54 results about "Space decomposition" patented technology

The invention relates to the technical field of large language models and privacy protection, and discloses a causal intervention-based vertical domain large model privacy knowledge decoupling method and a terminal. The method comprises the following steps: constructing a privacy, anti-fact and reservation data set; computing the causal effect of an intermediate node through activation value replacement, identifying a key node in combination with anti-factual reasoning, and constructing a map to determine a core causal propagation chain; training a discriminator by using a domain knowledge ontology graph, calculating and calibrating a lexical instantiation score, decomposing a representation space into orthogonal instance and concept subspaces, and determining spatial attribution and causal weight of parameters; and constructing a double-constraint optimization target which comprises retention and forgetting loss and regularization constraint, and updating parameters which are located in a real example subspace and belong to a propagation chain. According to the method, the causal propagation chain of privacy leakage in the vertical domain large language model can be accurately identified, selective decoupling of semantic boundary perception is realized, and the domain professional ability of the model is reserved to the maximum extent while specific privacy data is thoroughly eliminated.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial science and technology field, and discloses a video generation method, device and equipment based on hidden space decomposition and a storage medium, and the method comprises the steps: obtaining an initial video file, and carrying out the data preprocessing and sample construction of the initial video file to generate a training sample pair; performing joint training on the double-branch encoder to generate a target double-branch encoder; training a noise prediction network through the target content code and the target dynamic code output by the target double-branch encoder to obtain a target noise prediction network; training the confidence prediction network through the intermediate data to generate a target confidence prediction network, and constructing a video generation model based on the target double-branch encoder, the target noise prediction network and the target confidence prediction network; and performing video generation processing based on the to-be-edited image and the user editing instruction through the video generation model to generate a target video frame. According to the invention, the control precision and efficiency of video generation are improved.

The invention relates to the field of building three-dimensional reconstruction, in particular to a building three-dimensional reconstruction method based on multi-source data alignment, which comprises the following steps executed in sequence: S1, acquiring building three-dimensional feature data shot by a plurality of unmanned aerial vehicles, and performing multi-source collaborative alignment processing on the plurality of building three-dimensional feature data, the method comprises the following steps: S1, aligning building three-dimensional feature data to obtain aligned building three-dimensional feature data, S2, carrying out recursive space segmentation on the aligned building three-dimensional feature data by using an octree algorithm to obtain a plurality of segmented regions, and S3, defining each segmented region as a local center point by using tile type space decomposition and generating tile units; s4, feature points with the SDF value being 0 are extracted from the global symbol distance field through a moving cube algorithm, then a continuous geometric surface of the building is constructed through linear interpolation, the model is scaled to a real proportion through a distance reference provided by an RTK-GNSS system of the unmanned aerial vehicle, and a reconstructed model is output; the limitation that a traditional three-dimensional reconstruction model only has a visual rendering effect is broken through.

The application discloses a network fault positioning fast response system based on distributed log analysis, relates to the technical field of computer network and operation and maintenance, and comprises the following modules: a data acquisition module, which generates time series data frames based on a sliding window; a state vector construction module, which extracts indexes and constructs standardized state vectors; a covariance matrix updating module, which recursively updates a dynamic covariance matrix by using a forgetting factor; a feature space decomposition module, which constructs a residual projection matrix based on dynamic principal component dimension parameters; an anomaly detection module, which calculates residual anomaly energy and determines a fault candidate set based on contribution degrees; and a parameter self-adaptive module, which calculates residual space entropy values and feeds back to adjust principal component dimension parameters of the next frame; and the application realizes adaptive and accurate bounding of gray faults through entropy feedback closed loop and orthogonal projection.

The invention discloses an interpretable compressed sensing image reconstruction method and system based on value domain-null space decomposition, and belongs to the technical field of digital image processing and artificial intelligence. The invention provides a deep expansion network which is characterized in that in each iteration stage of the network, image features to be solved are explicitly decomposed into a value domain subspace and a null space subspace which are orthogonal to each other; wherein the value domain subspace is used for recovering and keeping global structure information consistent with a measured value and ensuring data fidelity; the null space subspace focuses on adaptively enhancing the edge and fine details of the image through a multi-scale attention enhancement pyramid fusion module MAPF and a cross-stage feature refinement fusion module FRF. According to the method, through a decoupling modeling strategy, the accuracy and visual quality of the reconstructed image are remarkably improved, a better balance is achieved between precision and efficiency, and the method can be widely applied to reconstruction of natural images and medical images.

The invention relates to the field of network control systems, in particular to a performance evaluation method for a bandwidth limitation network control system under hybrid attacks, which is realized based on a two-degree-of-freedom controller and comprises the following steps of: modeling denial of service attack and spoofing attack by utilizing a Bernoulli random process; obtaining a mathematical model of the bandwidth limitation network control system under the mixed attack; substituting the mathematical model into the tracking error signal to obtain a tracking error signal of the bandwidth limiting network control system under the hybrid attack; and obtaining a tracking error performance limit expression of the bandwidth limiting network control system under the hybrid attack according to the tracking error signal and the tracking error performance index of the bandwidth limiting network control system. According to the method, the optimal tracking performance of the system is obtained by using tools such as all-pass decomposition, partial fraction decomposition, dual Bezout equations, H2 space decomposition technologies and Youla parameterization of a controller, and the characteristics of the system and the influence of network attacks on the performance of the control system are quantitatively revealed.

The invention discloses a self-adaptive subspace projection suppression method based on optical pump magnetometer external noise, and belongs to the technical field of neural magnetic imaging (MEG) signal processing.The method comprises the steps that brain magnetic signals are collected through a wearable optical pump magnetometer array and preprocessed, and a signal and noise subspace decomposition model is established; integrating a model-driven noise modeling method and a data-driven noise modeling method, and constructing a noise subspace base vector; constructing a signal subspace basis vector in combination with a guide field matrix and event correlation analysis; identifying residual noise components based on event correlation analysis, and perfecting noise subspace modeling; signal noise separation is realized by adopting a hierarchical projection strategy; and analyzing the time correlation of internal and external space signals, and constructing a time projection matrix to remove residual interference to obtain a clean MEG signal. According to the invention, various types of external noise interferences can be effectively suppressed, the signal-to-noise ratio and the reliability of brain magnetic signals are remarkably improved, and the method is particularly suitable for practical application of a wearable optical pump magnetometer system.

This invention relates to the field of 3D building reconstruction, specifically to a method for 3D building reconstruction based on multi-source data alignment, comprising the following steps executed sequentially: S1: Acquire 3D building feature data captured by multiple drones, and perform multi-source collaborative alignment processing on the multiple 3D building feature data to obtain aligned 3D building feature data; S2: Use an octree algorithm to recursively spatially segment the aligned 3D building feature data to obtain multiple segmentation regions; S3: Use tile-based spatial decomposition to define each segmentation region as a local center point and generate tile units; S4: Use a moving cube algorithm to extract feature points with an SDF value of 0 from the global symbolic distance field, and then construct the continuous geometric surface of the building through linear interpolation. Scale the model to the true scale using the distance reference provided by the drone's RTK-GNSS system, and output the reconstructed model; This method overcomes the limitation of traditional 3D reconstruction models that only have visual rendering effects.

The invention discloses a product quantization method based on hierarchical fuzzy clustering, and the method comprises the steps: carrying out the spatial decomposition of a to-be-compressed high-dimensional vector of image or video data into a plurality of low-dimensional subspaces through employing a hierarchical decomposition strategy; in each low-dimensional subspace, a hierarchical fuzzy clustering method is adopted, so that each data point belongs to a plurality of fuzzy clustering centers at different membership degrees; and on the basis of a hierarchical fuzzy clustering result, a product quantization coding mechanism is adopted, so that the fuzzy clustering center of each subspace forms a corresponding codebook, data points are coded and compressed according to membership degrees of the data points in different clustering centers, and data compression of a high-dimensional vector of the image or video data is realized. The invention provides a compression method combining a hierarchical structure and a fuzzy clustering technology, and the efficiency, the accuracy and the robustness of vector compression are remarkably improved.

The invention provides a power grid investment dynamic game optimization method and system based on state space decomposition and agent reinforcement learning. According to the method, the power grid investment decision problem is modeled into the intelligent agent dynamic game problem, and the reinforcement learning technology is combined, so that the power grid investment scheme can be effectively optimized, and the economical efficiency and stability of the system are improved. According to the method, a state space decomposition strategy is adopted, the power grid investment problem is divided into a plurality of sub-problems, the dimensionality is reduced, and the calculation efficiency is improved. Meanwhile, intelligent agent reinforcement learning is introduced, so that intelligent agents (such as a power grid company, a power generation enterprise, a supervision mechanism and the like) optimize investment decisions through interactive learning, and finally a global optimal investment scheme is realized.

The application discloses a structural vibration identification method and device of computer vision, which comprises space decomposition, resampling and reconstruction, residual sparsity processing of reweighting, and time correlation enhancement, realizes processing and identification from video to vibration information, effectively resists noise, reduces phase step problems, and provides high-precision structural vibration analysis results.

The invention discloses a method and a device for determining a fault critical state vector based on state space decomposition of a power system, a storage medium and computer equipment. The method comprises the following steps: determining an element set of a target power system; clustering the power generation elements, generating a target power generation subsystem according to at least one clustering cluster, calculating a first fault reduction set of the target power generation subsystem, calculating a first fault critical state vector set according to the first fault reduction set, and generating a second fault critical state vector set according to the first fault critical state vector set; clustering the power transmission elements, generating a target power transmission subsystem according to at least one clustering cluster, calculating a second fault reduction set of the target power transmission subsystem, calculating a third fault critical state vector set according to the second fault reduction set, and generating a fourth fault critical state vector set according to the third fault critical state vector set; and combining the second fault critical state vector set and the fourth fault critical state vector set to obtain a plurality of fault critical state vectors of the target power system.

The invention discloses a spatial decomposition enhanced database tuning method based on large model guided exploration, comprising the following steps: S1, constructing a multi-source knowledge system and completing verification, and providing domain knowledge support for subsequent knob screening; s2, screening performance key knobs through a hybrid expert mechanism to realize static compression of a configuration space; s3, initializing an adjustment and optimization sample set, and defining a relative performance optimization target taking throughput and delay as cores; s4, performing dynamic adjustment and optimization based on a spatial decomposition algorithm, and realizing efficient configuration exploration in combination with a UCB criterion and Bayesian optimization; and S5, repeating the dynamic tuning step in the step S4 to a preset number of iterations, and outputting the configuration with the optimal relative performance in the sample set as the final parameter configuration of the database. According to the method, the knobs are accurately screened through multi-source knowledge and the MoE mechanism, the configuration space is efficiently explored in combination with space decomposition and BO, the tuning efficiency and the database performance can be improved, and the method can quickly adapt to dynamic working load drift.

Disclosed are a pose decoupling kinematics calibration method, device and apparatus, and a storage medium. The method includes the following steps: constructing a pose decoupling motion model based on an equivalent rotation vector and a rigid body motion transformation rule; introducing a pose error, and carrying out pose space decomposition through the pose decoupling motion model to obtain an orientation error expression and a position equivalent expression; analyzing a coupling influence of an orientation change on a position according to the orientation error expression and the position equivalent expression to obtain a pose coupling increment; constructing a target decoupling calibration model according to the pose coupling increment and an initial decoupling calibration model; and solving geometric errors in different degrees of freedom for the target decoupling calibration model according to a preset axis pose error by a least square method to obtain a calibration error.

The invention discloses a data processing method, an illumination calculation method, a storage medium and a program product, which can reduce the use amount of a memory / video memory and the size of a package while keeping an illumination effect. The data processing method is used for processing an illumination space of a virtual scene, and comprises the following steps: decomposing the illumination space into a coefficient grid and a plurality of base grids, and storing the coefficient grid and the plurality of base grids. The coefficient grid includes a coefficient feature dimension and a first spatial dimension, the first spatial dimension representing a spatial resolution of coefficient grid vertices of the coefficient grid, the coefficient feature dimension including channels storing coefficient features relative to each coefficient grid vertex. Each of the plurality of base grids includes a base feature dimension and a second spatial dimension, the second spatial dimension representing a spatial resolution of a base grid vertex of the base grid, the base feature dimension including a channel storing a base feature of a preset sampling frequency band with respect to each base grid vertex, the sampling frequency bands of each of the plurality of base grids being different from each other.

The invention discloses a network fault positioning quick response system based on distributed log analysis, and relates to the technical field of computer networks and operation and maintenance, and the system comprises a data acquisition module which generates a time sequence data frame based on a sliding window; the state vector construction module is used for extracting indexes and constructing standardized state vectors; the covariance matrix updating module is used for recursively updating the dynamic covariance matrix by using the forgetting factor; the feature space decomposition module is used for constructing a residual projection matrix based on the dynamic principal component dimension parameters; the anomaly detection module is used for calculating residual anomaly energy and determining a fault candidate set based on the contribution degree; the parameter self-adaptive module is used for calculating a residual spatial entropy value and feeding back and adjusting a principal component dimension parameter of a next frame; through entropy feedback closed loop and orthogonal projection, adaptive accurate delimitation of the gray scale fault is realized.

A pose-decoupled kinematic calibration method, apparatus and device, and a storage medium. The method comprises: constructing a pose-decoupled motion model on the basis of an equivalent rotation vector and a rigid body motion transformation rule (101); introducing a pose error, and performing pose space decomposition by means of the pose-decoupled motion model to obtain an orientation error expression and a position equivalent expression (102); analyzing the coupling effect of an orientation change on a position on the basis of the orientation error expression and the position equivalent expression so as to obtain a pose coupling increment (103); constructing a target decoupled calibration model on the basis of the pose coupling increment and an initial decoupled calibration model, wherein the initial decoupled calibration model is constructed on the basis of the pose-decoupled motion model (104); and using the least squares method to perform geometric error calculation of different degrees of freedom on the target decoupled calibration model on the basis of preset axial pose errors, so as to obtain a calibration error (105). The method can solve the technical problem in the prior art that the influence of an orientation on a position in a multi-degree-of-freedom motion system is not considered, making it is difficult to eliminate an error difference between different degrees of freedom, and thus resulting in poor calibration and compensation effects.

The invention relates to the field of networked system output tracking control performance, in particular to a networked system output tracking control performance method for model uncertainty. Data packet loss in a communication channel is modeled in a feedback channel of a network control system by using a Bernoulli process and a binary random process, and it is assumed that reference input and channel noise signals of the system are independent identically distributed random processes with non-zero mean values and are mutually independent, and through all-pass decomposition and partial fractional decomposition, the data packet loss in the communication channel is calculated. A system model is deduced by using tools and methods such as a double Bezout equation, a space decomposition technology, a Youla parameterization method of a controller, statistical characteristics of random signals and the like, and finally, an explicit expression of the optimal tracking performance of the system is obtained. According to the method, the problem of OTC performance deterioration possibly caused by data packet loss and additive white Gaussian noise (AWGN) is solved.

The invention relates to the technical field of five-axis linkage machining, in particular to a working method of a five-axis linkage machining center, which mainly comprises the following steps of: A, reading a machining path; b, the machining path is subjected to space decomposition, and paths of the X axis, the Y axis, the Z axis, the A axis and the B axis are formed; and C, according to the path, the X-axis module and the Z-axis module are used for driving the machining mechanism to move along the X-axis path and the Z-axis path correspondingly, the Y-axis module is used for driving the carrier to move along the Y-axis path, the A-axis module is used for controlling the carrier to move along the A-axis path, and the B-axis module is used for controlling the machining mechanism to move along the B-axis path. The machining mechanism is driven by the X-axis module and the Z-axis module, the carrier is driven by the Y-axis module, the situation that in the driving process, due to the fact that the machining mechanism is driven by three axes, the load requirement is high is avoided, and the effect of reducing the cost is achieved; and the X-axis module drives the machining mechanism, and the Y-axis module drives the carrier, so that the driving precision cannot be greatly lost, and most precision requirements can be met.

The invention relates to the technical field of medical image processing, in particular to an ultrasonic image processing system based on image enhancement, which comprises a field construction step: constructing a pixel-level structure tensor matrix, and mapping original ultrasonic image data into tensor field data; a flow direction locking step: performing feature space decomposition on the tensor field data to generate a diffusion guide graph containing a main feature vector; a nonlinear evolution step: constructing an anisotropic diffusion tensor based on characteristic value information, and executing time step iterative solution based on a partial differential equation; a reunion output step: performing boundary continuity reunion on the enhanced image data; according to the method, the direction of the tissue is accurately locked through the Riemannian metric tensor field, smooth texture smoothing and inverse texture sharpening are achieved, speckle noise is greatly restrained, and meanwhile the microstructure features are reserved.

The invention discloses a financial complex query statement generation method based on a search algorithm and action space decomposition, and belongs to the technical field of computers. The method comprises the following steps: acquiring user query data; screening a candidate atomic function set from the atomic function library according to user query data; constructing a function combination according to the candidate atomic function set; and verifying the function combination to obtain an optimal function combination as a query statement. According to the method, through action space decomposition, the difficulty of generating complex query statements is reduced, and common cascade errors of a single-step generation model are effectively avoided by utilizing the global planning capability of Monte Carlo tree search. Compared with the background technology, experimental data shows that the method improves the accuracy and robustness of generating the complex query statement to a certain extent. And meanwhile, simulation execution and consistency verification are assisted, so that the generated function combination is ensured to be correct in grammar and parameter.

The application discloses a 3D Gaussian splash optimization method based on iterative spatial decomposition, first data acquisition and preprocessing, then iterative spatial decomposition and hierarchical geometric prior sampling; hierarchical geometric perception initialization; 3D Gaussian model training and hierarchical geometric perception densification; finally, iterative optimization and output. The application solves the problem that the random downsampling or curvature-based downsampling method in the prior art is difficult to balance between efficiency and spatial structure reservation.

The application provides a medical image cross-modality generation method based on double-contrast frequency domain decomposition and adaptive diffusion, which comprises the following steps: acquiring MRI samples and preprocessing, constructing a k-space decomposition and enhancement process, constructing a k-space double-flow adaptive degradation network, and constructing a k-space double-flow adaptive prediction network. Through data enhancement technology and an adaptive Gaussian filter set, different frequency components are separated and enhanced, common edge features are highlighted, and the quality of generated information is improved. By using a residual noise diffusion model, high-resolution images with rich details and high diversity are generated while maintaining smooth areas and large-scale structures of images. By introducing anatomical structure consistency constraints and multi-modality fusion optimization strategies, the complementary information of T1 and T2 weighted MRI is fully utilized, and the generation effect and visual authenticity of the medical image cross-modality generation in a complex scene are significantly improved.

The present application relates to the technical field of computer vision, and provides a mirror reflection scene rendering method based on multi-space decomposition, which can perform high-quality new view rendering on a scene containing mirror reflection interference, implicitly decomposes a three-dimensional real scene into multiple parallel subspaces through a neural network, obtains a feature map through rendering, finally maps each sub-space feature map into an RGB image and a weight image through a decoding network and a gating network, and finally obtains an image of the scene at a specified view angle through weighted summation, and uses the corresponding view angle RGB image for supervision, which not only can render a higher quality image, but also avoids the multi-view inconsistency interference model fitting process caused by mirror reflection.

The invention discloses an unmanned aerial vehicle detection path planning method for low-altitude security and protection, and relates to an unmanned aerial vehicle. The method comprises the steps that geographic space data and flight mission parameters of a target area are acquired; constructing a three-dimensional grid map model according to the geographic space data; based on the three-dimensional grid map model, establishing a multi-constraint flight model comprising a height safety constraint, an airspace limitation constraint and a sensor coverage constraint; carrying out two-stage decomposition on the flight space by adopting a hierarchical set division method to obtain a hierarchical space decomposition graph; constructing an optimization problem of unmanned aerial vehicle detection path planning based on the spatial decomposition graph; solving the optimization problem by adopting a hybrid heuristic algorithm to obtain an unmanned aerial vehicle detection path covering the target area; according to the method, a large-scale complex three-dimensional urban space path planning problem is converted into a hierarchical small-scale sub-problem through hierarchical spatial decomposition, efficient solving is performed in combination with a hybrid heuristic algorithm, and the solving efficiency is improved.

One or more computer processors responsive to receiving an input, a trained model, a trained model outcome, a local outlier factor (LOF) threshold, and a maximum number of live polytopes to search over, generating an optimization problem to determine an output that is closest to the input with respect to a distance measure. The one or more computer processors transform a LOF constraint into a set of linear mixed integer constraints and a set of quadratic mixed integer constraints, utilizing the distance measure. The one or more computer processors decompose an input space into a plurality of polytopes based on a geometry associated with the trained model. The one or more computer processors generate a counterfactual based on the plurality of polytopes.

The application discloses an array inter-coupling channel estimation method based on an OFDM waveform, and comprises the following steps: S1, a signal transmission model based on an OFDM waveform is given; S2, assuming that a self-interference channel is static, multiple times of changing a transmitting and receiving beamforming vector are given, and multiple groups of r(f k ) are obtained, so that a calculation formula of an H(f k ) estimation value is obtained; S3, zero space decomposition is carried out on a transmitting beam pointing direction vector q t and a receiving beam pointing direction vector q r , and a W and V selection strategy is given; S4, the transmitting beamforming vector is sequentially configured as a column vector in W, and the receiving beamforming vector is sequentially configured as a column vector in V, so that multiple groups of r(f k ) are obtained; S5, the step S4 is performed K*J times, so that an R(f k ) matrix is obtained, and an estimation value of H(f k ) is obtained, that is, a self-interference channel matrix on each subcarrier is obtained. The application can be performed when a transmitting and receiving array surface normally works, transmission efficiency of array service is improved, and changes of a channel state can be timely responded.

The invention discloses a machine forgetting method based on gradient decomposition, which comprises the following steps of: determining a forgetting sample set and a reserved sample set from a training sample set of a trained model, and respectively inputting training samples in the forgetting sample set and the reserved sample set into the trained model; respectively calculating to obtain a forgetting gradient and a retention gradient corresponding to the forgetting loss and the retention loss, carrying out orthogonal decomposition of the forgetting gradient on the retention gradient, extracting components of the forgetting gradient in an orthogonal subspace, and then carrying out gradient symbol space decomposition to obtain a positive space and a negative space of dimensions in the gradient; and fusing the forgetting gradient and the reserved gradient in different modes according to the space to which the gradient dimension belongs to obtain a final gradient, updating parameters of the trained model according to the final gradient, and repeating the process until an end condition is reached. According to the method, forgetting information and reserved knowledge are explicitly decoupled through gradient decomposition, and the interpretability and robustness of machine forgetting are improved.

The present invention discloses a text semantic representation method and device based on information compression and spatial decomposition. The method comprises: performing intra-sentence text semantic mining on the text to be represented to obtain a node vector representation of the text to be represented; performing cross-sentence text semantic mining on the node vector representation of the text to be represented to obtain a sentence node vector representation of the text to be represented; and determining a text semantic representation of the text to be represented based on the sentence node vector representation.

The invention discloses a distributed radar anti-suppression interference method based on spatial decomposition. The method comprises the following steps: acquiring a receiving signal of an ith receiving station; performing time domain alignment on the received signal to obtain an aligned received signal; obtaining an interference subspace and a signal subspace according to the aligned covariance matrix of the received signals; performing subspace projection on the aligned receiving signals by using the signal subspace to obtain projected receiving signals, and filtering out suppressing interference components falling into the interference subspace based on an orthogonal relationship between the signal subspace and the interference subspace to obtain interference suppressed receiving signals; performing target detection on the received signal after interference suppression to obtain a distance vector taking the ith receiving station as a time domain alignment reference; and determining the real position of the target based on an N * N distance matrix obtained according to the N distance vectors. The method does not need training and weight coefficient estimation, is high in universality and calculation efficiency, and can effectively improve the target detection performance of a multi-radar system.