1530 results about "Adaptive filter" patented technology

The invention provides a method and system for enhancing wafer detection in a semiconductor manufacturing process, and relates to the technical field of wafer detection, and the method comprises the steps: transmitting dual-frequency laser to the surface of a wafer to form a controllable overlapping region, and collecting a beat frequency signal to obtain initial height information; carrying out adaptive filtering processing on the beat frequency signal, reconstructing a three-dimensional contour of the surface of the wafer, and generating an initial defect distribution diagram; and determining a secondary detection area according to the initial defect distribution diagram to perform orthogonal scanning, identifying and rejecting pseudo defect signals, extracting defect features, and determining defect cause types. According to the invention, the accuracy and efficiency of wafer defect detection are improved, and the semiconductor manufacturing quality can be improved.

The invention provides a braid quality detection system and method based on industrial camera detection, and relates to the technical field of data processing. A multi-angle acquisition module; an image fusion module; an optical correction module; a preprocessing module; a preliminary defect detection module; a fine defect analysis module; a self-adaptive filtering module; the dynamic coordinate mapping module is used for mapping the physical position of the defect to obtain actual coordinate data; the data storage and analysis module is used for storing and analyzing historical actual coordinate data and related detection data and generating a quality report and a trend analysis report; the alarm and control module is used for automatically sending out an alarm signal and adjusting production parameters or pausing production when the defect number or the trend change rate in the trend analysis report reaches a preset threshold value; according to the invention, the accuracy and efficiency of braid quality detection are improved.

The invention relates to the technical field of electromagnetic flowmeter calibration, and discloses an electromagnetic flowmeter online calibration system and an acquisition and storage device. The system comprises a signal acquisition module, a data processing module, an error analysis module, a parameter calibration module and a real-time monitoring module. The signal acquisition module captures an original signal waveform through a multi-channel sensor array and acquires flow signal data to generate a state set; the data processing module uses a wavelet packet transformation and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time warping error matching model to obtain an error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional integral differential composite strategy, and adjusts excitation current and electrode sensitivity according to an error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates a calibration instruction in combination with signal stability and power consumption limitation. According to the system, online accurate calibration of the electromagnetic flowmeter is realized, and the measurement accuracy and the equipment operation stability are improved.

The invention discloses a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: synchronously collecting a dielectric loss spectrum, a withstand voltage leakage current curve and a partial discharge spectrum of a distribution network cable through a phase resolution measurement device; processing the partial discharge spectrum by using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; constructing a cable multi-dimensional parameter normal domain hyperspherical model, and calculating a deviation distance value; and inputting the deviation distance value into a fuzzy inference engine, performing nonlinear conversion according to a preset fuzzy rule base, generating an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizing accurate evaluation of the cable insulation state. According to the invention, the accurate evaluation of the cable insulation state and the accurate positioning of the fault position are realized, and the operation reliability and maintenance efficiency of the power distribution network are improved.

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 relates to the technical field of communication control, and discloses an interconnection test method and system for a wireless communication module, and the method comprises the steps: collecting real-time communication data through a multi-source communication interface, extracting topological features through a graph theory model to generate a matrix, and obtaining a signal optimization parameter through a self-adaptive filtering model; and constructing a multi-target test path planning model and outputting optimal path data, and establishing a layered test execution model to complete the test. The system comprises a communication topology analysis module, a path dynamic planning module and a hierarchical test execution module. The method overcomes the defects of a traditional testing method, can comprehensively collect data, accurately analyze topology, effectively process signals, scientifically plan a testing path and perform layered collaborative testing, has remarkable effects in improving testing accuracy, efficiency and comprehensiveness, can adapt to the development requirements of a wireless communication technology, and ensures stable and reliable work of a wireless communication module.

The invention relates to the field of image processing, in particular to a seamless steel tube defect detection method based on image processing, which comprises the following steps: acquiring an image of a sized steel tube through a high-resolution industrial camera, and preprocessing to enhance the image quality; designing an adaptive filter based on image gradient direction and spatial frequency characteristics, and enhancing defect characteristics; extracting a defect region by adopting adaptive threshold segmentation and morphological operation, and constructing a comprehensive feature vector through a Hu moment and a gray-level co-occurrence matrix; and finally, defect identification is realized by using a support vector machine classifier, and technical support is provided for steel pipe production quality control. According to the method, the adaptive filter is constructed based on the gradient direction and the spatial frequency, and efficient detection and classification of seamless steel pipe surface defects are realized by combining the comprehensive feature vector and the support vector machine classifier.

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention provides a software development project progress prediction management method based on artificial intelligence, and the method comprises the steps: obtaining a project module dependency graph, analyzing the dependency intensity and influence range between modules, and determining the weight of a key module and the weight of a dependency module; according to the skill level of developers, assigning task priorities and combining technical complexity, calculating development time estimation of each module, and generating an initial progress plan; the development progress of the key module is monitored in real time, if lagging is found, a resource allocation mechanism is triggered, the task priority is evaluated again, and distribution of developers is adjusted; and if the deviation between the output of the progress prediction model and the actual situation exceeds a preset threshold value, triggering a model self-learning mechanism, retraining the progress prediction model, and updating the Bayesian network and the adaptive filtering parameters.

The invention relates to the technical field of anti-interference shielding, and discloses a new energy wire harness anti-interference shielding optimization method, device and equipment and a storage medium, and the method comprises the steps: sampling voltage and current signals at the two ends of a plurality of wire harnesses in a new energy automobile, and obtaining the original data of a plurality of wire harness signals; carrying out transformation and interpolation processing to obtain multiple pieces of wire harness signal harmonic component data; carrying out calculation and adaptive noise elimination to obtain a plurality of wire harness anti-interference performance indexes; filter parameter optimization calculation is carried out, and adaptive filter optimization parameters are obtained; creating a multi-stage filtering structure and carrying out eigenvalue decomposition orthogonal optimization to obtain multi-stage filtering structure parameters; according to the method, the whole vehicle electromagnetic compatibility of the new energy vehicle is optimized in a distributed mode, a whole vehicle wire harness anti-interference shielding optimization scheme is obtained, the method can dynamically adapt to the complex electromagnetic environment, wire harness anti-interference shielding optimization with local optimization and global optimization is considered, and the whole electromagnetic compatibility of the new energy vehicle is improved.

The invention relates to a heart rate prediction and model training method and system, wearable equipment and a medium. The training method comprises the following steps: acquiring a training sample set and a corresponding label set; wherein each training sample of the training sample set comprises a photoelectric volume pulse wave sequence and a corresponding acceleration sequence, and the label set is a real heart rate; preprocessing each training sample to obtain a corresponding photoelectric volume pulse wave frequency spectrum sequence and an acceleration frequency spectrum sequence; all the photoelectric volume pulse wave frequency spectrum sequences and the acceleration frequency spectrum sequences are input into a heart rate prediction model to be trained, and corresponding predicted heart rates are obtained; and calculating the difference degree between the predicted heart rate and the corresponding real heart rate, and updating parameters of the heart rate prediction model based on the difference degree to obtain a trained heart rate prediction model. According to the method, the problems of high signal processing cost and high calculation complexity caused by the use of a complex adaptive filtering process in the prior art are solved, and the heart rate prediction accuracy can be remarkably improved.

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 technical field of heat dissipation control, and discloses a heat dissipation fan multi-order filtering control method and device, and the method comprises the steps: collecting the operation parameters of a multi-fan heat dissipation system, and carrying out the construction of a dynamic state space model, and obtaining a system operation state equation and a temperature observation equation; inputting the system operation state equation and the temperature observation equation into an expectation maximization sliding window iteration extended Kalman filter to obtain a system state estimation value and a prediction error covariance matrix; performing three-stage series design on the cut-off frequency of the low-pass filter, the passband range of the band-pass filter and the coefficient of the adaptive filter to obtain a system multi-order filtering control model; based on a system multi-order filtering control model, an airflow coupling influence coefficient matrix among multiple fans is calculated, the total energy consumption, the noise level and the heat dissipation efficiency are subjected to optimization solution, a fan control optimization parameter set is obtained, the heat dissipation efficiency is guaranteed while the energy consumption is reduced, and dynamic balance among all control units is achieved.

The invention discloses a multi-scale feature fusion fault prediction method for a high-speed rail bearing. The method comprises the following steps: acquiring a fault vibration signal of the high-speed rail bearing through a vibration signal comparison technology; noise and interference signals of the high-speed rail bearing fault original vibration signals are removed through an adaptive filter; performing multi-level decomposition on the vibration signal by using wavelet packet decomposition, extracting sub-band signals of different frequency bands, calculating instantaneous frequency and energy distribution of the sub-band signals by using Hilbert-Huang transform, and forming a time-frequency-energy three-dimensional feature map; carrying out weight distribution on each feature dimension in the three-dimensional feature map by adopting a self-adaptive weighting algorithm; constructing a training data set through the time-frequency-energy three-dimensional feature map and the fault type label; and training the long short-term memory network LSTM by using the training data set, constructing a fault prediction model, and performing fault prediction on the high-speed rail bearing. According to the invention, accurate prediction of the high-speed rail bearing fault is realized, and the accuracy of fault detection is 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 radio monitoring, in particular to a spectrum analysis device and method for a radio monitoring system and a storage medium, and the method comprises the steps: obtaining a corresponding environment noise level in a monitoring process, adjusting a sensitivity threshold value corresponding to signal detection based on the environment noise level, receiving a corresponding to-be-analyzed radio frequency signal, and preprocessing the received to-be-analyzed radio frequency signal based on an adaptive filter; calling a pre-trained spectrum analysis model, inputting the preprocessed to-be-analyzed radio frequency signal into the spectrum analysis model, and obtaining a corresponding spectrum classification result; and calling frequency information and time sequence data corresponding to a radio frequency signal to be analyzed, obtaining corresponding spectrum analysis data based on the spectrum classification result, the frequency information and the time sequence data, and displaying key parameters in the spectrum analysis data through a graphical interface. According to the invention, the effectiveness of signal detection and identification can be improved.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses an unmanned aerial vehicle millimeter wave radar detection system and method and an unmanned aerial vehicle, and the system comprises a millimeter wave radar module which is used for executing multiple detection tasks and detecting the distance, speed and angle information of a target object, and has the functions of beam scanning and signal transmitting and receiving; the signal processing module is used for processing the received radar echo signal, performing target object detection and positioning through signal analysis, and generating a target positioning result; the target tracking module is used for tracking the target according to the target positioning result and the sensor data of the unmanned aerial vehicle; the method improves the signal intensity and detection distance in different detection environments by optimizing the direction and focusing of radar beams in real time, effectively eliminates noise interference through the Kalman filtering and particle filtering technology, accurately estimates the distance, speed and angle information of a target, and improves the detection accuracy through combining a deep learning algorithm and an adaptive filtering strategy. And processing details of the radar echo signals are dynamically optimized.

The invention provides a satellite navigation interference detection and suppression system and method, and belongs to the technical field of signal analysis. The sensor module is used for determining a target area where a sensor is installed according to a target detection sky, and deploying and installing the sensor in the target area to obtain original signal data of a satellite; the signal processing module is used for standardizing the original signal data to obtain standard signal data and converting the standard signal data into a first frequency spectrum; the feature recognition module is used for analyzing features of the first frequency spectrum through fast Fourier transform and screening signal points with abnormal fluctuation; the interference analysis module is used for classifying the features of all the signal points and matching corresponding interference categories to obtain all interference categories of the original signal data; and the interference suppression module is used for adjusting parameters in real time to reduce interference by using an adaptive filter according to the dynamic characteristics of the interference category. The reliability of navigation signals is improved, and the anti-interference capability is enhanced.

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 relates to the technical field of audio noise reduction, in particular to a hybrid noise reduction method and system for a Bluetooth headset. The method comprises the following steps: obtaining multi-dimensional environment noise data based on an environment noise sensor, and carrying out noise component feature analysis to generate environment noise multi-frequency component features; performing intelligent noise reduction intensity and self-adaptive filtering noise reduction suppression on the multi-frequency component characteristics of the environmental noise so as to generate an external environmental noise reduction suppression strategy; a micro audio signal is sent based on an ear canal form sensor, and audio signal feedback information is collected; performing three-dimensional ear canal structure modeling according to the audio signal feedback information so as to construct a three-dimensional ear canal structure model of the user; and obtaining a noise reduction feedback log of the wearer, and carrying out personalized noise reduction demand analysis to obtain the noise reduction demand of the user in the current scene. According to the invention, through comprehensive and finer mixed noise suppression, the audio quality and stability of the Bluetooth earphone are improved.

The invention relates to the field of signal processing, and discloses a biological signal processing method and device, computer equipment and a storage medium, and the method comprises the steps: collecting a biological signal, and carrying out the preliminary filtering of the biological signal, and obtaining a preliminary filtering signal; performing noise characteristic analysis on the preliminary filtering signal, selecting a proper filtering mode according to an analysis result, and designing filter parameters through fuzzy logic processing; inputting the filter parameters into an adaptive filter to process the signal so as to obtain a filtered biological signal; in combination with a de-noising convolutional neural network model, noise identification and fuzzy logic processing are performed on the filtered biological signals, and filter parameters are dynamically adjusted; and performing multi-round iterative optimization until a target filtering effect is obtained, and outputting an optimized biological signal. According to the biological signal processing method, complex noise in the biological signal is effectively removed, the filtering precision is improved, meanwhile, the signal integrity is kept, and a reliable basis is provided for subsequent biological signal analysis and processing.

The invention relates to the technical field of adaptive filtering, and discloses a deviation compensation constraint adaptive filtering method and system based on error nonlinearity, and the method comprises the steps: constructing an adaptive weight vector and a noise-containing input signal vector according to an adaptive weight and a noise-containing voice signal sampling value when the weight of an adaptive filter is subjected to linear constraint; after the inner product generates an output signal, an estimation error signal is generated, and an error square median is calculated; calculating an error signal variance estimated value and a self-adaptive weight vector power estimated value according to the error square median and the self-adaptive weight vector, and then calculating an input noise variance estimated value; calculating a deviation compensation item according to the adaptive weight vector and the input noise variance estimation value, and updating the adaptive weight vector with the noise-containing input signal vector and the estimation error signal; and calculating the inner product of the updated adaptive weight vector influenced by the low-efficiency input noise and the noise-containing input signal vector to obtain an output signal subjected to input noise deviation compensation.

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 discloses a signal monitoring method, system and device and a storage medium, and relates to the field of engines, and the method comprises the steps: carrying out the adaptive filtering processing of a rotating speed signal in a collection period, and obtaining the filtering signal data; performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; performing frequency domain analysis on the time domain signal data to identify a target frequency in a set frequency range; and if the accumulatively identified target frequency reaches a set alarm condition, triggering an alarm process. At the adaptive filtering position, noise and interference components in the signals can be effectively removed; local features of the signals can be extracted by performing time domain analysis on the filtered signals by using a sliding window technology. Furthermore, the target frequency in the set frequency range is identified through frequency domain analysis, and the periodic mode and key frequency components in the engine rotating speed signal can be accurately captured. The abnormal state of the engine in operation is effectively recognized, and potential faults or damage is prevented.

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.