48 results about "Orthogonal subspace" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention discloses a physiological data analysis method and system for evaluating a greenbelt visual fatigue intervention effect, and the method comprises the steps: carrying out the millisecond-level time alignment of three types of heterogeneous physiological signals, namely eye movement tracking data, an electroencephalogram signal and a sweat biomarker through a self-adaptive clock synchronization protocol, and generating a time-space coupled multi-modal physiological data set; carrying out nonlinear decomposition on the multi-modal physiological data set by adopting an improved variational auto-encoder, generating three orthogonal feature subspaces through implicit space projection, and carrying out tensor splicing on all orthogonal subspace features to form a decoupling feature vector; and constructing a multi-scale attention gating graph convolutional neural network model, and mapping the decoupling feature vector to a three-dimensional evaluation space containing a visual entropy value, a neural synchronization degree and a biochemical index. The deep coupling-decoupling architecture of the multi-modal physiological data is constructed, so that the three problems of signal misalignment, feature mixing and mechanism fuzziness are solved.

The invention discloses an underwater acoustic OFDM impulse noise estimation enhancement method based on distributed compressed sensing, and relates to the technical field of underwater acoustic communication. Carrying out frequency offset correction on the received OFDM signal by adopting a plurality of carrier frequency offset compensation values to obtain a plurality of groups of related signals; constructing an orthogonal projection matrix, projecting a received signal to a specific orthogonal subspace to eliminate channel components, and separating impulse noise; constructing a joint observation matrix by using the time domain sparsity of the impulse noise and the strong correlation under different carrier frequency offset compensation; and carrying out joint sparse reconstruction on the multiple groups of observation signals by adopting a distributed compressed sensing algorithm, and estimating an impulse noise time domain signal. A distributed compressed sensing algorithm is adopted, two characteristics of impulse noise are utilized, impulse noise estimation accuracy is improved, an impulse noise estimation effect is enhanced, and the problem that impulse noise estimation accuracy is limited under the condition of limited pilot frequency quantity is solved.

The invention discloses a self-supervised tomographic SAR (Synthetic Aperture Radar) reconstruction method based on orthogonal subspace decomposition. According to the method, a traditional single original space is reconstructed and expanded into a Range-Null-Raw triple space collaborative learning framework, an R2R and N2N principle-based self-supervised loss function is constructed by using an orthogonal subspace decomposition characteristic of a linear measurement operator, direct mapping learning from noise measurement data to a high-quality reconstruction result is realized, and the real-time performance of a real-time reconstruction system is greatly improved, so that the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance is improved. Observable components are learned through Range space, unobservable components are learned through Null space, a cross constraint strategy of global consistency is ensured through Raw space, and the adaptability of the model to real measurement conditions is enhanced. The performance gap between simulation training and actual deployment in deep learning TomoSAR reconstruction is effectively solved, and a new solution is provided for reliable application of an unsupervised deep reconstruction network in a real TomoSAR imaging task.

The invention discloses a distributed micro-service architecture service component efficient management and control method based on an intelligent learning model, and belongs to the field of intelligent operation and maintenance, and the method comprises the steps: S1, constructing a multi-granularity service component map, collecting service calling data, mapping the service calling data into a third-order tensor, eliminating non-service flow, and carrying out smooth processing; s2, performing parallel orthogonal subspace slice mapping on the tensor, and extracting an abnormal path subtensor set; s3, constructing a dynamic health assessment model based on the heterogeneous service dependency graph, and fusing indexes such as response time delay and overload frequency; s4, constructing a nested meta-learning model fusing support vector classification and graph convolution, and generating a strategy group; s5, expanding a service behavior response function in a disturbance environment, and improving the coverage rate of a strategy to a rare state; s6, based on an execution engine of a rescheduling factor, guiding the resources to be redirected to a high-aggregation-degree substructure; and S7, comparing the structure entropy change to judge whether to enter the next round of intervention or adjust the disturbance source. The beneficial effects are that abnormity is accurately detected, health is dynamically evaluated, and scheduling efficiency is improved.

The invention discloses a modal modeling method for a tendon-driven continuum robot. The method comprises the following steps: carrying out differential modeling on a configuration under a SE (3) Lie group-se (3) Lie algebra framework; the strain field is subjected to spectrum parameterization and is decomposed into active / passive orthogonal subspaces, and an inner product adopts energy weighted Hilbert measurement containing material and section parameters. The active mode is constructed by a linear continuous Jacobian of a tendon path; legendre polynomial expansion is adopted under the collinear layout, and complex Fourier harmonic expansion is adopted under the non-collinear layout; the passive mode is selected according to a spectrum slot position mutual exclusion principle so as to ensure that the passive mode is orthogonal to the active mode in energy and suppress spectrum leakage. Lie group integration and spectral integration are adopted for numerical value realization; newton-Rafson iteration is used in the quasi-static state, and generalized-alpha time integration or Newmark-beta integration is used in the dynamics. The method has high precision and robustness under nonlinear large deformation and multi-tendon coupling conditions, and is suitable for scenes such as software operation, minimally invasive intervention and precise detection.

The invention provides an attack detection method and system based on Bayesian incremental learning and a storage medium, and the method comprises the steps: 1, collecting a data set, and dividing the data set into a plurality of tasks according to years; 2, using a Bayesian continuous learning framework, taking posterior distribution obtained by learning of a previous task as prior distribution of a current task, and adopting a gradient projection method to project a gradient of the current task to an orthogonal subspace of an old task feature space to obtain projection parameters; step 3, performing label deviation and noise processing on the task; 4, minimizing new task loss to obtain parameters, and adopting a training strategy according to a label deviation and noise processing result; 5, finding an optimal combined solution; and step 6, taking the combined model parameters as initialization parameters of the next task, returning to the step 2, and entering the next round of iteration until training of all tasks is completed. The method has the beneficial effects that the knowledge retention capability can be remarkably improved, and the problem of disastrous forgetting is effectively solved.

This document describes techniques and systems of multiple-target, simultaneous beamforming for four-dimensional (4D) radar systems for efficient angle estimation in two dimensions with a high dynamic range. For example, a processor can use electromagnetic (EM) energy received by a two-dimensional (2D) array to determine first angles in a first dimension associated with one or more objects. The processor can then determine a subspace projection matrix using the first angles without an estimate of the power of noise or interference signals in the received EM energy. Using the subspace projection matrix, the processor can determine an interference-orthogonal subspace projection-based beamformer. With the interference-orthogonal subspace projection-based beamformer, the processor can determine the desired signal output from an adaptive beamformer for the EM energy and second angles corresponding to respective first angles for the objects.

The application provides a power system core data quantum-resistant encryption method and system based on lattice cryptography, relates to the technical field of power system information security, and comprises the following steps: decomposing core data into orthogonal subspace components, constructing a quantum-resistant key pair based on a lattice difficult problem, performing state migration through multiple rounds of lattice basis transformation and noise injection, and finally assembling a ciphertext-related hash chain. The application can resist quantum computing attacks, improve the security of power system core data, realize efficient encryption processing, and guarantee the structural integrity and traceability of data.

The invention provides a spectral data cube reconstruction method, device and equipment based on compressed physical prior, a storage medium and a program product, and relates to the technical field of spectral reconstruction, and the method comprises the steps: obtaining a two-dimensional measurement image formed after a to-be-observed target is coded and modulated by a spectral imaging chip; obtaining an original transmission spectrum matrix corresponding to each pixel in the spectral imaging chip; performing principal component analysis on the original transmission spectrum matrix in a spectrum dimension, and extracting a principal component feature vector through a singular value decomposition method; projecting the original transmission spectrum matrix into an orthogonal subspace formed by the principal component feature vectors to obtain a compressed physical prior vector; and inputting the two-dimensional measurement image and the compressed physical prior vector into a deep expansion neural network, and outputting a high-dimensional spectral data cube of the to-be-observed target. The reconstruction efficiency of the spectral data cube can be remarkably improved.

The application relates to the technical field of industrial equipment state monitoring, and particularly discloses a fault prediction and health management system based on an industrial robot, which collects robot benchmark operation data and real-time operation data, and constructs a benchmark data set containing individual identity labels; multi-domain feature extraction and weighted fusion are performed on the data, a high-dimensional feature space is obtained through phase space reconstruction; the high-dimensional features are decomposed into mutually orthogonal individual attribute subspaces and degradation state subspaces by using an orthogonal subspace learning algorithm, and pure benchmark degradation features are obtained; a health index is constructed based on the benchmark degradation features, and a segmented continuous degradation model is established; real-time features are projected into the degradation state subspace to obtain real-time degradation features, which are input into the degradation model to invert the remaining service life and output graded early warning information; the application effectively suppresses false abnormal alarms by stripping individual difference interference through orthogonal decomposition of the feature space, and improves the cross-device generalization capability and prediction accuracy.

The invention discloses a transmission optimization method and device for a multi-base-station communication and sensing integrated system and computer equipment, and the method comprises the steps: constructing a multi-base-station novel communication and sensing integrated system for an imperfect channel scene under a complex communication condition in a closed environment, and proposing an optimization algorithm and a passive sensing multiple signal classification algorithm. An imperfect channel model in near field communication and a communication and perception model in a system are constructed, and a cooperative perception performance index Cramer-Rao bound is given. And finally, in order to improve the overall perception performance of the system, constructing a resource allocation optimization problem taking the minimum Cramer-Rao bound as a target function on the premise of ensuring communication requirements. The optimization problem is a non-convex optimization problem which is difficult to solve, so that a non-convex objective function is converted into a convex objective function by using the Schel complement theorem, and communication demand constraints are converted into convex constraints by using the semi-definite relaxation theorem. For a target positioning problem in a sensing task, coordinate estimation is carried out based on a passive sensing MUSIC algorithm of an orthogonal subspace.

This application belongs to the interdisciplinary field of artificial intelligence, computer vision, and network information security. It discloses a deepfake detection method and system based on orthogonal subspace decomposition and hyperspherical metric. By applying singular value decomposition to the weight matrix of a pre-trained visual model, it explicitly constructs a frozen principal subspace that preserves general semantic knowledge and a trainable orthogonal residual subspace that captures specific forgery traces, achieving orthogonal isolation of the parameter space. Simultaneously, hyperspherical metric learning is introduced into the feature space, performing L2 normalization on the features and applying alignment and uniformity losses. Combined with spherical linear interpolation, latent space data augmentation is performed while preserving the Riemannian geometric structure. Through the synergistic constraints of the parameter and feature spaces, this application can reduce the interference of fine-tuning on pre-trained general visual knowledge and improve the feature discrimination stability and cross-forgery generalization ability in deepfake detection tasks.

The invention discloses a multi-harmonic injection-based dual three-phase permanent magnet synchronous motor torque increasing method, which comprises the following steps of: establishing a mathematical model of a dual three-phase permanent magnet synchronous motor in a six-dimensional natural coordinate system, and decoupling the six-dimensional natural coordinate system into three orthogonal subspaces; in the subspace, the fifth harmonic current and the seventh harmonic current are mapped into sixth harmonic current, and a phase current model containing fundamental waves, fifth harmonic waves and seventh harmonic waves is constructed; taking a phase current peak value as a constraint, carrying out collaborative optimization on fifth and seventh harmonics by adopting a genetic algorithm, and obtaining an optimal harmonic gain coefficient by taking maximization of a fundamental current as a target; generating a harmonic current reference sequence of the future step based on the rotor position, the angular velocity, the sampling period, the rated current and the optimal harmonic gain coefficient; and establishing a discrete prediction model of the dual three-phase permanent magnet synchronous motor, and carrying out multi-step tracking on the harmonic current reference sequence by adopting generalized prediction control. According to the invention, harmonic collaborative optimization can be realized, and the dynamic tracking performance is improved.

The invention discloses an underwater acoustic OFDM impulse noise elimination method based on an orthogonal subspace, and relates to underwater acoustic communication. Aiming at the problem that the performance of an OFDM underwater acoustic communication system is reduced due to the interference of impulse noise, an orthogonal projection matrix is constructed, and a received signal is projected to an orthogonal subspace corresponding to a channel component, so that the impulse noise and the channel are decomposed and coupled. The sparse characteristic of impulse noise is utilized, impulse noise estimation is carried out in the orthogonal subspace by adopting a compressed sensing algorithm, then time domain impulse noise is eliminated, and finally signals are restored through channel estimation, signal equalization and symbol detection. The problems of nonlinear distortion and error propagation in a traditional method are avoided, and the robustness and performance of an OFDM underwater acoustic communication system in an impulse noise interference environment are remarkably improved. Through comparison simulation experiments, under the condition of different signal-to-noise ratios and signal-to-pulse ratios, the method is superior to a traditional method in performance indexes such as pulse noise estimation mean square error, channel estimation mean square error and output signal-to-noise ratio.

The invention relates to the technical field of vision measurement, in particular to a bridge cable bent tower measuring device and method based on orthogonal subspace decomposition. The method comprises the following steps: S1, decomposing a rotation matrix and optimizing an objective function; s2, optimizing a solution algorithm, and verifying convergence; s3, the anti-noise capability is enhanced through projective invariance constraint and quadratic distance constraint; and S4, predicting a target shielding pose based on the hybrid predictor. According to the bridge cable bent tower measuring device and method based on orthogonal subspace decomposition, a rotation matrix is decomposed into three orthogonal projection subspaces, and the problem that a traditional iteration method is sensitive to an initial value is avoided; secondly, on the basis of Grassmann popularity, a convex relaxation optimization method is provided, and global convergence is guaranteed; and finally, introducing algebraic geometric constraint to enhance the anti-noise capability of the algorithm.

The invention relates to a large-scale power system discrete eigenvalue parallel computing method based on surrounding channel integration. Comprising the following steps: performing contour integral spectrum transformation on a system state matrix, and determining characteristic value dominance; differentiated integral curves are designed based on characteristic value dominance, the number of characteristic values in each integral curve is estimated, and the initial dimension of the corresponding characteristic subspace is determined; determining the number of initial integral points of each integral curve by adopting a self-adaptive method, and performing self-adaptive integral point configuration on each integral curve; determining an initial subspace dimension of each block based on the number of feature values and the number of parallel integral blocks; performing singular value decomposition on a block basis matrix to construct a standard orthogonal basis; combining the orthogonal basis of each block to form a global orthogonal subspace basis; performing Rayleigh-Ritz projection in a global space to solve a dimension-reduced generalized feature value; and outputting the discrete eigenvalue and the eigenvector. And accurate and efficient calculation of discrete characteristic values of a large-scale power system is realized.

The invention discloses a Hilbert space guided physical consistency dynamic scene three-dimensional reconstruction and rendering method and system, and the method comprises the steps: 1, receiving video data of a dynamic scene, constructing a three-dimensional Gaussian point set, and initializing the static attributes of Gaussian points; selecting part of Gaussian points as control points; 2, constructing an orthogonal subspace used for describing the motion trail of the Gaussian point in the Hilbert space; 3, each Gaussian point forms a Gaussian point track according to a time sequence; learning a group of subspace coupling coefficients for each Gaussian point trajectory, and representing each Gaussian point trajectory as a linear combination of primary functions of orthogonal subspaces; 4, calculating a residual vector of each Gaussian point track; 5, updating the central position of each Gaussian point; and 6, inputting the updated Gaussian points into a rendering pipeline, and generating a rendering image corresponding to the visual angle and the time. According to the method, on the premise of ensuring physical consistency, the dynamic scene rendering precision is remarkably improved.

The invention provides a psychological health assessment system based on emotional interaction, and relates to the field of psychological assessment. Comprising a quantum pre-judgment touch module, a spatio-temporal data disassembly module, an anti-fact entropy reconstruction module, a quantum situation engine module, a time anchor point welding module, a quantum time sequence identifier generation module and a self-destruction evolution module. The method comprises the following steps: measuring an electron spin state transition probability through a quantum tunneling sensor to generate a virtual touch scene and trigger a pre-reaction electric signal, disassembling user response data into space-time orthogonal subspace slices by using a non-exchangeable geometric converter, dynamically correcting an emotion potential energy equation and generating a virtual-real fusion scene; realizing data security evolution in combination with a quantum Chicnol effect and a Margarner-Fermi weaving technology; the system is integrated in a wearable head-mounted device, and high-sensitivity emotional interaction and anti-interference evaluation are realized through a superconducting quantum chip and a topological insulator array.

The application provides a spectral data cube reconstruction method and device based on compressed physical prior, equipment, storage medium and program product, relating to the technical field of spectral reconstruction, and the method comprises the following steps: obtaining a two-dimensional measurement image formed by encoding and modulation of a to-be-observed target through a spectral imaging chip; obtaining an original transmission spectrum matrix corresponding to each pixel in the spectral imaging chip; performing principal component analysis on the original transmission spectrum matrix in the spectral dimension, and extracting a principal component feature vector by a singular value decomposition method; projecting the original transmission spectrum matrix into an orthogonal subspace formed by the principal component feature vector to obtain a compressed physical prior vector; inputting the two-dimensional measurement image and the compressed physical prior vector into a deep unfolding neural network to output a high-dimensional spectral data cube of the to-be-observed target. The application can significantly improve the reconstruction efficiency of the spectral data cube.

The invention relates to the field of computer vision and camouflage target segmentation, in particular to a camouflage target segmentation method of a reversible expansion network based on SAM guidance, which comprises the following steps of: firstly, constructing a foreground space priori graph, a background space priori graph and a high-quality SAM pseudo mask by utilizing a segmentation cutting model SAM; the prior redundancy is eliminated through low-dimensional orthogonal subspace projection, and the separability of the foreground and the background is enhanced; pixel-level and gradient-level feature fitting items and SAM subspace priori constraint items are fused to construct an overall objective function, the objective function is expanded into a multi-stage alternating iteration process of a foreground optimization submodule SFOS and a background optimization submodule SBOS, and a foreground feature map and a background feature map are refined step by step; and finally, generating a camouflage target segmentation mask according to the iteratively optimized foreground feature map. Through large model prior guidance, two-stage feature modeling and multi-stage expansion optimization, the integrity and accuracy of camouflage target segmentation are significantly improved.

This application relates to the field of carpooling demand prediction technology, and particularly to a method, device, electronic device, and storage medium for predicting carpooling demand. The method includes: constructing a feature tensor set based on historical order data, meteorological parameters, and regional static feature data; transforming the target low-dimensional statistical features into target high-dimensional semantic features; and mapping the target high-dimensional semantic features to a target orthogonal subspace to generate a feature set that eliminates redundant temporal correlations. The feature set is then used to optimize the hyperparameters of a pre-constructed ensemble learning gradient boosting tree model until an iteration stopping condition is met, thus constructing a carpooling demand prediction model. This model outputs carpooling demand. This solves the problem that related technologies fail to couple the temporal and spatial dependencies of passenger travel demand, and that the prediction models are sensitive to hyperparameters, making it difficult to adapt to unconventional scenarios and output accurate carpooling demand.

The invention belongs to the technical field of cable fault detection, and particularly relates to a cable soft fault positioning method, device and equipment and a medium. The method comprises the following steps: acquiring and discretizing a real part signal of a cable transmission function, decomposing the signal by adopting an improved CEEMDAN algorithm, reconstructing the real part signal by an IMF component, and converting the real part signal into a complex signal vector; and constructing an orthogonal subspace by using covariance matrix decomposition, and calculating a MUSIC-pseudo spectrum based on a noise subspace to realize fault positioning. The improved CEEMDAN and MUSIC algorithms are fused, modal aliasing is reduced through adaptive noise auxiliary decomposition, multiple reflection interference and noise are effectively suppressed in combination with subspace decomposition, and the problem that a traditional method is insufficient in precision in high-frequency attenuation and long-distance detection is solved. Compared with a time domain reflection method and a frequency domain reflection method, the scheme does not need complex hardware support, and the soft fault positioning sensitivity is remarkably improved.

The invention discloses a data-free model fusion method based on orthogonal and projection double-space optimization, which belongs to the technical field of data model fusion, is used for data-free model fusion, and comprises the following steps of: performing singular value decomposition on a shared orthogonal subspace, removing redundant vectors to obtain a redundancy-free subspace, and performing data fusion on the redundancy-free subspace; key parameters are projected into an orthogonal subspace parameter module through orthogonal space projection, an orthogonal subspace optimizer carries out gradient information updating and then feeds back to the orthogonal subspace parameter module, and a projection subspace optimizer carries out gradient updating and feeds back to a double-space constraint device. And carrying out model fusion on the pre-training model, the output of the projection subspace optimizer and the reformed vector. The method is suitable for merging a plurality of expert models in a multi-task scene, does not need to depend on additional data or retraining, and remarkably reduces the calculation cost and privacy risk; and task sharing information is maximized and parameter conflicts are minimized simultaneously in the subspace, so that the overall performance and applicability of data-free model fusion are further improved.

The invention discloses an underwater vegetation classification method based on unsupervised learning and feature fusion. The method comprises the following steps: S1, collecting an original image; s2, inputting the original image into a deep convolutional neural network, removing redundant information, and generating a low-dimensional visual feature vector; s3, performing quantification processing on the original image through a multi-modal large model, performing ID processing on the unstructured graph according to the biological cue word, inputting the processed unstructured graph into an EAPCR-AE model, and extracting a low-dimensional high-density semantic feature vector; s4, performing L2 normalization on the low-dimensional visual feature vector and the low-dimensional high-density semantic feature vector respectively, and then performing splicing; constructing an orthogonal subspace by using a principal component analysis technology, extracting a principal component of which the cumulative variance contribution rate reaches a preset threshold value, and outputting a fusion feature vector; and S5, performing clustering and voting judgment on the fused feature vector to form a classification result. According to the underwater vegetation classification method, the feature fusion barrier of visual features and word meaning features in label classification is overcome, and the classification accuracy is high.

The invention discloses a dual three-phase permanent magnet synchronous motor model predictive current control method based on an optimized duty ratio error, and belongs to the technical field of motor control. The method comprises the following steps: firstly, constructing a six-phase mathematical model of a motor, and converting the six-phase mathematical model into an orthogonal subspace model through vector space decoupling and Parker transformation; then, establishing a cost function based on a current tracking error, and solving an analytical solution of an unconstrained modulation voltage of the cost function; on the basis, a quadratic programming problem with the minimum modulation voltage error as the target is constructed, and the optimal modulation voltage corresponding to the optimal duty ratio is solved under the linear boundary constraint. In order to efficiently solve, the optimization problem is processed by adopting a primal-dual interior point algorithm. And finally, generating modulation voltage according to the solved optimal duty ratio to drive the inverter to output. According to the invention, the dynamic response speed of the system is effectively improved, the steady-state current error and the harmonic component are obviously reduced, and the method is suitable for a high-performance motor driving system.

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.

This invention relates to the field of undisturbed sample transportation. To achieve hazardous transportation assessment of undisturbed samples, this application provides a method and system for assessing the transportation risks of undisturbed samples based on vibration reduction theory. The method involves acquiring the vibration of the transport container, the force and displacement of the vibration-damping support, and the constraint changes of the encapsulated soil sample cylinder to form a transportation response segment sequence. This sequence is then input into MTS-JEPA, where joint embedding mapping is performed on short-term impact and long-term cumulative scales to obtain a transportation potential state sequence. An orthogonal subspace state codeword is constructed using AMP in a soft codebook to form an orthogonal subspace codebook. The transportation potential state sequences are then categorized to form hazardous state groups and hazardous state change sequences. Finally, a hazard assessment result is generated under the structural instability threshold. This method achieves hazardous transportation assessment of undisturbed samples with high accuracy.

This invention discloses an AI-powered intelligent monitoring method for enterprise business stability based on multimodal data fusion, relating to the field of enterprise business stability monitoring. The method includes: collecting and preprocessing multimodal data to obtain an aligned multimodal dataset; extracting topological features from the call chain data in the aligned multimodal dataset and mapping them to a hypergraph adjacency matrix to reconstruct the service dependency hypergraph; converting the indicator data, log data, and call chain data in the aligned multimodal dataset into discrete features of symbolic semantic tokens, performing metric orthogonal subspace alignment under topological constraints to obtain an orthogonally aligned multimodal feature set; and obtaining the current business activity type and priority information, encoding it into a business semantic vector reflecting business stability requirements. This invention achieves enterprise business stability monitoring effects that adapt to business stability requirements and realize accurate anomaly detection and intelligent root cause localization across the entire link dimension.