28 results about "Iterative filtering" patented technology

The invention discloses a power cable outer sheath fault positioning method, and belongs to the technical field of cable fault positioning. Aiming at the problems of poor adaptability, weak anti-interference capability and low positioning precision of the existing cable outer sheath fault positioning technology, the method comprises the following steps of: acquiring horizontal and vertical components of an electromagnetic signal by deploying an array sensor, and obtaining pure vector distribution through Fourier transform and wavelet denoising processing; judging coordinates of an abnormal point according to an amplitude threshold value and a phase ratio, and determining a fault cluster center by adopting K-means clustering; and fusing the environmental data, refining the position through iterative filtering, comparing and confirming a final fault point in combination with triangulation and historical data, and generating a vector distribution map and a diagnosis report. According to the invention, high-precision and anti-interference positioning and visual diagnosis of cable faults in a complex environment are realized.

According to the switch cabinet vibration signal positioning method and device, the storage medium and the computer equipment provided by the invention, in the operation process of the switch cabinet, the vibration signals generated by the switch cabinet are collected in multiple directions, amplitude detection is carried out after multi-channel time sequence data are formed, and signal segments with potential defects are extracted from the multi-channel time sequence data; further generating a vibration event data packet corresponding to each vibration signal so as to carry out targeted analysis; as the convolution iteration filtering algorithm has better anti-noise capability and higher time resolution, the method can be adopted to extract the signal first arrival time of each vibration event data packet more accurately; and directly performing time difference analysis on the first arrival time of each signal by using the mathematical relationship between the time difference and the vibration source point position established in the finite difference time domain simulation model to obtain the defect vibration source point position of the switch cabinet, so that large-scale disassembly or complex measurement of the switch cabinet is not needed, and the time difference analysis efficiency is improved. Therefore, the position of the defect vibration source point can be accurately determined.

The invention provides a method and device for determining hydraulic damping of an underwater rotating disc, and relates to the technical field of hydrodynamics, the method comprises the following steps: obtaining a vertical displacement vibration signal of the rotating disc in a composite mode through vibration mode measurement; performing iterative filtering on the vertical displacement vibration signal to obtain a target signal of the rotating disc in a pitch circle mode; fitting a vibration response function of the rotating disc based on the target signal; based on the vibration response function, establishing a frequency response function of the rotating disc; determining the inherent frequency of the rotating disc based on the frequency response function; based on the rotating frequency of the rotating disc, performing dimensionless processing on the inherent frequency to obtain the falling frequency of the rotating disc; and based on the drop frequency, performing linear fitting regression on the hydraulic damping of the rotating disc to obtain a relational expression between the hydraulic damping and the drop frequency. According to the method for determining the hydraulic damping of the underwater rotating disc, the hydraulic damping of the rotating disc is accurately measured.

The invention discloses an HDMI wire harness signal quality reduction positioning method, which comprises the steps of obtaining signals at two ends of a wire harness through a time domain reflection measurement method, collecting waveform data by adopting high-speed sampling equipment, determining potential defect points and physical characteristics of the potential defect points by utilizing signal propagation time and reflection characteristic analysis and combining Fourier transform, and positioning the potential defect points and the physical characteristics of the potential defect points. Accurate coordinates of defect points are obtained through joint calculation, the signal quality decline degree is quantified, a signal quality decline spatial distribution diagram is generated through a three-dimensional interpolation algorithm, and visual display is achieved. In order to improve the detection efficiency, a convolutional neural network is introduced to train historical data, a positioning precision model is optimized, and finally, high-precision signal quality reduction position distribution is obtained through an iterative filtering method. According to the method and the device, accurate positioning and visualization of the HDMI wire harness signal quality problem are realized, the fault diagnosis efficiency and accuracy are improved, and a reliable basis is provided for production optimization of the HDMI wire harness.

The present application relates to the technical field of Internet detection, and particularly relates to an attribute network abnormal node detection method and system based on iterative filtering, which takes an attribute network adjacency matrix and an attribute matrix as training data input of an abnormal node detection model, determines abnormal nodes in a to-be-detected attribute network and outputs by using an iterative filtering model training process, and in each iterative filtering model training, detects and filters abnormal nodes in the training data and updates the training data until no new abnormal node is generated in the next iterative filtering training data. The present application ensures the homogeneity assumption of the network by the iterative filtering model training process, avoids mutual interference between abnormal features and normal nodes, causes misjudgment of edge nodes, improves the detection effect of the model, and when compressing node feature extraction, not only aggregates neighbor information but also simultaneously fuses node self information, so that the extracted features contain neighbor features and retain self features to the greatest extent, avoid dilution of the features of the nodes, improve the accuracy of the detection, add node and global similarity calculation to improve the interference of reconstruction error on the abnormal misjudgment of the nodes, enhance the model recognition ability for network abnormal nodes, and have a good application prospect.

The invention relates to the technical field of seismic exploration data processing, in particular to a depth domain seismic data volume high-wavenumber imaging noise iterative filtering method and a depth domain seismic data volume high-wavenumber imaging noise iterative filtering device. The method comprises the following steps: acquiring parameter information of an initial depth domain seismic imaging data volume and a velocity model; carrying out numerical discretization on the diffusion filtering equation according to the parameter information according to a set time step length, a diffusion coefficient and a grid step length, and constructing a first filtering coefficient; according to the parameter information, carrying out numerical discretization on the seismic wave equation according to a set time step length, a speed model and a grid step length, and constructing a second filter coefficient; constructing a filtering operator according to the filtering coefficient; and carrying out imaging noise iterative filtering processing on the initial depth domain seismic imaging data volume by using a filtering operator to obtain a filtered depth domain seismic data volume. The problems of trace processing channel by channel and high-wave-number imaging noise amplitude-preserving suppression are effectively solved, so that the false appearance of reservoir imaging can be eliminated, and depth domain structure interpretation and reservoir research are facilitated.

This application relates to a microscope image deblurring method based on adversarial networks and iterative filters. This method utilizes deep learning to learn the relationship between deblurring in out-of-focus and fully focused images under an optical microscope, thereby achieving deblurring of data acquired through optical microscopy. The iterative filtering approach effectively removes localized blur under the microscope, solving the problems of image blurring and incomplete detail in optical microscope images.

The application discloses a hyperspectral remote sensing image change detection method based on adaptive iterative filtering, and specifically comprises the following steps: step one, selecting three bands with the largest information quantity from two groups of hyperspectral remote sensing images at different times; step two, writing the selected two groups of bands into the red, green and blue bands of a natural image in the order of the index value from small to large; step three, performing adaptive region growing on the two pictures pixel by pixel, if the difference between the pixel value of the neighborhood and the center pixel is less than the standard deviation of the eight-neighborhood pixels, the pixel is merged into the adaptive region, otherwise, the pixel is not merged; step four, performing region growing on the two filtered pictures in step three again to obtain a change amplitude graph; and step five, calculating the threshold of the obtained change amplitude graph to obtain a final change detection result graph. By adopting the method, the homogenous regions on the picture are more obvious, and the precision of the hyperspectral remote sensing image change detection is improved.

This invention discloses a single-frame coded phase retrieval system and parameter calibration method based on iterative filtering inversion, belonging to the field of lensless computational microscopy imaging technology. It solves the problems of mismatch between single-frame coded phase retrieval algorithms and complex coding scenarios, difficulty in balancing noise suppression and resolution, large system diffraction distance estimation errors, and inaccurate wavefront lateral offset calibration, leading to low imaging accuracy and poor stability. This invention constructs a system including a light source, replaceable amplitude / phase coding, CMOS imaging, one-dimensional electric displacement drive, and a built-in IrCPR algorithm control module, along with a process of "multi-distance calibration of coding functions → Z1 / Z2 calibration using ToG / SG according to coding board type → cross-correlation spectrum registration." This invention improves the accuracy of coding function calibration and diffraction distance estimation, achieves pixel-level alignment of the coded wavefront, and completes complex amplitude reconstruction with a single frame acquisition. The system has strong adaptability and stability, making it suitable for high-precision microscopy imaging scenarios.

The invention discloses an RS-485 bus-based equipment unique identification code acquisition method, which is suitable for communication between a host and a plurality of slaves, and each slave comprises an equipment memory module used for storing an identification code with uniqueness of equipment; according to the acquisition method, matrix coding is used for communicating and interacting with a host and a slave, accurate positioning is realized by adopting an iterative combination mode according to conflict dynamic screening, and thus the unique identification code for identifying the equipment is acquired. By adopting the acquisition method provided by the invention, the communication complexity can be remarkably reduced through matrix coding, staged verification and an iterative screening mechanism, so that the automatic identification requirement of the equipment is met, and the acquisition method can be widely applied to large-scale equipment networking scenes such as intelligent power grids and industrial automation; and bottom support is provided for equipment management, fault location and security authentication.

The present application relates to a kind of Vienna rectifier wide load range operation control method mode, proposes a load power estimation algorithm, by collecting three-phase voltage, three-phase current, DC side upper and lower voltage and combining forgetting iterative filtering algorithm and sliding window filtering algorithm, accurately estimate load power and filter out the noise caused by sampling error.Based on the proposed load power algorithm, a new outer ring structure is proposed, which uses capacitor energy storage feedback and load power feedforward control to separate the control of DC side energy storage capacitor and DC load control.When load mutation occurs, the demand of load can be estimated in advance, and the calculation is introduced to the current loop, which improves the response speed of the system and better stabilizes the DC side voltage.The present application solves the problem of parameter design difficulty of the outer ring of traditional control mode of Vienna rectifier, improves the response speed when load mutation occurs, and solves the overvoltage problem during light load start.

The invention discloses an IQ imbalance calibration method and system. The method comprises pre-calibration and fine calibration. During pre-calibration, firstly, M frequency points are selected in a sectional mode, narrow-band calibration is carried out, correlation factors of amplitude imbalance errors and phase imbalance errors of the multiple frequency points are obtained, interpolation fitting is carried out on zero frequency points, and frequency response characteristics in the whole bandwidth are obtained. On the basis, a solution equation is constructed to calculate a filter coefficient, and a filter is designed to compensate the IQ imbalance error. During fine calibration, based on the least square method, the compensation coefficient of the filter is calculated in a real-time and self-adaptive manner, whether iteration is updated or not is judged, iterative filtering is carried out to enhance the stability of the system, and the problem of calibration performance deterioration caused by environmental factors such as temperature change is effectively solved. Compared with traditional narrowband calibration, the calibration system combining pre-calibration and fine calibration has a calibration effect on receiver large-bandwidth IQ imbalance, the adaptive capacity of the calibration system to environment temperature changes is enhanced, and the calibration precision and robustness are improved.

The invention provides a data filtering method, system, device and product for automobile user comments, relates to the technical field of comment data processing, and realizes efficient identification and continuous optimization elimination of unreal user comments by constructing a dynamic filtering mechanism oriented to the automobile field. The method comprises the following steps: firstly, carrying out preliminary screening on multi-source heterogeneous comments based on a preset rule base, then carrying out semantic segmentation and high-frequency word statistics in combination with a field customized stop word list, automatically mining potential non-real user identification keywords by utilizing an inter-class distinction degree index, and dynamically updating the rule base; and through multi-round iterative filtering, it is ensured that a newly appearing false comment mode can be captured in time and brought into an identification range until the system converges. According to the technical effects, the purity and representativeness of a real user comment data set are remarkably improved, and a high-credibility data basis is provided for subsequent user insight, product improvement and market decision.

The invention discloses a structure tensor anisotropic diffusion interference suppression method guided by a time-frequency energy threshold, and relates to the technical field of radars, and the method comprises the steps: determining a mixed signal; performing short-time Fourier transform on the mixed signal to obtain a time-frequency diagram; calculating an energy boundary threshold value based on the relationship between the interference-to-signal ratio and the energy of the time-frequency diagram; a core layer is determined from the time-frequency graph based on the energy boundary threshold value, a structure tensor is determined by performing Gaussian smoothing operation on the core layer, and the core layer is an interference core area; using the empirical coefficient and the structure tensor to construct a diffusion tensor used for controlling the diffusion direction and intensity; and carrying out anisotropic diffusion iterative filtering on the time-frequency diagram by using the diffusion tensor to obtain an anti-interference mixed signal, thereby providing an interference suppression method which can realize self-adaptive suppression and maximally maintain target information under the condition that an interference model, threshold experience or a training sample does not need to be preset.

The invention discloses a transformer substation scene-oriented point cloud denoising method and system, and relates to the technical field of three-dimensional point cloud data processing, and the method comprises the steps: obtaining the multi-source remote sensing data of a transformer substation, generating a target point cloud through point cloud normalization, and carrying out the flow field-driven convolution filtering through an iteration filtering module connected in series in a point cloud iteration enhancement filtering network, noise linear path migration is guided through flow field modeling, local-global correction is optimized through multi-module iteration, the high-density arrangement characteristic of substation equipment is adapted, and the efficiency, precision and complex scene adaptability of point cloud denoising are improved.

The invention provides a fault source positioning method and system based on Wiener iterative filtering. The method comprises the following steps: realizing synchronous acquisition of noise-containing partial discharge signals through a multi-channel sensor array arranged at two ends of a stator bar or along the line; performing adaptive noise reduction on the signal by using an iterative Wiener filter so as to highlight a discharge characteristic; estimating the relative time delay of the discharge pulse reaching different sensors according to the filtered signal; and based on the time delay, determining the position of the defect in the stator bar through a time difference positioning mechanism. According to the method, high-intensity background noise mixed in test signals can be effectively filtered out, the recognition degree of discharge characteristic signals is remarkably enhanced through optimization iteration of the weight of the self-adaptive filter, and high-precision judgment of the position of a defect source is achieved in combination with a time difference positioning mechanism. The method is not only suitable for partial discharge signal processing in power equipment, but also provides an efficient and stable signal processing tool for identifying an abnormal discharge event in a time domain dielectric response signal.

The application discloses a tool wear prediction method based on a multi-element fast iterative filtering decomposition method and relates to the tool wear prediction field. The tool wear prediction method comprises the following steps: S1, establishing a wear prediction model based on a support vector machine model; S2, collecting original machining signals of a milling process of a machine tool through three-way vibration sensors arranged on a machine tool spindle; and S3, decomposing the original machining signals by using a multi-element fast iterative filtering decomposition method to obtain a plurality of multi-channel intrinsic modal components. The tool wear prediction method can predict the wear condition of a tool during a tool mechanical milling process based on an indirect monitoring method, does not need to stop and restart the equipment, reduces the milling precision error caused by frequent shutdown and disassembly of the tool, can reduce the detection time of the tool, does not affect the production efficiency, can predict the wear condition of the tool in time, and can replace the tool before the tool reaches the maximum wear value.

The application discloses a three-dimensional grid data filtering method and device, equipment and a storage medium, wherein the method comprises: obtaining an initial three-dimensional grid model; determining that the curvature of any vertex in the initial three-dimensional grid model is greater than or equal to the curvature threshold of the three-dimensional grid model; performing the following iterative filtering processing on the initial three-dimensional grid model: performing feature recognition on the initial three-dimensional grid model based on the curvature of each vertex in the initial three-dimensional grid model to determine a non-feature area and a feature area in the initial three-dimensional grid model; performing filtering processing on the non-feature area and the feature area respectively by using a Laplace filtering algorithm and a feature recovery algorithm to obtain a target three-dimensional grid model; and in the case that the curvature of each vertex in the target three-dimensional grid model is less than the curvature threshold, it is determined that the filtering of the initial three-dimensional grid model is completed.

The invention discloses a fetal heart monitoring method and system based on morphological feature visualization. The method comprises the steps that a fetal heart rate baseline and an acceleration and deceleration section of a fetal heart rate signal are extracted; extracting a uterine contraction base line and a uterine contraction state of the uterine contraction signal; fetal heart monitoring is achieved according to the acceleration and deceleration sections of the fetal heart rate signals and the uterine contraction state of the uterine contraction signals. The iterative filtering pruning baseline extraction algorithm adopted by the invention can effectively resist the interference of peak, loss and high-frequency noise in the signal through a multi-round threshold convergence strategy.

The invention provides a self-adaptive unscented Kalman filter laser radar signal processing method and system, and belongs to the field of laser radar signal processing. The method comprises the following steps: constructing a laser radar detection physical model containing thick-tail distribution noise characteristics; performing initialization operation; inputting an original noisy signal sequence, and carrying out iterative filtering processing on the original noisy signal sequence by detecting distance units; in each iteration loop, dual adaptive unscented Kalman filtering is synchronously executed; for the processed observation data of all the detection distance units, outputting a final posteriori state vector sequence of all the detection distance units, and reconstructing to obtain a high-fidelity laser radar echo signal curve; and extracting the high-fidelity optical parameters, and performing inversion on the high-fidelity optical parameters to obtain an inversion result. According to the method, non-Gaussian characteristics of environmental noise can be accurately represented and adapted, high-fidelity extraction and reconstruction of weak echo signals under the background of strong interference and strong attenuation are realized, and the accuracy and robustness of laser radar detection are remarkably improved.

Provided is a vehicle 4D millimeter-wave radar inertial odometry method and a computer-readable medium. The method includes: eliminating significant radar spatial noise through relaxation filtering; performing single-frame velocity measurement based on a selecting weight iteration-based robust estimation method, and eliminating dynamic noise; performing IMU observation integration by mechanical arrangement to obtain a predicted vehicle state; constructing velocity constraints based on single-frame velocity measurement and a predicted velocity to initially update the vehicle state; constructing a local map based on the previous point cloud frame, searching n-nearest neighbor points of each radar point in the local map at next moment, calculating a weighted distance from distribution to multi-distribution as matching constraints from point cloud to local map, and performing further updating based on iterative filtering to obtain a precise vehicle state; and updating the vehicle state at all moments by 4Dradar closed-loop detection and GICP to reduce the positioning drift.

The present application relates to a forest canopy height extraction method based on a spaceborne photon counting lidar, and belongs to the technical field of photon point cloud data processing.The present application comprises the following steps: firstly, accurately dividing signal and noise data of photon point cloud data; then, based on the separated signal data, fitting a ground curve by using an iterative filtering algorithm of local terrain, and removing pseudo-ground points by using mutation detection of a gradient descent method and decomposition and reconstruction functions of EMD, so as to accurately identify ground point and non-ground point data; finally, extracting canopy top points from non-ground points by using a dynamic binning statistical method, and calculating canopy height according to the height difference between the canopy top points and the ground points.The present application realizes accurate segmentation of signals and noises of different terrain scenes and determines a noise threshold; further removes pseudo-ground points and adjusts canopy top points under the premise of ensuring that ground signals are not distorted, and greatly improves tree height extraction precision.

The application provides a coagulation system alignment method and device based on iterative filtering and a storage medium. The method comprises the following steps: collecting the earth rotation angular velocity, the local latitude, the local gravity acceleration value, the gyro signal data and the accelerometer signal data of an inertial navigation system in real time; calculating the initial conversion matrix Ci0_n(t), the inertial system second integral vector VG2_i0(t) of the gravity acceleration, the carrier system second integral vector VF2_ib0(t) of the accelerometer and the intermediate conversion matrix Cb_ib0(t) based on the collected data; calculating the Euler angle tlt(t) in real time according to the obtained VG2_i0(t) and VF2_ib0(t); calculating the updated conversion matrix Cib0_i0(t) based on the Euler angle tlt(t); and finally calculating the obtained Ci0_n(t), Cb_ib0(t) and Cib0_i0(t) to obtain the attitude matrix, so as to obtain the pitch angle, the roll angle and the heading angle information of the target object and complete the alignment. In addition, the application further provides a coagulation system alignment device based on iterative filtering and a storage medium. The application can not only store a large amount of intermediate data, but also can calculate the attitude angle in real time, and is simple and efficient to implement.

This invention provides a swarm cooperative navigation method based on particle distance fitness, relating to the field of multi-agent cooperative navigation technology. The method includes: in a multi-UAV cooperative formation composed of multiple UAVs, establishing a system motion model based on uniform acceleration curve kinematics and constructing a system measurement model using relative ranging constraints measured by ultra-wideband; based on the constructed system motion model and system measurement model, constructing a swarm cooperative navigation filtering model; and based on the constructed swarm cooperative navigation filtering model, using an error-constrained quantum particle swarm optimization improved particle filtering algorithm based on particle distance fitness for iterative filtering to obtain the optimized results of the state variables of each UAV. This invention can effectively improve the positioning accuracy and robustness of swarm UAVs in the event of global satellite navigation system rejection.

The application provides a kind of star inertial installation error calibration method and system based on iterative filtering, for the installation error between inertial navigation and star sensor affects the problem of star-sensitive attitude determination accuracy, the state space model and measurement model of iterative extended Kalman filter are constructed through the star vector under the body coordinate system of star sensor, ephemeris star vector and inertial information under the inertial coordinate system, the measurement jacobian matrix is obtained by derivation and calculation, the measurement jacobian matrix is substituted into the measurement model of iterative extended Kalman filter to iteratively estimate the installation error of inertial celestial combined navigation system, i.e. the installation error of inertial celestial combined navigation system, the function of calibrating the installation error of inertial celestial combined navigation system is realized.The application eliminates the influence of the installation error between inertial navigation system and star sensor on star-sensitive attitude determination, is suitable for combined navigation application occasion, and has good adaptability and high precision.

This application provides a data filtering method, device, and storage medium, relating to the field of artificial intelligence chip technology. The method includes: performing multiple rounds of iterative filtering on a target dataset using a thread bundle and a target queue, and obtaining the target filtering result based on the K data items retained in the target queue at the time of termination; each round of iterative filtering includes: reading data from the target dataset in parallel using multiple threads in the thread bundle; for the first thread whose read data meets the enqueueing condition, writing the corresponding read data into the target queue in parallel according to the write offset of each first thread to achieve conflict-free parallel enqueueing; when the target queue is full or the target dataset has been read, sorting the data in the target queue and removing data other than the first K data items from the target queue; and updating the enqueueing condition based on the Kth data item in the target queue; this greatly reduces the amount of data involved in sorting, thereby reducing chip resource consumption and improving the efficiency of data filtering.

The present application relates to the technical field of machine vision detection, and in particular to a printing label anti-counterfeiting identification method and system based on machine vision, which utilizes multispectral imaging to collect data to generate an initial spectral image set; adopts refraction correction modeling to peel off the interference signal of the transparent coating layer; through iterative filtering, residual noise is filtered out to generate a pure signal; a layer separation and spatial decoupling algorithm is used to analyze the multi-layer structure and extract a feature vector; the distribution of hidden anti-counterfeiting codes and fiber patterns is analyzed in combination with coding rules; a pattern matching and threshold determination logic is used to generate a label authenticity determination result; and a continuous identification optimization report is generated based on dynamic feedback control. The present application can effectively eliminate complex coating layer interference, realize accurate analysis of the deep anti-counterfeiting features of multi-layer labels, and significantly improve the robustness and accuracy of anti-counterfeiting identification in a high-speed production environment.

The invention particularly relates to a forward scattering target detection method based on frequency domain steady-state reference, which comprises the following steps: carrying out time-frequency analysis on a received continuous wave signal, and constructing a peak power sequence reflecting signal power stability; according to the transient stationary characteristic of the sequence, automatically identifying and extracting a pure reference signal only containing a direct wave and quasi-static multipath; instantaneous phase information of a current to-be-processed signal frame is acquired, dynamic recombination is performed by using the instantaneous phase information and amplitude information of the pure reference signal, and a reconstructed reference signal highly coherent with the to-be-blanked direct wave and the multipath component is constructed; and taking the reconstructed reference signal as reference input of adaptive iterative filtering, taking a to-be-processed signal frame as expected signal input, and performing adaptive iterative filtering estimation and subtracting a direct wave and a multipath component to obtain a suppressed power spectrum and a peak sequence. According to the method, the problem that weak target signals are difficult to detect by strong direct waves and multipath components under a single-frequency continuous wave system is solved.