396 results about "Filter methods" patented technology

The invention discloses a GRACE and Swarm time-varying gravity field fusion filtering method based on a state space model, and the method comprises the steps: taking a spherical harmonic coefficient as a state quantity, constructing a random walk process equation, introducing three types of observations, employing a quantization parameter for observation noise and process noise covariance, constructing according to an order / power law, and carrying out the self-adaptive updating along with the monthly; a Nelder-Mead method is adopted to search for spectral index parameters, and an EM algorithm and a statistical method are utilized to update other parameters in a closed / quasi-closed mode; obtaining the state and posterior covariance of a full time sequence by using Kalman filtering and RTS smoothing; in the GRACE and GRACE-FO window period, continuous reconstruction is carried out by means of a process model and Swarm; and outputting quality evaluation information including monthly gravity field coefficients, posterior covariance, innovative variance ratio, residual whitening test, space power spectrum, uncertainty band and the like. According to the method, while physical rationality and calculation feasibility are ensured, a continuous and stable monthly time-varying gravitational field sequence with quantifiable uncertainty is realized.

The invention discloses a geometric constraint fitting point cloud filtering method for sea surface three-dimensional reconstruction, and belongs to the technical field of computer vision and three-dimensional reconstruction. The objective of the invention is to solve the problem of insufficient subsequent three-dimensional reconstruction precision caused by interference of reflection noise, mismatching points and the like in sea surface point cloud. The method specifically comprises the following seven steps: firstly, acquiring sea surface original point cloud through three-dimensional data acquisition equipment; a filtering technology is adopted to obtain a to-be-fitted point cloud; fitting a quadric surface through an improved RANSAC (Random Sample Consensus) algorithm to solve an initial parameter; constructing a comprehensive error function, and optimizing the model through gradient descent; effective inner points are screened through quadratic term coefficient constraint and a distance threshold value; and finally, iterating until a termination condition is met, and outputting an optimal effective point cloud. The method is high in noise rejection rate, the point cloud fits the sea surface form, and high-quality data support can be provided for sea surface fitting, sea wave simulation and unmanned ship control.

The invention discloses a robustness tracking filtering method for group targets, and belongs to the field of radar and signal processing. The implementation method comprises the following steps: establishing a kinetic model of a group target and a three-coordinate ground-based radar detection probability and measurement model; bayesian recursion of a multi-target state random finite set is realized by a Poisson random finite set, and an intensity function of the Poisson random finite set is described in a Gaussian mixture form. Estimating the maximum motion distance of the target between two adjacent filtering steps based on the dynamic characteristics of the group target, and achieving the detection of the target; the detection probability is described by using Bernoulli distribution, under the condition that the clutter intensity does not exceed the real target intensity, the prior clutter intensity is taken as a threshold value, the detection probability is adaptively adjusted in the filtering iteration process, the respective weights of the measurement irrelevant part and the measurement relevant part are updated, and the strong-robustness multi-target tracking under the condition of sensor leak detection is realized. The method also has the advantages of strong robustness, high tracking precision and small calculation amount.

The invention belongs to the technical field of hidden danger detection, and more specifically relates to a power transmission hidden danger filtering method and system fusing a visual model and spatial logic correction, and a medium. The method comprises the following steps: hidden danger candidate generation based on a high-performance target detection model: adopting a reference segmentation image dynamic multiplexing strategy to complete background semantic analysis of a power transmission channel inspection image, and generating a final reference segmentation image as a semantic mask; performing spatial mapping and logic correction on hidden danger candidate boxes and semantic masks; performing confidence weighting and soft filtering on boundary cases; identifying the boundary cases, constructing a three-dimensional confidence fusion model to calculate fusion confidence, realizing soft filtering according to the fusion confidence, and outputting a high-credibility hidden danger alarm list. The problems that in the prior art, due to the fact that a background semantic base is unstable and total image re-segmentation is frequently triggered, computing resources are wasted, scenes violating common sense cannot be recognized, and the uncertainty of a boundary area is lack of fine-grained description are solved.

The invention discloses an abnormal data real-time filtering method and system based on edge computing in a dynamic environment system, the method is executed by an edge computing gateway, sliding window weighted average preprocessing is equivalently realized by adopting integer shift operation, and the single computing overhead is controlled within 50 clock cycles; dynamically and adaptively adjusting dynamic reference model parameters based on the ratio of the network load to the sensor sampling frequency; performing multi-stage anomaly filtering of a hard threshold value, a mutation rate and a statistical interval on the data through a three-stage pipeline judgment structure executed by atomization; an FPGA hardware queue manager independent of a main processor bypasses a TCP stack to push abnormal data at the highest priority, and redundant data is stored in a zero-copy annular buffer area managed by DMA. According to the invention, the problems of intranet congestion, server I / O bottleneck and alarm delay under the centralized architecture of the traditional dynamic loop system are solved, and the fault, fault, fault, fault, fault and fault are realized on resource-limited platforms such as Cortex-M4 and the like; the average alarm delay is 8 milliseconds; and the data compression rate is more than 90%.

The invention relates to the technical field of point cloud data processing, and provides a ground filtering method and system based on cloth simulation parameter dynamic optimization, and the method comprises the following steps: obtaining point cloud data of a to-be-processed region, carrying out the preprocessing, constructing a complexity index, and calculating a complexity index value based on the preprocessed data; clustering the obtained complexity indexes by adopting a clustering method fusing an elbow rule and a spatial neighborhood constraint to obtain a terrain complexity category; and selecting distribution parameters of a distribution algorithm based on the terrain complexity category and the dynamic mapping of the constructed complexity category-optimal parameter group, and further performing ground point filtering and ground extraction by adopting the distribution algorithm. According to the invention, distribution simulation parameter configuration is guided through terrain complexity analysis, accurate adaptation of different terrain areas is realized, and the accuracy and global consistency of ground point extraction are improved.

The invention relates to a PID (Proportion Integration Differentiation) drawing element intelligent identification and topology reconstruction method based on visual detection, which comprises the following steps of: cooperatively extracting multi-modal information, detecting components based on improved YOLOv11, identifying all text labels in a graph by adopting PaddleOCR, and carrying out pipeline identification algorithm and merging filtering method based on probability Hough transform of pixel points. Identifying and defining the T-shaped connection point as a special topological node to obtain positioning information of components and characters in a PID drawing, associating the components with the characters by using a regular expression and an Euclidean distance, and converting image elements into a topological graph model with a semantic relationship; according to the method, the end-to-end automation process of detection-association-reconstruction is achieved, finally, display is conducted in a graphical interface mode, a data basis is provided for subsequent application such as drawing analysis, system simulation and equipment management, end-to-end automatic conversion is achieved, and efficiency is greatly improved.

Context-aware content filtering adapted for a knowledge domain is provided. In certain examples, a classification confidence score by a classifier indicates a level of confidence that a prompt from a user is associated with the knowledge domain. The classification confidence score is compared with a threshold. When the score is below a threshold, a violation notice is provided to the user without submitting the prompt to a generative artificial intelligence (GAI) model. When the classification confidence score is above the threshold, the prompt is further processed to determine, according to rules, whether the prompt should be submitted to the GAI model. In various examples, the rules are applied to contextual information, safety score information, and intent information derived from the prompt.

The invention relates to the technical field of intelligent traffic, in particular to a road traffic incident intelligent identification and filtering method and system, and the method comprises the steps: collecting video image data, radar point cloud data and environment sensing data, and generating the track information of a traffic target; performing space-time alignment and feature fusion on the trajectory information and the radar point cloud data to form multi-modal features; the method comprises the following steps: preliminarily identifying a traffic abnormal event based on multi-modal feature analysis, and generating event report information comprising an event image and associated features; performing deep semantic understanding on the event report information, analyzing the event image, the track information and the environment sensing data according to the associated features, and determining the semantic category of the traffic abnormal event; executing intelligent filtering processing based on the semantic category; and recording the traffic abnormality subjected to intelligent filtering processing as an effective traffic event, and generating a traffic recognition result. According to the method, deep semantic understanding is carried out through rich multi-modal features, and the overall accuracy and processing efficiency of traffic incident recognition are improved.

The invention provides a satellite-borne single-photon laser radar point cloud self-supervised filtering method and system, and relates to the technical field of laser radar data processing, and the filtering method comprises the steps: carrying out the point cloud self-supervised filtering through a core hypothesis that a source point cloud and a target point cloud which are randomly split from a same coarse filtering point cloud slice should have similar distribution; a completely self-supervised deep learning filtering framework is constructed, and efficient denoising of the satellite-borne single photon point cloud can be realized without manual annotation or external prior knowledge. The statistical consistency of the point cloud is used as a supervision signal, and the limitation that structural noise with a specific mode cannot be effectively filtered out through a traditional threshold value method is overcome. Distribution characteristics of the point cloud are automatically learned through a deep learning model, coordinate correction is carried out, full-automatic processing is achieved, and the processing efficiency of mass satellite-borne point cloud data is improved; according to the method, the fine filtering point cloud after coordinate correction is directly output, and terrain details can be better kept.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The invention provides a thermal elastic structure joint topological optimization method based on MATLAB and ABAQUS, and belongs to the technical field of thermal elastic structure optimization design. Firstly, finite element models of mechanical loads and thermal loads are established in ABAQUS, and files are exported; then calling an ABAQUS operation model in MATLAB, extracting core data such as nodes, units and a stiffness matrix, and constructing a parameterized thermodynamic finite element model based on an SIMP method; calculating a function value and sensitivity by taking volume minimization as a target and taking global stress and temperature constraint as conditions; and finally, iteratively optimizing the design variables by adopting a moving asymptote algorithm, and obtaining a final topological configuration through a filtering method. Through deep cooperation of MATLAB and ABAQUS, core finite element data of commercial software is extracted at a time for optimization iteration, repeated data generation and interaction are avoided, the calculation efficiency is greatly improved, and an efficient solution is provided for processing the topological optimization problem of stress and temperature constraints under complex heat-force loads.

The invention provides a zero-trust gateway encrypted traffic non-inductive filtering method and device based on an eBPF multi-level mapping architecture and a computer program product. The method comprises the steps that a data packet is intercepted in a network driving layer through an XDP mode of an eBPF, and quintuple and encryption protocol visible meta information are extracted; performing hierarchical strategy matching on the meta-information by adopting a multi-stage mapping architecture, and dynamically optimizing a search path through a performance model; and executing real-time verification and access decisions based on a zero-trust policy engine to realize non-perceptual filtering of encrypted traffic. According to the method, the problems of weak detection capability and high matching delay of a traditional scheme under encrypted traffic are solved, the method has the advantages of high speed, low delay and high precision, and meanwhile, data privacy and compliance are guaranteed.

The invention discloses a yellow storm terrorism content identification and filtering method and system, and relates to the technical field of digital content auditing, and the method comprises the steps: carrying out the modal decomposition and preprocessing of a to-be-detected content, including the calculation of a single-modal sensitive probability and confidence, and recording the coordinates and confidence of a local image region; calculating a dynamic weight and performing normalization processing based on the confidence and the prior credibility of each mode; calculating a final fusion score by combining the sensitive probability and the weight of each mode, and performing content auditing decision according to a preset threshold value; and the modal priori credibility is updated through manual feedback data. According to the method, the detection rate of cross-modal violation contents is remarkably improved through a multi-modal collaborative analysis and confidence-driven dynamic fusion technology; and the system adopts a three-level decision-making mechanism to automatically allocate auditing resources, so that the manual rechecking cost is greatly reduced, meanwhile, an online learning function is introduced to continuously optimize the model, the sensitivity to novel violation contents is kept, and the processing efficiency and detail identification are both considered.

The invention provides a segmented adaptive SG filtering method and device based on cosine weight transition. The method comprises the following steps: obtaining a noisy fault signal of a power distribution network; extracting local feature indexes of the signals, wherein the local feature indexes comprise an average gradient representing the change frequency of the signals and a composite feature index representing the noise level; dynamically determining a window length and a polynomial order of SG filtering based on the local feature indexes; the transient stage is the first 1 / 2 period of the post-fault signal, the steady-state stage is the subsequent part of the post-fault signal, an overlapping region is arranged at the junction of the transient stage and the steady-state stage, the overlapping region is a 1 / 2 period following the transient stage, and a cosine weight function is adopted to perform weighted fusion on filtering results of the two stages in the overlapping region; and splicing the filtering results of the non-overlapping region and the overlapping region of the transient stage and the non-overlapping region of the steady stage, and outputting a filtered fault feature signal.

Methods and systems for generating output content using a generative artificial intelligence (AI) model based on an input. A similarity-assessment layer at the output of the generative AI model determines a similarity measure for the output content vis-à-vis pre-existing items in a repository. The similarity measure is compared to a threshold value and, responsive to the comparison indicating excessive similarity, one or both of the input and the generative AI model are adjusted, and the generative AI model is re-run to generate new output content.

The invention discloses a new energy automobile part defect detection method and system based on deep learning, and the method comprises the steps: carrying out the image collection of the surface of a new energy automobile part through a camera, and forming an initial image set; according to the initial image set, a filtering method is adopted to carry out de-noising processing, noise influences are effectively restrained for complex shapes of parts and interference of various materials, and de-noised images are determined; extracting edge features from the de-noised image, identifying contour changes by using an edge detection algorithm, identifying potential abnormal boundaries for defect diversity, and obtaining an edge feature map; if the abnormal boundary strength in the edge feature map exceeds a preset threshold value, deep feature learning is performed on an abnormal boundary region through a neural network model, multi-layer image extraction information is fused, and a defect type is judged; defect area and position coordinates are calculated by adopting a defect type result, multi-scale feature information is integrated according to accurate classification requirements, quantitative description of defects is obtained, and production quality and automation level are remarkably improved.

The invention discloses a driving simulator motion prompting algorithm based on deep reinforcement learning. According to the method, a motion prompt control problem is modeled into a Markov decision process including a platform physical state, vehicle reference motion sensation and platform motion sensation; a near-end strategy optimization algorithm is adopted, a deep neural network intelligent body is trained to learn an optimal control strategy through a multi-target reward function fusing vestibular perception errors, working space boundaries and action smoothness, and the optimal control strategy is deployed in a real-time control system. Experiments show that the method ensures millisecond-level real-time calculation, meanwhile, the average error reproduced by motion feelings in each axial direction is reduced by about 13.6% compared with a model prediction control method and is reduced by about 42.4% compared with a classic wash-out filtering method, and the method has good universality and expansibility.

The invention discloses a self-adaptive vehicle navigation filtering method and device, and belongs to the technical field of intelligent driving. The method comprises the following steps: constructing a nonlinear uncertainty tracking system model according to the mutability of an actual vehicle tracking state, the nonlinearity of a sensor measurement equation and the non-Gaussian property of a measurement error; aiming at a nonlinear uncertainty tracking system model, combining strong tracking filtering and a maximum entropy criterion, and constructing a cost function for estimating the optimal state of the unmanned vehicle; determining a fading factor in the cost function based on the orthogonality of the measurement residual sequence; constructing a novel adaptive navigation filtering algorithm by combining an unscented Kalman filtering framework according to the cost function and the fading factor; and carrying out data processing on the unmanned vehicle tracking system according to the constructed adaptive navigation filtering algorithm. According to the method, the problems of mutability, nonlinearity, non-Gaussian property and the like in a nonlinear tracking system can be inhibited at the same time, and the tracking precision and reliability of the unmanned vehicle in a complex environment are improved.

The invention provides a daily scene millimeter wave sensing non-human target filtering method and system, and the method comprises the steps: transmitting a millimeter wave to a target scene based on a millimeter wave radar, collecting an echo signal, mapping a point cloud sequence of the echo signal to a high-dimensional frequency domain space, and obtaining a first point cloud sequence; inputting the first point cloud sequence into a preset uniform motion filtering module, and mapping the output of the uniform motion filtering module to a time domain space to obtain a second point cloud sequence; the second point cloud sequence is input into a preset periodic motion filtering module, the periodic motion filtering module is provided with a multi-head attention layer, a linear layer and a multi-layer perceptron layer, and a point cloud feature sequence is output through the multi-layer perceptron layer; and inputting the point cloud feature sequence into a preset classifier to complete point cloud classification.

The invention discloses an intelligent noise filtering method and device based on scene recognition and a medium, and relates to the technical field of multimedia information processing, and the method comprises the steps: determining a noise filtering strategy under different scene types according to a scene and noise feature matching result, and applying the noise filtering strategy to image data and audio data, obtaining image data and audio data after noise filtering; performing image enhancement and image feature optimization processing on the image data after noise filtering, and performing audio feature selection and audio feature optimization processing on the audio data after noise filtering to obtain an optimized image feature sequence and an optimized audio feature sequence; and executing an analysis task on the image data and the audio data after noise filtering through the optimized image feature sequence and the audio feature sequence, feeding back the result of the analysis task to a noise filtering strategy, and adjusting the corresponding relationship between the scene type and the noise filtering strategy according to the result of the analysis task. And updating of a noise filtering strategy around an analysis task result is realized.

The invention discloses a burying point data collecting and filtering method and system based on multi-strategy configuration, and belongs to the field of data processing. The method comprises the following steps: S1, acquiring burying point data, and classifying and caching the burying point data into a memory; s2, a filtering algorithm is preset and used for collecting data from the memory; s3, loading and analyzing the configuration file, and generating an acquisition strategy; according to the acquisition strategy, calling a corresponding filtering algorithm to acquire data from the memory; and S4, storing and uploading the collected data. According to the method, flexible and efficient buried point data acquisition and filtering are realized by defining a plurality of acquisition algorithms and a plurality of configuration strategies, so that the requirements of different scenes are met.

The invention relates to a filtering method (200) for a leak test cycle of a certain type of object to be tested, for constructing a database capable of determining at least one reference value relating to a leak test of said object type, in particular suitable for an early leak detection method; the invention relates to a method (200) for measuring a test cycle comprising the measurement of a physical quantity related to the degree of leakage in at least one part of an object to be tested or in a peripheral cavity of the object to be tested, said method (200) comprising at least the following steps: determining (E2) a deviation between the measured values of the physical quantity at the starting time and the ending time of the test cycle, referred to as a total deviation; the total deviation is compared (E3) with a total deviation reference value.

The invention relates to an electricity consumption information data cleaning and filtering method and system, and the method comprises the steps: dividing collected electricity consumption data into an end point data set, a middle data set and a tail end data set according to properties, and carrying out the basic abnormal value removal and missing data filling; converting the time domain data of each data set into a frequency domain pattern capable of representing the fluctuation mode of the time domain data through discrete Fourier transform; performing correlation analysis from top to bottom based on a graph, screening an intermediate data set by utilizing the graph of the endpoint data set, and classifying and screening an end data set by utilizing the screened intermediate data set graph; performing block processing on the screened end data set, extracting core features of the end data set by separating an input effect, a load effect and associated components, and dividing the core features into weak, medium and strong categories according to the fluctuation intensity of the features; carrying out inverse transformation on each processed graphic expression, and outputting cleaned and filtered high-quality time domain power utilization data; the accuracy of data cleaning can be improved, and the data value can be deeply mined.

This invention discloses a dynamic quantile filtering method for product co-occurrence networks, comprising: firstly, constructing a product co-occurrence network based on the co-occurrence relationships of products in orders; then, employing a dynamic quantile filtering strategy to denoise the network, specifically: obtaining the co-occurrence frequency weights of all product pairs and forming a co-occurrence matrix; extracting all non-zero co-occurrence frequency weights for each node; defining a local adaptive threshold based on the quantiles of these weights; retaining edges greater than or equal to the threshold; resetting the weights of edges less than the threshold to zero; and retaining isolated nodes; symmetricizing the adjacency matrix using an intersection strategy to obtain a weighted adjacency matrix; updating the network to obtain the denoised product co-occurrence network; and finally, using the Louvain algorithm to perform community partitioning on the denoised network to obtain a product community network. This invention can adaptively filter out weak connections and noisy edges in the product co-occurrence network, effectively improving the accuracy and robustness of community detection.

The invention discloses a multi-level sensitive word filtering method and system, and relates to the technical field of computers, and the multi-level sensitive word filtering method and system can comprehensively cover antagonism avoidance and semantic fuzzy scenes through a multi-level mechanism of fusion rule matching, expansion word recognition and semantic depth analysis, so that the sensitivity of a user is improved. Therefore, missed judgment and misjudgment are effectively reduced, the system adaptability is improved, missed judgment and misjudgment in the sensitive word filtering process are effectively reduced, and the adaptability and processing accuracy of the system to dynamic changes are improved.

Methods, apparatus, and non-transitory computer-readable storage media for video decoding and encoding are provided. In a method for video decoding, a decoder may: determine a non-adjacent neighboring block of a current inter-coded block, where the non-adjacent neighboring block includes a plurality of reconstruction sample points that are non-adjacent to the current inter-coded block; obtaining a plurality of prediction sample points of the non-adjacent adjacent blocks based on the motion vectors of the non-adjacent adjacent blocks; obtaining a filter based on the plurality of prediction sample points and the plurality of reconstruction sample points; obtaining a current prediction block based on the motion vector of the current inter-frame coding block; and obtaining a filtered prediction block based on the filter and the current prediction block.

Methods and systems for generating output content using a generative artificial intelligence (AI) model based on an input. A similarity-assessment layer at the output of the generative AI model determines a similarity measure for the output content vis-à-vis pre-existing items in a repository. The similarity measure is compared to a threshold value and, responsive to the comparison indicating excessive similarity, one or both of the input and the generative AI model are adjusted, and the generative AI model is re-run to generate new output content.

The application discloses a filtering method for AV1, including: determining a to-be-filtered partition, wherein the to-be-filtered partition includes a plurality of blocks, and the plurality of blocks form a plurality of rows; and performing parallel filtering operations on at least two of the plurality of blocks by using several threads, wherein the several threads are responsible for filtering operations on several rows in a one-to-one correspondence, the several rows include at least two consecutive rows in the plurality of rows, and each block in the at least two blocks is respectively located in a different row. The application further discloses a filtering apparatus, a computer device, and a computer-readable storage medium.

This application relates to the field of three-phase distribution network fault detection and location technology, and discloses a method, device, system, and storage medium for fault location in distribution systems. It addresses the problem of low accuracy in fault identification and location in existing solutions due to poor traceability of PMU measurement data and high computational costs. The method includes: acquiring power grid data collected by acquisition units located on the distribution network of the distribution system; fusing the three-phase π model and state estimation equations characterizing the distribution network branches to obtain equations corresponding to the distribution branches, and solving them to obtain theoretical values; using a threshold filtering method to remove data with numerical fluctuations exceeding preset conditions from the power grid data, and compensating the removed power grid data based on the data acquisition accuracy to obtain target power grid data; using the LNR algorithm to perform fault detection based on the target power grid data and theoretical values; and employing a multi-stage candidate branch location strategy to locate the fault when it is detected.