11 results about "Orthogonal subspace" patented technology

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.

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.

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.

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.

The application discloses a multi-modal large language model passive forgetting method based on an agent anchor point, and aims at solving the problem that original private data cannot be accessed in a privacy compliance scene. First, a text-guided coarse-to-fine retrieval strategy is adopted, cross-modal feature alignment is utilized to accurately locate an agent anchor point which overlaps with a target semantic from a public data set, and a substitute supervision signal is constructed. Secondly, a double-constraint semantic isolation optimization is implemented. On one hand, a text-anchor semantic repulsion mechanism is introduced to cut off a visual-induced link of a target concept in a feature space, and accurate erasing is realized. On the other hand, a zero-space projection technology is utilized to strictly limit gradient updating in an orthogonal subspace which retains knowledge, and feature isotropic regularization is used to prevent manifold collapse. While completely forgetting sensitive concepts, the method effectively guarantees the general perception and reasoning ability of the model, and significantly reduces the risk of catastrophic forgetting.

This invention discloses a passive forgetting method for multimodal large language models based on surrogate anchors. Addressing the challenge of inaccessible original private data in privacy-compliant scenarios, this invention first employs a text-guided coarse-to-fine retrieval strategy. It utilizes cross-modal feature alignment to accurately locate surrogate anchors semantically overlapping with the target from public datasets, constructing alternative supervision signals. Secondly, it implements a dual-constraint semantic isolation optimization: on one hand, it introduces a text-anchor semantic exclusion mechanism to sever the visual triggering link of the target concept in the feature space, achieving precise erasure; on the other hand, it uses null space projection technology to strictly restrict gradient updates to an orthogonal subspace that preserves knowledge, and combines this with isotropic feature regularization to prevent manifold collapse. This method effectively safeguards the model's general perception and reasoning capabilities while completely forgetting sensitive concepts, significantly reducing the risk of catastrophic forgetting.

This invention relates to a two-stage federated distillation and large model fine-tuning method based on differential privacy, belonging to the fields of artificial intelligence security, federated learning, and differential privacy technology. The method includes: Step 1, federated pre-training based on complexity adaptive budget management and momentum orthogonal subspace directional noise addition; Step 2, local dataset distillation based on frequency domain-aware complex plane anisotropy noise addition; and Step 3, cloud-based large model fine-tuning and end-to-end privacy compliance assessment based on noise prior-driven adaptive rank. This invention, by introducing mathematically provable RDP differential privacy protection in both the federated pre-training and data distillation stages, achieves precise allocation and tracking of the privacy budget in both stages. It completes high-fidelity generation of the distilled dataset and secure fine-tuning of the large model while strictly protecting the privacy of the client's original data, which is of great significance for promoting the secure and efficient deployment of large models in distributed privacy-sensitive environments.

This invention discloses a method for severe weather image restoration based on decoupling adversarial mechanisms and continuous learning, belonging to the field of computer vision and image processing technology. The invention obtains the restored image by inputting the severe weather image into a two-stream decoupling network for reconstruction. The two-stream decoupling network sequentially includes: a shallow feature extraction module, a two-stream module, an orthogonal subspace purification module, a feature fusion module, and an image reconstruction module. The two-stream module consists of a general feature extraction branch and a task-specific feature extraction branch. Compared with existing technologies, this invention effectively overcomes the catastrophic forgetting problem in continuous learning, achieves deep orthogonal decoupling of background and degraded features, and improves the image restoration quality in scenarios where multiple types of severe weather data arrive incrementally via streaming.

The application discloses a multi-base station sensing integrated system transmission optimization method and device and computer equipment, the method comprises the following steps: for the imperfect channel scene of complex communication conditions in a closed environment, a new multi-base station sensing integrated system is constructed, and an optimization algorithm and a passive sensing multiple signal classification algorithm are proposed. The imperfect channel model in near field communication, the communication and sensing model in the system, and the cooperative sensing performance index Cramer-Rao bound are given. Finally, in order to improve the overall sensing performance of the system, the resource allocation optimization problem with the minimum Cramer-Rao bound as the objective function is constructed under the premise of guaranteeing the communication demand. The optimization problem is a non-convex optimization problem and is difficult to solve, therefore, the Shur complement theorem is used to convert the non-convex objective function into a convex objective function, and the semi-definite relaxation theorem is used to convert the communication demand constraint into a convex constraint. For the target positioning problem in the sensing task, the passive sensing MUSIC algorithm based on the orthogonal subspace is used for coordinate estimation.