgment, and discloses a power distribution network ground fault accurate positioning method based on traveling wave detection and artificial intelligence, which comprises the following steps: (a) collecting a power distribution network traveling wave voltage signal through a multi-node synchronous sensor array, and generating a preprocessing signal through baseline correction and wavelet threshold denoising; (b) constructing a Gaussian random field model of line resistance, inductance and capacitance, and simulating a traveling wave propagation process in combination with a nonlinear wave equation; and (c) defining Finsler measurement on the traveling wave signal manifold, and separating the aliasing mode by minimizing path integration. According to the invention, the technical scheme of multi-node synchronous signal acquisition and self-adaptive preprocessing is adopted, so that the effect of eliminating coupling of power frequency interference and high-frequency noise is achieved. Compared with a scheme depending on a single sensor and a fixed filtering threshold in the prior art, the method solves the defect that the wave head time difference extraction is not accurate due to asynchronous sampling.

The invention discloses a turning signal noise reduction method based on an improved wavelet threshold, and the method comprises the steps: carrying out the multi-layer wavelet decomposition of a noise-containing turning signal, obtaining the low-frequency and high-frequency wavelet coefficients of each layer, and processing the high-frequency wavelet coefficients of each layer through an improved threshold function, thereby achieving the noise reduction of the turning signal. And reconstructing a signal through wavelet inverse transformation of the processed wavelet coefficient to realize denoising. The optimal number of decomposition layers required by a sampling signal is determined according to noise power, and finally, an optimal wavelet basis is determined by taking a signal-to-noise ratio, a mean square error and the like as evaluation indexes. By means of the improved wavelet threshold function, on one hand, the signal denoising effect can be achieved, and on the other hand, the situation that details are damaged due to excessive noise reduction can be avoided. Experimental data show that the improved threshold function effectively suppresses random errors, the signal-to-noise ratio is improved, the root-mean-square error is reduced, and the denoising effect is good.

The invention relates to the technical field of signal processing, and discloses a power equipment partial discharge signal denoising method and system, and the method comprises the steps: S1, generating an exponential decay type pulse signal simulating partial discharge, and superposing white noise and narrow-band interference into the exponential decay type pulse signal to generate a noisy signal; s2, decomposing the noisy signal into a plurality of intrinsic mode components through an ensemble empirical mode decomposition method, calculating a correlation coefficient and a kurtosis index of each intrinsic mode component, and identifying a noise component and a signal component based on the correlation coefficient and the kurtosis index; s3, wavelet threshold denoising is carried out on the noise components, and a wavelet threshold is dynamically adjusted for each noise component through a particle swarm optimization algorithm; and S4, reconstructing the de-noised noise component and the reserved signal component into a final de-noised signal, and evaluating the de-noising performance based on the signal-to-noise ratio, the root mean square error, the peak signal-to-noise ratio and the correlation coefficient.

The invention relates to the technical field of signal processing, and provides an electromyogram data-based muscle movement analysis method, device and equipment and a storage medium, the electromyogram data-based muscle movement analysis method comprises the following steps: using an adaptive filter to pre-process original electromyogram signals, and dynamically filtering noise signals; segmenting the electromyographic signal after the noise signal is filtered by using a sliding window, calculating the energy of the signal in each window, and detecting the zero-crossing rate of the signal in each window; performing wavelet threshold denoising on the original electromyographic signals to obtain wavelet domain features; fusing the signal energy, the zero-crossing rate and the wavelet domain features to form a feature vector; and inputting the feature vector into a pre-trained classification model for analysis to obtain a muscle motion state. The signal energy, the zero-crossing rate and the wavelet are fused with the features for analysis, real-time effective analysis of the muscle motion state is achieved, and the accuracy of muscle motion state analysis under the dynamic condition is improved.

The invention relates to a method for predicting the bearing capacity of a concrete-filled steel tube yielding lagging jack, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring stress data at a key stress position of a concrete filled steel tube arch solid structure; stress data screening is carried out, and a screened data set is stored in an HDF5 hierarchical format; denoising the stored stress data by adopting a self-adaptive normalization method and a wavelet threshold function; time domain-frequency domain feature fusion is carried out on the stress data after denoising normalization through dynamic time segmentation and short-time Fourier transform; constructing a concrete-filled steel tube yielding arch bearing capacity prediction model, inputting the fusion feature vector into the model, and training the model to obtain a trained model; and processing the collected data, inputting the processed data into the trained model to obtain a predicted value of the bearing capacity, and triggering early warning when the predicted value exceeds 90% of the designed bearing capacity for three continuous times. The prediction capability of the model can be improved.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to the technical field of tunnel construction, in particular to a blasting vibration signal noise reduction method, computer equipment and a medium, and the noise reduction method comprises the steps: carrying out the adaptive optimization of key parameters of a VMD through a frost ice optimization algorithm, and obtaining an optimized VMD; using the optimized VMD to decompose the actually measured signal of the tunnel blasting vibration to obtain a plurality of intrinsic mode components; calculating the variance contribution rate of each intrinsic mode component, and removing the components of which the variance contribution rates do not exceed 1%; using a wavelet threshold to carry out noise reduction on the residual intrinsic mode components; and reconstructing the intrinsic mode component after noise reduction to obtain a tunnel blasting vibration signal after noise reduction. According to the method, the VMD parameters are optimized through the frost ice optimization algorithm, the tunnel blasting vibration signals are subjected to noise reduction in combination with the wavelet threshold value, the good signal noise reduction effect is achieved, meanwhile, the modal aliasing phenomenon during signal decomposition is effectively restrained, and the problem that key parameters need to be valued depending on experience in the VMD is solved.

The invention relates to the technical field of electric power system control, and discloses an electric power system arc harmonic collaborative suppression and intelligent closed-loop control system, which comprises the steps of: acquiring sensor data and uploading the sensor data to an edge computing unit by deploying a sensor; wavelet threshold denoising and electromagnetic interference compensation are adopted; the method comprises the following steps: constructing a coupling model containing arc plasma dynamic resistance, optimizing VMD parameters through PSO, extracting a joint feature vector, filling an arc and harmonic dynamic coupling analysis gap, calculating a total harmonic distortion rate and arc extinction time in combination with a power grid damping ratio, solving an optimal SVG trigger angle, and solving the problem of lack of power grid stability evaluation; correcting the opening time of the circuit breaker, calculating the error of the total harmonic distortion rate and the error of the arc extinction time, and adjusting the SVG trigger angle; calculating the precision of the evaluation model, calculating the full life cycle through the aging coefficient, analyzing and judging whether an early warning signal is generated or not, and triggering equipment replacement early warning based on the generated early warning signal.

The invention discloses a mine water storage layer leakage risk early warning and emergency decision intelligent system, which is characterized in that the system acquires osmotic pressure gradient, microseismic events, tracer migration rate and rock stratum displacement data in real time through distributed sensors, and generates a standardized multi-parameter data set through processing such as wavelet threshold denoising and variation mode decomposition; outputting a leakage probability value P and a potential fracture azimuth angle theta by using a fuzzy neural network model; early warning in three levels according to the P value, wherein Plt is greater than or equal to 0.3; when 0.6, regulating and controlling pore pressure, wherein 0.6 < = Plt; when P is larger than or equal to 0.85, sampling is encrypted, a grouting path is generated, and when P is larger than or equal to 0.85, an optimal evacuation path is calculated; constructing a grouting pressure gradient field according to the theta and the early warning grade, and dynamically matching the ratio of the leaking stoppage material; and online updating of model parameters is realized through closed-loop control. The system realizes multi-physics field coupling monitoring and dynamic adaptive decision making, and improves leakage risk assessment accuracy and emergency response efficiency.

A fiber optic gyroscope temperature drift modeling and real-time compensation method is provided, and the specific steps are as follows: Step 1: Design a fiber optic gyroscope temperature test, and collect the fiber optic gyroscope output value and the fiber optic gyroscope internal temperature sensor data within the full temperature range of ‑30° to 70°; Step 2: Perform wavelet threshold denoising preprocessing on the fiber optic gyroscope output obtained in step 1; Step 3: Establish a fiber optic gyroscope temperature drift segmented compensation model F with temperature change and temperature gradient as independent variables and the fiber optic gyroscope output signal after wavelet threshold denoising preprocessing as the dependent variable; Step 4: Calculate the coefficient of the temperature compensation model F by the least squares method and compensate the fiber optic gyroscope output in real time. The present invention meets the requirements of fast calculation speed and simplicity in engineering, meets the requirements of real-time compensation in engineering, and can effectively solve the problem of fiber optic gyroscope temperature drift, thereby improving the accuracy of the fiber optic gyroscope.

The invention relates to the technical field of image processing, and discloses a gynecological cell morphology intelligent identification method and system based on machine vision, and the method comprises the steps: collecting an original image of gynecological cells, carrying out the color normalization processing of the collected original image, carrying out the denoising processing through employing a wavelet threshold value, and carrying out the image enhancement through employing adaptive histogram equalization; according to the method, color consistency errors of different batches of dyed images are reduced, noise introduced in the image acquisition process is eliminated, cell structure details are reserved, and the accuracy of diagnosis is improved. And then image enhancement is carried out, the contrast ratio of cell nucleuses and cytoplasm is improved, so that the characteristics in the cells are more obvious, segmentation can be more accurately completed during subsequent image segmentation, the lesion level of the gynecological cells can be accurately identified, and the accuracy of an identification result is ensured.

The invention discloses a low signal-to-noise ratio conditional modulation identification method based on wavelet transform and channel attention. The method comprises the following steps: acquiring a received signal; and inputting the received signal into an identification model of a pre-trained neural network, and outputting a classification identification result and classification accuracy. In the recognition model based on signal reconstruction and the neural network, a wavelet threshold estimation module and a wavelet reconstruction module form a signal reconstruction path, a multi-scale feature extraction module and a prediction classification module form a recognition path, and the two paths are mutually enhanced, so that the combination of the advantages of a digital signal processing technology and the neural network is realized; a wavelet threshold estimation module is introduced to predict a denoising threshold through a neural network, and network parameters of the wavelet threshold estimation module are updated according to back propagation; a signal reconstruction path is combined with a recognition path, the interpretability of the network is enhanced, and the accuracy of modulation recognition in a low signal-to-noise ratio scene is remarkably improved by using multi-scale modulation characteristics.

The invention discloses a numerical control machine tool dynamic error cooperative compensation control method adaptive to high-speed machining, and relates to the technical field of numerical control machine tool error control. According to the method, a multi-source dynamic error sensing system comprising a grating displacement sensor, a six-dimensional force sensor and the like is constructed to acquire data; after wavelet threshold denoising and Kalman filtering preprocessing, inputting a three-layer LSTM error coupling prediction model combined with an attention mechanism, embedding a servo motor load characteristic curve in the model, and outputting three types of error compensation amounts; through servo-level compensation and machining-level compensation, the position of a feed shaft, the rotating speed of a main shaft, the cutting feed rate and the behavior of a micro-displacement actuator are corrected, and machining errors caused by deflection and vibration conduction of the main shaft are counteracted. And iteratively updating model parameters by using a gradient descent algorithm. According to the method, through multi-source error synchronous sensing, error coupling modeling and hierarchical cooperative compensation, dynamic error cooperative control more adaptive to a high-speed processing scene is realized, and the method has a wide application value.

The invention provides a photovoltaic direct current fault arc signal noise reduction method and device, and relates to the technical field of photovoltaic direct current fault arc diagnosis. The method comprises the following steps: decomposing a photovoltaic DC fault arc signal based on an IMGO-ICEEMDAN decomposition method to obtain a photovoltaic DC fault arc intrinsic mode component; calculating a multivariate intrinsic mode index, and dividing the photovoltaic DC fault arc intrinsic mode component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic mode index; performing noise reduction on the noisy signal component through the improved wavelet threshold to obtain an improved wavelet threshold noise reduction signal; and reconstructing the low-frequency signal component and the improved wavelet threshold noise reduction signal to obtain a noise reduction signal. According to the noise reduction method, the noise reduction effect of the fault arc signal is effectively improved while the key characteristics of the photovoltaic direct current fault arc signal are reserved, and the fault arc recognition accuracy can be remarkably improved.

The invention discloses a device for acoustically monitoring suspended sediment concentration and section particle size mean value of an ocean and a river, and relates to the technical field of ocean and river water environment monitoring. The four groups of piezoelectric ceramic transducers at the transmitting end are used for transmitting sound pulses with multiple frequencies into the same water body, and the broadband hydrophone array at the receiving end is used for synchronously receiving acoustic backward scattering signals which are scattered back. Wavelet threshold denoising and frequency domain filtering are combined, acoustic back scattering signals are preprocessed, turbulence and biological noise are eliminated, nonlinear errors caused by the particle size-concentration coupling effect are solved through an inversion algorithm fusing attenuation and scattering signals and in combination with a dynamic hierarchical data processing technology, and the accuracy of the acoustic back scattering signals is improved. The device realizes high-precision real-time measurement of suspended sediment parameters in ocean and river environments, has the capability of adaptive correction of environmental parameters, and is suitable for scientific research and engineering monitoring scenes.

The invention discloses a fracturing equipment state monitoring and fault diagnosis system and method, and belongs to the technical field of oil and gas field fracturing equipment intelligence. The invention aims to solve the problems that in the prior art, monitoring depends on a single signal, fault early warning lags behind, and the misjudgment rate is high. The method comprises the following steps: collecting multi-source operation data of the fracturing pump in real time; establishing a theoretical pressure indicator diagram, and comparing the theoretical pressure indicator diagram with an actual indicator diagram generated by real-time data to realize first-stage fault judgment; an improved wavelet threshold noise reduction method is adopted to process the signals, and time domain and frequency domain features are extracted; and inputting the processed feature data into the combined diagnosis model for second-stage fault judgment. The joint diagnosis model combines a principal component analysis (PCA) model used for uncalibrated data anomaly detection and a BP neural network model used for calibrated data fault classification. Through deep fusion of the mechanism model and the data driving model, environmental interference is effectively resisted, and the diagnosis accuracy and the operation and maintenance efficiency are remarkably improved.