2663 results about "Distortion" patented technology

The invention discloses an experimental calibration method based on multi-sensor signal fusion processing, particularly relates to the field of data analysis, and comprises the steps of a stepped confrontation disturbance protocol, data stream processing and real-time analysis, and multi-stage logic judgment and calibration execution. A stepped confrontation disturbance protocol is created for the first time, potential errors of the sensor are excited in real time and corrected in a closed loop mode under the condition that system operation is not interrupted, and a traditional shutdown calibration mode is thoroughly replaced; a multi-stage disturbance defense system is constructed, and composite interferences such as spectrum aliasing, overload distortion and signal-to-noise ratio attenuation caused by electromechanical thermal multi-physical field strong coupling are effectively suppressed; laser space calibration and dynamic rigidity modulation technologies are fused, and space drift errors caused by deformation of the mounting base and mechanical loosening are accurately diagnosed and compensated. Finally, long-term precision stability and data fusion reliability of the multi-source sensor under complex working conditions are achieved.

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention provides an unmanned aerial vehicle multi-dimensional information fusion method and system based on acousto-optic-electric composite detection. According to the method, acoustic frequency band signals, visible light image sequences and electromagnetic field intensity change data are collected, acoustic characteristic flow, optical characteristic flow and electrical characteristic flow are generated, and based on the time deviation between a low-frequency vibration mode and transient electromagnetic pulses, multi-channel dynamic calibration is carried out on an image distortion area in the optical characteristic flow. The method comprises the following steps: acquiring an acoustic feature flow, an optical feature flow and an electrical feature flow, correcting space-time coordinate parameters of the acoustic feature flow, the optical feature flow and the electrical feature flow, generating a calibration feature flow, carrying out anti-interference fusion processing on the calibration feature flow, extracting multi-dimensional coupling features, inputting the multi-dimensional coupling features into an unmanned aerial vehicle flight path planning model, and generating a three-dimensional space situation model; according to the invention, the physical space-time consistency alignment and noise suppression of the multi-modal data are realized, and the obstacle positioning precision and the dynamic trajectory prediction real-time performance are significantly improved.

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention provides a multi-stage turbulent dynamic video recovery method based on a physical model, and relates to the technical field of image processing. According to the method, the dynamic benefit index DEI is introduced, the turbulence intensity, the dynamic region ratio and the optical flow diagram are combined, the video dynamic intensity under different turbulence conditions is accurately quantified, the high-dynamic turbulence data set is constructed, and the model training and evaluation pertinence is enhanced. In addition, according to the multi-stage recovery frame PMR provided by the invention, a recovery task is decoupled into three stages of de-inclination, motion segmentation enhancement and deblurring, and step-by-step recovery is carried out, so that complex distortion is more accurately dealt with, and efficient generalization and high-quality recovery are realized. The frame is also combined with a dual-stage registration strategy, and through global motion alignment and pixel-level optical flow correction, camera shake and optical interference in a real scene are reduced, the problem of multi-frame dislocation in a turbulent dynamic scene is effectively solved, and the stability and robustness of recovery are improved.

The invention relates to the technical field of integrated circuit test and calibration, and discloses a chip test and calibration method and a chip tester. The chip test calibration method is applied to a chip test module and specifically comprises the following steps that S101, a starting signal sent by a user terminal is received, environment initialization operation is executed, and after initialization is completed, a system automatically enters a standby mode and waits for an external trigger signal or a user instruction; and S102, synchronously capturing input and output signal waveforms of the tested chip at a preset sampling frequency through a high-precision current sensor and a voltage sampling circuit. Through a dynamic parameter acquisition and real-time compensation technology, a voltage reference error is effectively reduced, the temperature control precision is superior to + / -0.3 DEG C, signal distortion caused by environmental interference is effectively inhibited, a multi-dimensional calibration model is combined with time domain, frequency domain and statistical characteristic analysis, nonlinear errors and system drift can be dynamically corrected, and the system performance is improved. And the attenuation error of a test signal transmission path is lower than 0.02 dB.

The invention discloses a self-adaptive filtering and intelligent separation method for a multi-mode partial discharge signal, and relates to the technical field of insulation state monitoring and signal processing of power equipment, and the method comprises the following steps: S1, collecting the partial discharge signal of the power equipment in real time, and synchronously obtaining an equipment operation parameter and an environment noise signal; according to the adaptive filtering and intelligent separation method for the multi-modal partial discharge signal, the problems of modal aliasing and feature distortion caused by signal and noise time-frequency coupling in a dynamic noise environment in a traditional method are effectively solved through a cooperative mechanism of dynamic coupling degree modeling and dual-channel adversarial decoupling. The dynamic coupling path sensing module quantifies interaction characteristics of noise and signals in real time, and realizes accurate suppression of high-frequency transient interference and low-frequency periodic noise in combination with a double-channel architecture of complex field phase sensitive filtering and vibration trajectory matched filtering.

The invention discloses an automatic identification and expansion communication method based on an RS485 interface, and relates to the technical field of industrial automation communication. The problems that an existing RS485 interface is complex in configuration, poor in protocol compatibility and high in data transmission delay are solved. According to the scheme, the terminal matched resistance and the bias current are adjusted in real time by dynamically measuring the bus equivalent distributed capacitance value, and signal attenuation caused by the capacitance effect is inhibited; a silicon controlled rectifier trigger type charge discharge mechanism is adopted to force bus residual charge to return to zero quickly, and an idle state is accurately judged in combination with a hysteresis comparator; dynamically compensating signal edge distortion and calibrating a sampling window time sequence based on a pre-emphasis filtering and Kalman filtering algorithm; multi-node stable communication is realized through capacitance value driven conflict detection threshold adaptive adjustment and a Poisson back-off model; according to the invention, the real-time performance, the anti-interference capability and the multi-node expansion reliability of RS485 communication in a complex industrial scene are obviously improved, and the debugging and maintenance cost of the system is reduced at the same time.

The invention discloses a time-space alignment fusion processing method and system for multi-source physiological signals, and relates to the technical field of data processing.The method comprises the steps that the multi-source physiological signals in the limb movement state are synchronously collected, and a heterogeneous time sequence data set is obtained; performing space-time alignment processing on the heterogeneous time sequence data set to generate a synchronous physiological signal matrix; performing signal quality evaluation based on the synchronous physiological signal matrix, constructing a weighted decision tree model, performing confidence fusion on the synchronous physiological signal matrix according to the weighted decision tree model, and generating multi-parameter joint monitoring data; and performing motion artifact suppression processing on the multi-parameter joint monitoring data, outputting a physiological parameter index set, and transmitting the physiological parameter index set to a first-aid equipment monitoring terminal. Therefore, the technical effects of eliminating signal distortion, improving monitoring data quality and ensuring first-aid monitoring precision are achieved.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention discloses an intelligent material conveying deviation remote monitoring system based on Internet of Things, which relates to the technical field of material conveying deviation remote monitoring and comprises a signal monitoring module, an abnormal window calibration module, a distortion evaluation module, a data correction module and a remote optimization module. The distortion evaluation module is used for acquiring ultrasonic propagation distortion characteristic information in a signal propagation abnormal window in real time, analyzing and evaluating the echo signal distortion degree under the condition of ultrasonic signal propagation path distortion after acquisition, and dividing the echo signal distortion degree into three types, namely a light distortion degree, a medium distortion degree and a heavy distortion degree; and the data correction module is used for taking corresponding correction measures on the to-be-corrected measurement data under the condition of different echo signal distortion degrees based on the evaluation result. According to the method, the problem of measurement errors caused by propagation path distortion of ultrasonic signals in conveyor belt deviation monitoring is solved, accurate distortion evaluation and adaptive data correction are realized, and the monitoring precision and stability are improved.

The invention provides a big data intelligent decision analysis method and system. The method comprises the following steps: acquiring vibration data and mechanical node stress distribution data in power equipment transportation, and extracting high-frequency harmonic distortion characteristics aligned with a vibration data timestamp; performing cross-domain correlation analysis on stress data fluctuation and harmonic characteristic dynamic change, and identifying a symbiotic mode of mechanical stress abnormality and electrical harmonic distortion by using a machine learning model; based on logistics path planning and equipment constraints, a cooperative control instruction for adjusting the transportation path and the power supply loop parameters is generated through a multi-target cooperative optimization algorithm; and constructing a joint risk assessment matrix, and dynamically adjusting the cooperative relationship between the transportation path and the harmonic suppression parameter according to the quantitative analysis result and the cooperative control instruction to realize the closed-loop decision of the power equipment. According to the technical scheme provided by the invention, the power equipment transportation data processing efficiency is remarkably improved.

The invention provides a power distribution network insulator early fault detection method and system considering an interference event. The method comprises the steps of obtaining a zero-sequence current of a power distribution network insulator, and calculating a zero-sequence current mutation rate based on the zero-sequence current; if the zero-sequence current abrupt change rate is greater than a set threshold value, judging that the power distribution network has a fault or a disturbance event; obtaining original recording data of the insulator of the power distribution network, and filtering the original recording data based on a wavelet reconstruction algorithm; for the filtered signal, identifying a peak waveform based on a fault phase voltage extreme point; the non-linear distortion waveform is identified based on zero-sequence current sine fitting, and based on an identification result, when a set first criterion or a set second criterion is satisfied, it is judged that an early fault of the insulator may occur; and calculating the wavelet energy entropy of the signal, if the wavelet energy entropy is greater than a threshold value of the set entropy, determining that the power distribution network insulator has an early fault, otherwise, determining that the power distribution network insulator has an interference event.

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

The invention discloses a microwave radio frequency link multi-index comprehensive measurement method, and relates to the technical field of radio frequency communication testing, and the method comprises the following steps: S001, building a peak power prediction model, generating a peak power probability curve based on the statistical distribution characteristics of a plurality of carrier signals, and forming a peak early warning vector containing an estimated time point and an amplitude value; and S002, executing synchronous sampling scheduling according to the peak early warning vector, and by adjusting a sampling trigger threshold and a time window parameter, enabling a sampling window to aim at peak power occurrence time and cover a time interval of amplitude limiting protection. According to the method, through peak value prediction, accurate sampling, multi-dimensional difference and distortion identification, efficient capture and dynamic regulation and control of amplitude limiting abnormity are realized, a prediction-identification-adjustment-optimization closed-loop process is constructed, the monitoring integrity and adaptive capacity of a microwave radio frequency link to sudden distortion are remarkably improved, and stable operation of a system is guaranteed.

The invention discloses a metal plate laser cutting control method, and relates to the technical field of laser cutting, material parameters, thickness information and cutting path data are obtained, a mixed enveloping rectangle algorithm is combined with a genetic algorithm to optimize a cutting sequence, and a path sequence containing idle stroke avoidance and nested graph priority is generated; constructing a laser parameter prediction model based on the material, thickness and path characteristics, and dynamically adjusting the power gradient; the slag splashing state is judged through data fusion of a capacitive sensor and an encoder, a quality feedback signal is extracted in combination with machine vision, and laser parameters are dynamically adjusted through a fuzzy control algorithm; wavefront distortion is monitored in real time, the correction frequency is adjusted according to the slag splashing state, and a deformable mirror is used for compensating the phase; when the quality is abnormal, historical parameter combinations are retrieved through a cosine similarity algorithm for joint compensation. According to the method, the path cutting efficiency is improved, and the problems of low efficiency of path planning and parameter adjustment lagging in the prior art are solved.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

The invention relates to the technical field of copper pipe defect detection, in particular to a copper pipe internal thread secondary machining defect detection method and system, and the method comprises the steps: obtaining thread surface feature data based on multi-mode optical parameter measurement, and the multi-mode optical parameters at least comprise a polarized light inhibition reflection parameter and a multi-band spectral reflection parameter; temperature gradient parameters in the machining process are synchronously obtained, and deformation error correction is conducted on thread surface feature data in combination with dynamic compensation; according to the corrected thread surface feature data, a multi-dimensional defect judgment model is pre-trained to execute hierarchical defect detection; and integrating the coupling relationship between the multi-mode optical parameters and the electromagnetic parameters to output a defect measurement result, wherein the coupling relationship realizes multi-physical quantity fusion judgment through a preset weight distribution matrix. According to the invention, the problems of optical detection signal distortion and high microcrack omission ratio caused by surface high specular reflection in copper pipe internal thread secondary processing defect detection are effectively solved.

The invention relates to the technical field of flow measurement and control, and particularly discloses a cheese filling flow real-time calibration control method and system.The cheese filling flow real-time calibration control method comprises the following steps that working condition information of cheese in a filling pipeline is collected in real time, and the working condition information comprises the temperature, the pressure, an original flow signal, the pump rotating speed and the proportional valve opening degree; dynamically selecting a filtering order according to the signal-to-noise ratio of the original flow signal, and outputting the filtered flow; and based on the acquired working condition information, predicting the dynamic viscosity of the cheese in real time through a neural network, fitting a flow-working condition relation function and calculating a target flow value. The signal-to-noise ratio of an original flow signal is dynamically adapted to optimize filtering processing, signal distortion caused by interference such as temperature drift and mechanical vibration is effectively eliminated, and the flow detection precision is improved; by means of the neural network, the dynamic viscosity of cheese is predicted in real time, the model is dynamically adjusted, the problems that traditional viscosity estimation lags behind, and the batch adaptability is poor are solved, and an accurate basis is provided for flow target value calculation.

The invention relates to the technical field of water pump fault diagnosis, discloses a condensate pump shafting vibration fault diagnosis method and device based on multi-parameter analysis and a medium, and solves the problems of sensor signal distortion and noise interference of a condensate pump under high-temperature and high-humidity working conditions through multi-source data fusion and environment adaptive compensation. Dynamic time-frequency domain feature extraction and an improved lightweight residual network are adopted, and efficient decoupling and accurate recognition of multi-fault concurrent features are achieved; the fuzzy inference rule base and the shafting fluctuation propagation model are combined, the fault positioning precision and the early fault detection rate are remarkably improved, scientific and reliable technical support is provided for diagnosis and maintenance of the shafting vibration fault of the condensate pump, and safe and stable operation of factory equipment is effectively guaranteed.

The invention relates to the technical field of voice processing, in particular to a mixed voice processing method, electronic equipment and a computer readable medium. The method comprises the following steps: collecting mixed voice and environment influence parameters; carrying out bionic frequency domain analysis on the mixed voice to obtain low-frequency attenuation compensation characteristic data; performing multipath effect propagation analysis on the low-frequency attenuation compensation characteristic data through the environmental influence parameters to generate channel distortion data; performing time domain-frequency domain joint deconvolution processing on the mixed voice by using the channel distortion data to generate a direct sound component and a reflected sound component; performing adversarial training based on the direct sound component and the reflected sound component to generate anti-multipath speech enhancement data; and constructing a dynamic frequency compensation filter based on preset environmental acoustic characteristics. Through the multi-stage signal processing, frequency compensation and real-time optimization technology, the output quality of the mixed voice is improved.

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

The invention relates to the technical field of power distribution networks, and discloses a power distribution network fault self-recovery method and system based on machine learning, and the method comprises the steps: collecting the multi-source fault feature data of a power distribution network in real time, including the voltage and current waveform distortion feature, the load sudden change feature and the equipment temperature anomaly feature; and inputting the multi-source fault feature data into a multi-stage classification model for processing, and by setting a pseudo fault filtering module, establishing a typical interference feature library and setting dynamic filtering thresholds for different working conditions during real-time fault detection of the power distribution network, so that the recognition accuracy of various interference signals is ensured, and the fault detection accuracy of the power distribution network is improved. Meanwhile, similarity comparison is carried out on real-time collected data and pseudo fault features, whether the problems of signal distortion and noise pollution occur at the fault positioning position of the power distribution network or not can be detected in real time, the accuracy of fault type recognition is ensured, the misjudgment risk is reduced, and when strategy adaptation is abnormal, the matching degree of the self-healing strategy and the actual state of the power grid is ensured.

The invention provides an electrical equipment remote maintenance guidance method and system. The method comprises the following steps: capturing an aperiodic torque waveform and a magnetic field gradient abnormal signal by deploying a torque fluctuation sensor and a magnetoresistive sensor; performing multi-scale frequency band division on the non-periodic torque waveform, screening out a transient high-frequency component in a start-stop stage of equipment, and separating out a magnetic field polarity reversal characteristic from a magnetic field gradient abnormal signal; performing cross-domain matching on the occurrence time of the transient high-frequency component and the spatial orientation of the magnetic field polarity reversal feature to generate a mixed feature mark; calculating a frequency band overlapping degree based on the time-frequency energy distribution marked by the mixed features and generating a coherence map; and matching a fault historical case library according to the coherence map, and outputting a maintenance strategy set aiming at the rotating shaft dynamic unbalance and electromagnetic interference superposition fault. According to the invention, through fusion of torque fluctuation and magnetic field distortion collaborative analysis, the precision of mechanical and electromagnetic composite fault diagnosis of the rotating shaft is improved.

The invention provides a low-delay streaming voice interaction system with an interruption processing function, and relates to the technical field of artificial intelligence. Necessary preprocessing and acoustic feature extraction are performed through a real-time acoustic processing module, and distortion and complex environmental noise introduced by an interaction channel are resisted through a robustness enhancement technology; the streaming acoustic decoding module performs acoustic modeling, language model application and decoding in real time in parallel, and outputs an ultra-low-delay text transfer result stream; the real-time acoustic processing module is combined with a signal processing technology and is responsible for detecting user voice activity in a high-precision and ultra-low-delay manner, and particularly judging the real-time voice activity state of a user through the user voice activity in an AI voice playing period; an efficient and low-delay bidirectional streaming network transmission mode is adopted between modules of the system and between the modules and a communication platform, and it is ensured that audio streams, acoustic feature streams, text streams and control signals can be transmitted and processed in real time with extremely low end-to-end delay.

The invention relates to the technical field of crane line fault diagnosis, in particular to a crane line fault diagnosis system and method based on multi-source data fusion, which comprises a data acquisition and processing unit, a mechanical and electrical coupling characteristic unit and a characteristic fusion and fault quantification unit, the three-axis vibration acceleration and the three-phase current waveform of the track are obtained through the data collecting and processing unit, the mechanical and electrical coupling characteristic unit conducts three-dimensional vector synthesis and wavelet packet decomposition on vibration data, and a time-space incidence matrix of harmonic distortion and vibration is constructed. And the feature fusion and fault quantification unit outputs coupling factors by using a bidirectional long-short-term memory network and an attention mechanism, and outputs a fault probability value through a dynamic time warping matching algorithm after time-frequency domain analysis and sample entropy judgment, so that time-space correlation modeling and dynamic fault matching of multi-source data are realized. And the fault positioning precision and the diagnosis accuracy are improved.

The invention discloses a wavefront reconstruction method for improving the dynamic range of a Shack-Hartmann wavefront sensor. Belongs to the technical field of wavefront detection. According to the method, a new Shack-Hartmann wavefront detection method is needed, the effective dynamic range of a Shack-Hartmann sensor can be expanded on the premise that the hardware complexity is not remarkably increased, local mismatching accumulation can be avoided through a reasonable matching strategy, and the detection accuracy is improved. Therefore, high-precision and high-reliability wavefront reconstruction can be realized in a large-amplitude wavefront distortion scene. Comprising the following steps: S1, detecting by a Shack-Hartmann wavefront sensor to obtain a light spot distribution diagram H (x, y); s2, extracting a light spot centroid pixel-level position through a matched filtering algorithm; s3, calculating the sub-pixel-level position of the mass center of the light spot through Gaussian interpolation; s4, a mass center-subaperture matching relation is established through a Kuhn-Munkres algorithm; and S5, calculating the wavefront slope according to the corresponding relationship between the centroid and the sub-aperture, and then recovering the wavefront according to the wavefront slope.

The invention discloses a dynamic object suppression video image stabilization method based on depth information and global consistency, and the method comprises the steps: extracting key points in a video, carrying out the optical flow estimation, and obtaining the depth information of the key points; a static weight and a motion vector residual error are calculated based on the motion information, the depth information and the global homography matrix of each key point, a dynamic weight is obtained based on an attention mechanism, the dynamic weight and the static weight are fused to obtain a comprehensive weight, and a weighted motion residual error is calculated based on the comprehensive weight and the motion vector residual error; spreading the motion information to grid vertexes, obtaining dense residual motion based on weighted motion residual prediction, and accumulating the dense residual motion to obtain a track; and carrying out smoothing and motion compensation on the track and outputting a stable video sequence. The problem that in a complex scene, motion of a dynamic object causes distortion and inaccuracy of a stabilization result is solved, interference of the dynamic object can be effectively suppressed, and the effect and robustness of video image stabilization are improved.

The invention provides a consistent video generation method and device based on wavelet transform and frequency decomposition, and relates to the technical field of image processing. Through a wavelet multi-stage frequency band decoupling-fusion framework and a dynamic cross-modal attention mechanism, a dynamic alignment mechanism of a distributed sensing adapter and an HJB optimization strategy of hierarchical diffusion are combined, so that the consistency video generation efficiency is improved. Identity representation fusion across space-time dimensions is achieved while parameter efficiency is guaranteed, the problems of high-frequency detail drift, dynamic scene identity distortion and poor long sequence generation stability in a traditional video generation method are solved, the quality and stability of a generated video are remarkably improved, and the video generation efficiency is improved. And more advanced technical support is provided for video processing and related applications.

The invention relates to the technical field of earphone noise reduction processing, and discloses a motorcycle riding earphone noise reduction method combining different noise reduction algorithms, comprising the following steps: acquiring an original audio signal, and distributing the original audio signal to each processing module; a preprocessing signal is generated, voice activity information is determined, and environmental noise energy information is calculated; according to the environmental noise energy or the auxiliary information, adaptively adjusting a high-low noise energy threshold value; the original signals are input into the traditional and AI noise reduction module in parallel to generate two paths of noise reduction signals; based on the environment noise, the voice information and the threshold value, performing weighted fusion to generate an output signal; and carrying out equalization and compression processing on the output signal, and driving the loudspeaker to output. According to the invention, comprehensive judgment on environmental noise energy information, voice activity information and riding speed is introduced, an output signal of a traditional noise reduction module is set to be zero in a high-speed voice-free environment, and AI noise reduction is combined, so that tone quality distortion of a traditional noise reduction algorithm in a noise environment is effectively avoided.