966 results about "Adaptive filter" patented technology

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention provides a fiber-optic gyroscope drift compensation method and system fused with LMS self-adaptive filtering, and relates to the technical field of LMS self-adaptive filtering, the method comprises the following steps: synchronously collecting multi-point temperature data, temperature change rate, axial and radial gradient information and original output signals of a gyroscope shell, and calculating the multi-point temperature data, the temperature change rate, the axial and radial gradient information and the original output signals of the gyroscope shell; the original signal is separated into a temperature drift component and a non-temperature noise component through variational mode decomposition, and noise is filtered out; establishing a nonlinear mapping relation by combining the temperature field data and the drift component to generate a drift predicted value, and inputting the predicted value and the residual error of the actual drift component into an LMS adaptive filter for dynamic error compensation; and finally, superposing the compensation result and the predicted value, and outputting a corrected gyro signal. According to the invention, the drift compensation precision and stability of the fiber-optic gyroscope in a complex variable-temperature environment are improved.

The invention relates to the field of water quality monitoring analysis, in particular to a multi-modal water quality monitoring data intelligent processing method and system. The method comprises the following steps: obtaining a water quality monitoring parameter stream and a water quality monitoring image, and carrying out adaptive processing to obtain a filtering optimization parameter stream and an optimization monitoring image; performing water quality state evolution perception according to the adaptive filtering optimization parameter flow, and constructing a water quality state evolution graph; and performing biological index response analysis and water quality ecological influence evaluation based on the water quality state trend evolution graph to obtain a water quality evaluation report. And carrying out pollutant spatial distribution change analysis based on the optimized monitoring image, and executing final water quality monitoring evaluation according to a water quality evaluation report. The invention provides efficient and accurate water quality status analysis.

The invention relates to the technical field of battery fault detection, and particularly discloses a new energy automobile battery fault intelligent diagnosis method and system, and the method comprises the steps: collecting the three-dimensional time sequence data flow of the voltage, temperature and internal resistance of a battery pack through a distributed sensor array; the environment temperature, the number of charge and discharge cycles and the vehicle operation condition parameters are synchronously integrated as auxiliary diagnosis dimensions, and a multi-source heterogeneous data sensing system is constructed; the method comprises the following steps: preprocessing original data by adopting a dual-channel hybrid filtering architecture based on a multi-source heterogeneous data sensing system, and establishing an adaptive filtering parameter adjustment mechanism to generate multi-modal data; according to the method, multi-dimensional time sequence data and auxiliary diagnosis parameters of the battery pack are comprehensively collected through a constructed multi-source heterogeneous data sensing system, a rich and accurate data basis is provided for fault diagnosis, noise interference is reduced through a two-channel hybrid filtering architecture and a self-adaptive filtering parameter adjustment mechanism, and the fault diagnosis accuracy is improved. And a solid foundation is laid for subsequent feature extraction and model training.

The invention discloses a PPP-RTK time delay processing method and device in conversion from SSR to OSR, and relates to the technical field of global navigation satellite system positioning, and the method comprises the steps: converting orbit, clock error, ionosphere and troposphere delay SSR parameters provided by a server into OSR virtual observation values through geometric distance calculation and projection function mapping; based on a constant velocity model and a static random process, predicting a correction amount of a future moment, and correcting an OSR virtual observation value through state transition matrix compensation time delay; combining server side parameters and user side observation data, recursively generating an error covariance matrix, dynamically adjusting a state prediction weight, and obtaining covariance information through adaptive filtering optimization positioning calculation; and realizing ambiguity rapid fixation and centimeter-level positioning by using the corrected OSR observation value and covariance information. According to the method, real-time centimeter-level positioning in a high-delay scene is realized through a dynamic correction value prediction method and a recursive covariance estimation method.

The invention discloses a dynamic Gaussian filtering point cloud optimization method and system based on local features, and relates to the technical field of point cloud data processing and three-dimensional computer vision, and the method comprises the steps: carrying out the preprocessing of original laser radar point cloud data, dynamically adjusting the sampling rate, constructing an index structure, and carrying out the dynamic Gaussian filtering based on the preprocessed point cloud data. The method comprises the steps of extracting geometric features of local neighborhoods, fusing the geometric features into weighted feature vectors, dynamically adjusting kernel function parameters of Gaussian filtering according to the weighted feature vectors, generating an adaptive filtering window, performing weighted Gaussian filtering processing on point cloud data by using the dynamically adjusted kernel function parameters, and outputting denoised point cloud data. According to the method, efficient point cloud retrieval is realized through dynamic sampling and index construction, multi-feature optimization weight is fused to generate an adaptive filtering window, de-noising and detail reservation are balanced, spatial distribution is recovered, missing points are interpolated and filled, a complete point cloud data set is generated, and data quality and processing precision are remarkably improved.

The invention discloses a signal processing method based on deep learning and adaptive filtering fusion. The method comprises the following steps: S1, collecting and preprocessing an original signal; s2, carrying out adaptive filtering; s3, constructing a deep neural network model to perform deep feature extraction on the filtered signal; s4, signal fusion and interference elimination are carried out; S5, a real-time closed-loop feedback mechanism is established, and dynamic parameter adjustment is carried out; s6, performing an end-to-end dynamic optimization process; s7, performing iterative updating and high-confidence sample judgment; and S8, processing a newly collected signal by using the trained adaptive filtering module and the deep neural network model, and generating a reconstructed signal through signal fusion. According to the method, through effective fusion of adaptive filtering and the deep neural network, accurate suppression and high-dimensional feature extraction of a multipath effect, nonlinear noise and transient interference in a complex wireless signal are realized, so that the precision, robustness and real-time performance of signal processing are remarkably improved.

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention relates to the technical field of ground resistance detection, in particular to a single-core cable ground resistance detection method capable of resisting strong power frequency interference. High-frequency signals are injected in a non-contact mode through a coupling type capacitor clamp, background voltage and current signals are collected firstly, and injection frequency far away from power frequency and harmonic waves is selected through Fourier analysis; cancelling background noise by adopting a normalized least mean square adaptive filter to generate an enhanced signal; the signal is down-converted to a base band through digital quadrature demodulation, a Kalman filter estimates the amplitude and the phase in real time, and the state and noise parameters are periodically calibrated through a compressed sensing algorithm; and finally, loop impedance is calculated according to the voltage and current amplitudes and the phase difference to obtain grounding resistance. The method has the advantages of being non-contact, high in safety, strong in anti-interference capability, high in measurement precision and the like, and is suitable for monitoring the grounding state of the cable in a complex electromagnetic environment.

The invention relates to the technical field of motor detection, and discloses a motor instantaneous torque measurement method and system based on a high-bandwidth sensor, and the method comprises the steps: synchronously collecting a torque signal and a multi-source interference variable through the high-bandwidth sensor, and forming an original torque sequence after time synchronization superposition; extracting disturbance frequency characteristics through multi-scale wavelet transform and spectral analysis; a pre-trained adaptive filter model is utilized to generate a filter coefficient matched with the working condition, and primary filtering is completed; residual fluctuation is suppressed in combination with Kalman state estimation, and an optimized torque estimation value is obtained; parameter self-adaptive compensation is realized through online calibration weight updating, and a stable torque measurement sequence is output; load abrupt change pre-judgment and threshold value dynamic updating are achieved based on time-frequency correlation analysis, and finally rapid measurement convergence after working condition abrupt change is ensured through a closed-loop reset mechanism. According to the method, the problem of low instantaneous torque measurement precision in the prior art can be solved.

The invention relates to the technical field of belt conveyor carrier roller fault monitoring, and discloses a belt conveyor carrier roller fault distributed optical fiber monitoring system which comprises a double-light-source DAS system, optical fiber information acquisition, filtering preprocessing, time domain analysis, time-frequency domain analysis, phase analysis, multi-mode feature fusion, fault classification and fault position and grade output. By building a dual-light-source polarization diversity DAS system and combining an adaptive filtering preprocessing technology, high signal-to-noise ratio acquisition and noise reduction processing of vibration signals are realized, dual-light-source orthogonal polarization state transmission effectively suppresses polarization fading, wavelet packet denoising and variable-step LMS filtering collaboratively filter environmental noise and pulse interference, and the noise reduction performance of the vibration signals is improved. Meanwhile, the application of the dynamic weighted root-mean-square value and the continuous wavelet transform ensures the accurate capture of the time domain energy characteristic and the frequency domain time-varying characteristic, and lays a reliable foundation for the multi-modal characteristic fusion.

The invention relates to the technical field of ocean engineering surveying and mapping, and particularly provides an ocean engineering surveying and mapping terrain correction method based on multi-beam sounding. The method comprises the following steps: constructing a tremor-sound velocity coupling model through multi-source data synchronous acquisition, and generating a sound velocity dynamic correction term; fusing the static sound velocity profile, the internal wave disturbance term and the sound velocity correction term, and establishing a time-shifting sound velocity field of spatio-temporal evolution; realizing dynamic space homing calculation through a double-shaft decoupling rotation matrix based on a hull attitude and a time shifting sound velocity field; tunnel effect noise is suppressed by adopting an airspace adaptive filtering technology; the terrain deformation rate is calculated in combination with continuous cycle data, and a deformation threshold value is dynamically set through soil mechanical parameters to trigger graded safety early warning; and outputting the high-precision three-dimensional terrain model without the sound velocity error and the noise interference. According to the method, the problems of geological activity interference compensation deficiency, tunnel effect noise suppression insufficiency, large dynamic environment homing error and the like in traditional surveying and mapping are solved, and the reliability of complex sea area topographic survey is remarkably improved.

The invention discloses a power capacitor bank dielectric loss on-line monitoring method, particularly relates to the technical field of power system equipment insulation state monitoring, and is used for solving the problems of insufficient signal synchronization precision and large dielectric loss calculation error under complex electromagnetic interference in the existing method. The method comprises the following steps: acquiring voltage and current signals in real time and dynamically tracking fundamental frequency; intercepting a main period waveform, constructing phase space trajectory analysis complexity, and generating distortion features in combination with symbol sequence transfer entropy; dynamically adjusting filtering parameters based on the distortion characteristics and the Lyapunov exponent gradient; calculating a high-precision phase difference through zero crossing point interpolation correction and sliding window filtering, and finally determining a dielectric loss value; through dynamic frequency tracking, nonlinear feature collaborative analysis and an adaptive filtering strategy, noise interference is effectively suppressed, phase synchronization precision and dielectric loss calculation reliability are improved, and accurate and real-time monitoring support is provided for insulation performance evaluation of the power capacitor bank.

The invention relates to the technical field of underwater detection, and discloses an underwater comprehensive detection system. The system collects multi-dimensional acoustic data including sound wave reflection intensity, propagation time delay and frequency dispersion characteristics of a target water area through an underwater sonar array; dividing the detection area into a plurality of layered units according to a water attenuation coefficient and a substrate reflection characteristic of the acoustic data, and extracting an environment intrinsic parameter of each layered unit; constructing an acoustic propagation path model based on the environmental intrinsic parameters, and generating adaptive filtering parameters in combination with a preset noise suppression strategy; calling sonar array hardware configuration data, matching filtering parameters with an array beam forming mode, and calculating a detection sensitivity threshold value of each layered unit; and integrating the sensitivity threshold and the acoustic data, generating a three-dimensional acoustic characteristic spectrum of the target water area, and marking the space coordinates and confidence level of the abnormal area. The system can improve the comprehensiveness and accuracy of underwater detection, and meets the requirement of high-precision detection.

The invention relates to the technical field of power systems, in particular to a UPQC harmonic compensation method and system based on repetitive control, and the method comprises the steps: obtaining a load current signal and a voltage signal of a power distribution network, and extracting a harmonic instruction current based on the load current signal; and inputting the harmonic instruction current into a feed-forward channel constructed based on an adaptive filter, and generating a feed-forward control quantity. The phase and frequency of the voltage signal of the power distribution network are tracked in real time through a digital phase-locked loop, the fundamental frequency of the power grid is accurately solved, and the power distribution network can be accurately controlled. The internal model period and the phase lead compensation amount of the frequency adaptive repetitive controller are dynamically calculated, and the internal model structure of the repetitive controller is always matched with the current power grid frequency by updating the two core parameters in real time, so that the compensation precision decline caused by frequency deviation is avoided; and high-precision suppression of the periodic harmonic waves can be realized in a normal fluctuation range of the fundamental wave frequency of the power grid.

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a rigid-elastic coupling-oriented active and passive integrated control method for a hypersonic flight vehicle. The invention aims to realize stable tracking control of the elastic hypersonic flight vehicle. The method comprises the following steps: constructing a longitudinal dynamic model of the elastic hypersonic aircraft; self-adaptive identification of the elastic vibration frequency is realized through a cascaded self-adaptive filter; an elastic modal filtering estimation method is designed, and high-precision and low-cost elastic modal state quantity is provided for subsequent active feedback controller design; and then rigid-elastic coupling model decomposition is carried out, the control performance is ensured by using active disturbance rejection passive control for a rigid body subsystem, an RBF neural network is introduced for an elastic subsystem, an elastic mode is actively inhibited by using sliding mode control, and stable tracking of a reference instruction is realized. The method is an active and passive integrated control method for the hypersonic flight vehicle oriented to rigid-elastic coupling, and the application prospect is wide.

The invention discloses an electroencephalogram signal artifact removal method and an acquisition system. The method comprises the following steps: synchronously acquiring sensor data of a testee and each lead electroencephalogram signal by utilizing a unified trigger signal; performing timestamp alignment verification on the two types of synchronous data; a motion state quantitative index is determined, and if the motion state quantitative index does not exceed a preset motion threshold value, the collected electroencephalogram signals are output; if yes, an exception processing flow is triggered and comprises the steps that self-adaptive filtering and nonlinear compensation are sequentially executed on the electroencephalogram signals, static electroencephalogram data of the testee serve as reference in the self-adaptive filtering process, an NLMS filter with the step length dynamically adjusted is adopted, a motion artifact linear part and base noise are restrained, a self-adaptive filter is constructed based on the second-order Volterra series in the nonlinear compensation process, and the motion artifact linear part and the base noise are restrained; a kernel coefficient is optimized through an RLS algorithm, and nonlinear distortion is processed; and outputting the electroencephalogram signal after the exception processing. The method is suitable for non-linear and non-stationary motion artifact signals, signal synchronism and quality can be improved, and processing efficiency is considered.

The invention discloses a steering engine accurate control method and system based on an optimization control algorithm, and the system comprises a data collection module which collects key working parameters of a steering engine in real time through a plurality of sensors, and carries out the data processing, thereby forming a state vector; the self-adaptive filtering module is used for carrying out self-adaptive filtering fusion processing on the data acquired by the sensor and dynamically estimating the attitude angle and the corresponding angular velocity of the aircraft; the sliding mode control module constructs a sliding mode surface and a control rate of a steering engine system according to the dynamic error of the system, calculates control input of each axis of the aircraft according to the attitude angle real-time state error, and generates a sliding mode control signal; the driving execution module is used for converting the control signal into a voltage or PWM signal and inputting the voltage or PWM signal into a steering engine, so as to change thrust and realize adjustment and control of a posture or a position; and the real-time feedback module monitors the current state of the steering engine in real time to realize dynamic correction and real-time optimization. The adaptive filtering and sliding mode control method provided by the invention is higher in control precision, quicker in system response and higher in adaptability to complex environments, and can effectively improve the application performance and stability of a steering engine execution system in the fields of industrial automation, aerospace, intelligent manufacturing and the like.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal, and belongs to the field of gas detection. The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal. The infrared CO2 high-precision detection system comprises a sensing module, a signal conditioning and collecting module and a self-adaptive filtering processing module. According to the method, the problems of contradiction between response speed and stability, discrete signal tracking lag and sensitivity to environmental interference in the prior art are solved, the weight of a filtering channel is adaptively and dynamically adjusted, and the advantages of a fast filtering channel and a slow filtering channel are combined, so that balance between dynamic response and steady-state precision is realized; the problem of tracking lag of discrete signals is solved by analyzing the change rate of the signals in real time and dynamically adjusting a filtering strategy; and through a hybrid filter and an environment compensation algorithm, the influence of temperature and humidity changes on a detection result is reduced, so that the long-term stability of the sensor is improved.

The invention relates to the technical field of adaptive filters, and discloses a wide linear adaptive filter, which comprises the following steps of: performing tensor integral solution on a system weight vector at the moment of the wide linear adaptive filter to obtain two adaptive weight vectors; constructing an input matrix based on the sampling value of the input signal, and calculating an estimation signal of the moment based on the product of the input matrix of the moment and the two adaptive weight vectors; and calculating the difference value between the expected signal and the estimation signal at the moment, obtaining an estimation error signal, and updating the gain vector at the obtaining moment and the estimation error signal by using a minimum error mode criterion, updating the two adaptive weight vectors at the obtaining moment, and reconstructing the system weight vector at the moment of the wide linear adaptive filter. According to the invention, the recursive adaptive filtering algorithm resistant to impulse noise interference is realized with low calculation complexity by using weight decomposition and selection of a robust cost function.

The invention discloses a photovoltaic digital twin operation and maintenance platform oriented to multi-source collaborative diagnosis, and particularly relates to the technical field of photovoltaic operation and maintenance, normalization, time-frequency feature extraction and adaptive filtering are performed on component health degree and environmental parameters through edge nodes, and a lightweight encrypted performance abstract is generated, and is subjected to differential compression and synchronized to a cloud end; the cloud executes security federal fusion and dynamic reputation weighting on the abstract, and outputs a robust global performance vector; the collaborative optimization engine calls reinforcement learning based on the vector and a historical strategy to generate an original power distribution strategy, and the original power distribution strategy is issued after being corrected through a projection gradient descent mapping function; an edge actuator loads an environment snapshot to perform parallel simulation verification and risk assessment in a digital twin environment, so that the problems of multi-source data isolation and privacy disclosure are effectively solved; a self-adaptive multi-resolution grid, heterogeneous acceleration parallel and distributed message scheduling are provided, and high efficiency and real-time performance of large-scale multi-physics coupling simulation are achieved.

The invention discloses a sea water area karst cave group transient electromagnetic collaborative investigation data processing method and system, and the method comprises the steps: transmitting a wide frequency spectrum excitation signal to the seabed through a shipborne transient electromagnetic transmitting system, and synchronously collecting a secondary field attenuation signal responded by a seabed medium through a pull-type receiving array, meanwhile, a distributed sensor network is arranged at a preset seabed node to collect background noise base data; performing motion attitude compensation on the secondary field attenuation signal to generate transient electromagnetic response data; constructing an adaptive filter based on the background noise base data, performing noise suppression processing on the transient electromagnetic response data, and outputting noise suppression data; and performing space-time alignment on the noise suppression data of the towed array and the seabed node data to form a three-dimensional space sampling data set. According to the method, high-precision fusion of deep and shallow detection data and quantitative evaluation of the stability of the karst cave are realized, and the detection reliability and the engineering risk early warning capability of the complex ocean karst area are remarkably improved.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention discloses a non-ferrous metal ingot detection method and equipment based on three-dimensional vision. Belongs to the field of nonferrous metal detection. The method comprises the steps of construction of a three-dimensional pixel model with laser projection structured light projection and line laser scanning camera shooting, adaptive filtering preprocessing, establishment of a three-dimensional point cloud model through deep learning, feature information extraction, deep learning algorithm identification and classification, production process problem analysis and optimization and the like in sequence. Therefore, high-precision, multi-angle and all-directional detection of the non-ferrous metal ingot is realized. The equipment comprises a multi-axis collaborative three-dimensional visual inspection subsystem consisting of a mounting bracket, an X-Y-Z servo motion platform, a line laser scanning camera and the like. According to the method, the quality detection precision and defect detection rate of the non-ferrous metal ingot can be effectively improved, detailed product quality analysis is provided, and the method plays an important role in improving the product quality, optimizing the production process and reducing the production cost.

The invention discloses a pressure damage classification system based on image processing, which belongs to the technical field of image processing and comprises a pressure damage image acquisition module, a pressure damage image preprocessing module, a pressure damage classification model construction module and a pressure damage classification module. According to the method, a comprehensive noise characteristic value is obtained based on a pixel variance, an LBP value variance and a distance weight, an adaptive filtering weight is calculated, bilateral filtering and Gaussian filtering are fused for denoising, a detail enhancement factor is generated in combination with a local entropy value and a Sobel gradient amplitude, detail enhancement is carried out, the local contrast is enhanced, and the image quality is remarkably improved; according to the method, multi-scale features are extracted and fused, a space-channel attention mechanism is introduced to obtain a global weight, global enhanced fusion features and local enhanced fusion features are obtained, and comprehensive features are generated through fusion, so that the capability of identifying stress damage in different stages is remarkably improved.

The invention relates to the technical field of biomedical signal processing, and discloses a multi-wavelength PPG signal motion artifact elimination algorithm and system. The method comprises the following steps: acquiring a multi-wavelength PPG signal by using acquisition equipment; inputting the acquired noise-containing multi-wavelength PPG signal into a filter; the filter processes an input signal and then generates a denoised PPG signal; extracting a period of the denoised signal in real time by using an autocorrelation function; inputting the denoised PPG signal and a period extracted in real time into a BSS algorithm to find an optimal noise reference signal; taking the denoised PPG signal and the optimal noise signal as a first input and a second input of adaptive filtering to eliminate motion artifacts; the motion artifacts are eliminated by using the algorithm, and finally the PPG signals with the motion artifacts eliminated are output; the method overcomes the problems of dependence on additional hardware, low correlation of noise reference signals, complex algorithm and the like in the prior art.

The invention discloses a time division duplex multi-carrier synchronous transmission method based on 5G, which relates to the technical field of communication, and comprises the following steps of: establishing a carrier mapping table, respectively mapping a plurality of logic carriers to predefined physical resource blocks, and marking uplink and downlink switching time slots according to a time division mode; after a user terminal receives a synchronization signal, alignment is carried out through a high-precision local clock and the reference time of a main base station, and a synchronization reference point is recorded with a nanosecond-level time resolution. According to the invention, through the dynamic synchronization control and frequency domain interference suppression technology, the synchronization stability and the data transmission reliability in a high-speed scene are significantly improved; a synchronous load scoring mechanism is introduced to realize efficient scheduling and conflict avoidance of synchronous resources in a multi-user concurrent environment; and in combination with interference weight estimation and an adaptive filtering strategy, the anti-interference capability and environment adaptability of the system are effectively enhanced, and the requirements of key services such as the Internet of Vehicles and industrial control on low-delay and high-reliability communication are comprehensively met.