1733 results about "Vector generation" patented technology

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

The invention discloses a rule base dynamic construction method and device based on a large language model and a medium, and relates to the field of rule base construction.The method comprises the steps that on the basis of a preset layered template structure, meta-knowledge injection is conducted through a field knowledge graph corresponding to standard data, and dynamic cue words are generated; outputting a corresponding semantic triple through the large language model, and performing semantic enhancement on the semantic triple; performing symbolization processing and vectorization processing to obtain a rule vector, and generating a corresponding specified rule; and locally and dynamically updating the rule subset in the rule base through data updating of an external specified knowledge base, and dynamically updating the rule weight of the specified rule determined through the confidence coefficient. Through deep collaboration of the large language model and the symbol system, a logic verification layer is introduced through local dynamic updating to carry out formalized constraint on a large language model generation result, and the rule logic completeness is ensured while the generation capability is reserved.

The invention relates to a network attack tracing method, system and device based on a user portrait, and a medium. The attack tracing method comprises the following steps: collecting interactive behavior data of a user in a Web page; performing compression and serialization processing on the interaction behavior data to generate a structured interaction log file; collecting fingerprint data of the user terminal and performing standardization processing to generate a fingerprint feature vector; based on the structured interaction log file and the fingerprint feature vector, generating a user unique identifier containing a risk level tag; performing clustering analysis on the interaction behavior data according to the unique identifier of the user, identifying an abnormal behavior mode of the abnormal user and marking an attack type; acquiring a public network IP address of an abnormal user, and positioning a geographic position in combination with an IP database; and constructing an attack traceability report based on the user unique identifier of the abnormal user, the abnormal behavior mode, the public network IP address and the geographic position. According to the invention, the user portrait can be accurately constructed, and the network attack behavior can be effectively identified and traced.

The invention discloses an intelligent network transaction credit risk assessment system and method based on multi-modal data fusion and deep learning, and particularly relates to the technical field of network transaction risk assessment, and the system comprises a multi-modal data collection, fusion and deep reinforcement learning agent, and a large model interaction and risk assessment and supervision execution module. The multi-modal data acquisition module acquires various data in real time; the fusion module realizes semantic alignment and interactive learning through multiple strategies; the deep reinforcement learning agent module optimizes a monitoring and supervision strategy based on an Actor-Critic framework; the large model interaction module provides multi-aspect support by means of various technologies; and the risk assessment and supervision execution module identifies illegal behaviors and executes supervision actions. The evaluation method comprises the steps of data acquisition and preprocessing, feature vector generation, agent training, large model deployment optimization, real-time monitoring decision and the like. The system can improve the monitoring and supervision efficiency, protects the rights and interests of consumers, constructs a healthy transaction environment, and promotes the development of related technologies.

The invention discloses a partial discharge signal identification method and system based on deep learning and a storage medium, and relates to the field of computer systems based on a specific calculation model, and the method comprises the steps: dividing multi-source monitoring data into a high-frequency signal group and a low-frequency signal group; respectively inputting corresponding feature information to the first deep neural network and the second deep neural network to obtain a high-frequency feature vector and a low-frequency feature vector; generating a signal attenuation compensation factor based on the low-frequency feature vector, and performing distortion compensation on the high-frequency feature vector to obtain a high-frequency correction vector; calculating the feature correlation degree of the high-frequency correction vector and the low-frequency feature vector, and when the feature correlation degree is lower than a preset correlation threshold value, extracting transient feature information of the high-frequency correction vector, and fusing the low-frequency feature vector to obtain a low-frequency correction vector; and inputting the high-frequency correction vector and the low-frequency correction vector to a partial discharge identification model to obtain a partial discharge type and identification confidence. According to the invention, the identification accuracy of the partial discharge signal can be improved.

The invention discloses an intelligent legal instrument retrieval system based on a vector technology, which relates to the technical field of information retrieval and comprises an instrument vector generation module, an index construction module, a query vector generation module, a similarity calculation module and a result optimization module, comprising but not limited to word segmentation and stop word removal operations, and converting the legal document into vector representation by using a pre-trained word vector model; through the intelligent legal instrument retrieval system based on the vector technology, semantic meanings of the legal instruments can be effectively understood, and even in the face of fuzzy or inaccurate query input of a user, related legal instruments can be quickly and accurately positioned by means of similarity calculation in a vector space; the efficiency, correlation and accuracy of legal document retrieval are greatly improved, and the urgent demands of the legal industry and related demanders for efficient legal information retrieval are met.

The invention belongs to the field of machine tool health state monitoring, and discloses a machine tool feed shaft health monitoring method based on an optimization condition auto-encoder. The sensor collects vibration signals and power signals of the machine tool feed shaft in a healthy state and a partial fault state; preprocessing and denoising the vibration signal and the power signal through wavelet packet decomposition; after denoising, calculating time domain and frequency domain eigenvalues to form a machine tool eigenvector, generating a time-frequency matrix, converting the time-frequency matrix into an RGB image, and constructing a machine tool multi-source data set; a machine tool multi-source data set is divided, a training set, a verification set and a test set are obtained, and the training set only contains health data; the machine tool feature vector and the training set are input into a machine tool feed shaft health state monitoring model based on an optimization condition variational auto-encoder for training, and the trained model is used for machine tool feed shaft health state monitoring. According to the method, the health state of the feed shaft is monitored only by using the health data, so that the influence of imbalance of the fault data and the health data on the data-driven monitoring method is reduced.

A method and related system for generating document embeddings within an embedding space based on a set of structured documents by determining (i) a first vector based on a first segment of a first document and (ii) a second vector based on a second segment of the first document and updating association vectors indicating the second segment based on a distance between the first and second vectors. The method also includes generating a document embedding based on the association vectors, generating a candidate vector based on a candidate document, and determining a result indicating that a second distance between the candidate vector and a first document embedding satisfies a document embedding distance threshold. The method may also include generating a new document by providing, to a text generation model, a portion of the candidate document and a portion of the second segment of the first document.

The invention provides a procurement knowledge retrieval method and system based on a large language model in the technical field of procurement data management. The method comprises the steps that S1, a large number of procurement documents are obtained and segmented to obtain a plurality of pieces of procurement knowledge; s2, classifying the purchase knowledge through a large language model to obtain classification tags and purchase knowledge vectors, and generating routing corpora of the classification tags; s3, creating a knowledge base comprising a plurality of knowledge sub-libraries based on the classification labels, creating a routing library, storing the purchase knowledge and the purchase knowledge vectors to the knowledge sub-libraries, and storing routing corpora to the routing library; step S4, matching a routing corpus from a routing library based on the retrieval statement; step S5, positioning knowledge sub-libraries from the knowledge base based on the classification labels corresponding to the routing corpora; and step S6, matching the purchase knowledge from the knowledge sub-library based on the retrieval statement to generate a retrieval result. The method has the advantages that the accuracy, efficiency and diversity of purchasing knowledge retrieval are greatly improved.

The embodiment of the invention provides a structured query statement generation method and device, electronic equipment and a storage medium, belongs to the technical field of statement generation, and is suitable for the field of financial science and technology. The method comprises the steps of obtaining a target query text; performing text coding on the target query text to obtain a query text vector; screening out a matching table structure definition vector matched with the target query text from the candidate table structure definition vectors based on the query text vector; screening out a matched scene document vector from the candidate scene document vectors based on the query text vector; screening out matched statement question-answer vectors from the candidate statement question-answer vectors based on the query text vector; generating target prompt data based on the matching table structure definition vector, the matching scene document vector and the matching statement question and answer vector; and performing structured query statement generation on the target prompt data based on the statement generation model to obtain a target query statement. According to the embodiment of the invention, the accuracy of the structured query statements generated in different scenes can be improved.

Systems and methods for generating tile-able patterns from text include obtaining a text prompt and generating, by a generation prior model, a latent vector based on the text prompt, where the generation prior model is trained to output vectors within a distribution of tile-able patterns. An image generation model then generates an output image based on the latent vector. The output image comprises a tile-able pattern including an element from the text prompt.

The present application provides a data clustering method and apparatus, a system and a computer-readable storage medium. The method comprises: acquiring a node feature vector of each node and a node relationship network, the node feature vectors indicating attribute features of entity objects, and the node relationship network indicating a real interaction relationship between the entity objects; and in a hyperbolic space, generating node representations according to the node relationship network and the node feature vectors, and outputting a clustering result on the basis of the node representations. Using the method can fully capture contained low-order and high-order topological structure information in the hyperbolic space, thus achieving a better clustering effect.

An autonomous vehicle comprises a sensor unit 102 configured to generate an environmental signal corresponding to a surrounding environment. A vector generator unit 108 generates a numeric vector from

Rebalancing in a fleet of storage systems using data science including generating, by the cloud-based rebalancing system, a plurality of workload migration scenarios to address a detected workload imbalance among a plurality of workloads in a fleet of storage systems; determining, by the cloud-based rebalancing system, a plurality of movement vectors for each workload migration scenario, wherein each of the plurality of movement vectors describes a consideration factor for migrating a workload of the plurality of workloads within the fleet of storage systems; and generating, by the cloud-based rebalancing system, at least one rebalancing proposal based on the plurality of movement vectors for each workload migration scenario.

The invention belongs to the technical field of computer vision and deep learning, and particularly relates to a PCB defect detection method based on combination of a visual converter and conditional diffusion. Comprising the following steps: constructing an unlabeled PCB image data set and carrying out data preprocessing and enhancement to obtain a preprocessed image; executing a self-supervised pre-training task on the preprocessed image to obtain a feature extraction network; based on a conditional diffusion model, generating a synthetic defect PCB image and a label thereof by using the features output by the feature extraction network and the defect type control vector; mixing the synthetic defect image with a small number of real defect images to construct a training set; performing training adjustment on the defect detection model by adopting the training set to obtain a trained defect detection model; performing PCB defect detection by using the trained defect detection model; according to the method, the robustness and the cross-domain generalization ability are remarkably improved, the missed detection risk is reduced, and the rapid and stable quality control requirement of the production line is met.

The invention relates to the technical field of government and enterprise services, and discloses an intelligent adaptation system and method for government and enterprise services, and the method comprises the following steps: carrying out the structural analysis of an original policy text through a deep learning model, including word segmentation, word vector generation, dominant condition extraction and implicit condition reasoning, and finally constructing a policy knowledge graph; the multi-source heterogeneous data is integrated, dynamic enterprise feature vectors are generated through spatial-temporal feature construction, industrial association diagram modeling and spatial-temporal diagram convolution calculation, the enterprise state change gradient is detected, and the abnormal fluctuation risk is recognized; and mapping the policy condition vector and the enterprise feature to a hyperbolic space, calculating a hyperbolic distance and converting the hyperbolic distance into a matching degree, superposing timeliness attenuation, data freshness punishment and state mutation filtering, and outputting a final matching result. According to the invention, a closed-loop system of policy analysis, accurate matching, resource allocation and feedback optimization is finally constructed, the financial resource waste rate is compressed to a low level, and the government affair service efficiency is improved.

The invention discloses a course session recommendation method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the channel splicing of a semantic embedding matrix of course text description data and a user behavior feature matrix, and generating a multi-dimensional feature matrix; constructing an adjacent graph and a hypergraph between courses according to the multi-dimensional feature matrix, performing graph convolution operation based on the adjacent graph and the hypergraph, and generating a target embedded representation vector of each course node; generating an attention weight of each course node, performing weighted aggregation on the target embedded representation vector based on the attention weight, and performing course session recommendation based on the target embedded representation vector after weighted aggregation; according to the method, the curriculum semantic features and the user behavior features are fused, so that the feature expression ability of curriculum nodes is effectively improved, local neighborhood features between curriculums and global features in a hypergraph structure are captured, dynamic changes of user interests are accurately captured, and accurate curriculum session recommendation is achieved.

The invention relates to the technical field of machine vision, in particular to an intelligent machining positioning method and system based on machine vision, and the method comprises the steps: collecting and preprocessing original visual image data, and extracting geometric structure features and surface morphological features from the preprocessed visual image data, thereby comprehensively mining the characteristics of a workpiece; when the machining positioning offset vector is determined, the geometric structure features are matched with the preset machining reference template, and the workpiece machining position deviation can be preliminarily determined; on the basis of surface morphological characteristics, compensation adjustment is carried out on a machining positioning offset vector to generate an optimized positioning vector, the complex condition of the workpiece surface is considered, positioning deviation possibly generated according to a geometric structure purely is corrected, and positioning is more accurate; a machining path adjusting instruction is generated according to the optimized positioning vector and transmitted to a machining control unit, machining equipment can be effectively guided to adjust the machining path according to the actual state of the workpiece, it is ensured that the machining process is accurate and efficient, and the workpiece machining quality and production efficiency are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a generation strategy optimization method, device and equipment based on a dynamic environment and a medium. Correcting the generated action vector by combining a domain constraint strategy to obtain a compliant action vector, constructing a multi-dimensional reward vector according to feedback after execution, scaling the reward vector into a reward signal, and finally updating the pre-training generative model by adopting a self-adaptive strategy optimization module based on the reward signal and an interaction track to obtain a reward result. And collaborative evolution of strategy generation and environmental response is realized. According to the method, by introducing dynamic environment information and domain constraints, compliance correction and optimization updating of the generative strategy are realized, and the stability and practicability of the model in a complex environment are improved.

The invention discloses a track bridge health assessment method based on deep learning and multi-modal data fusion, and aims to overcome the technical defects of insufficient multi-source data collaboration and weak spatial-temporal characteristic coupling capability in the existing method. The spatial-temporal deep analysis of multi-modal data is realized by constructing a CNN-LSTM-DNN model. According to the technical scheme, the method comprises the following steps: 1) deploying acceleration, strain and temperature sensor clusters, and generating a space-time aligned heterogeneous data set through time synchronization, low-pass filtering and normalization processing; 2) extracting spatial topological features of a sensor network by using a CNN, and capturing a structure local deformation and load distribution mode; 3) analyzing a time sequence evolution rule of parameters such as bridge vibration and temperature change through LSTM, and establishing a long-term dependency relationship of dynamic load-environmental disturbance-structural response; 4) designing a weighted attention mechanism, fusing the spatial-temporal feature vector, and generating a comprehensive feature containing multi-physics coupling information, and 5) constructing a nonlinear mapping model based on DNN, outputting a comprehensive health index (CHI), and dividing damage levels.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention discloses an unmanned ship cluster cooperative communication method and system, particularly relates to the technical field of unmanned ship cluster communication, and is used for solving the problem of poor unmanned ship cluster cooperative communication. According to the method, a cooperative communication mechanism with real-time sensing and self-adaptive capabilities is constructed, and effective links meeting communication distance and link weight conditions are screened out by collecting spatial position, speed and course information of the unmanned surface vehicle, constructing a standardized state vector and generating and dynamically maintaining a communication topological graph; monitoring a link load in real time, calculating a node congestion sensing factor, triggering path optimization, selecting an optimal path based on a distance and the load, and adjusting a forwarding rate; a scheduling factor is constructed by combining a task time limit, an action node range and a delay sensitivity level, a priority score is generated, a communication task is scheduled to an optimal link and an optimal time period according to a task priority, and space-time collaborative scheduling optimization of an unmanned ship cluster according to a task level is realized.

The technology described herein relates to systems, methods, and computer storage media, among other things, for providing an enhanced conceptualized search based on enriched categorizations of item listings. For example, the enhanced conceptualized search can include generating a search vector, using one or more generative artificial intelligence (AI) models, based on a received search query. Some embodiments of generating the search vector may include applying one or more generative AI models to one or more sets of prior user interactions. Based on the search vector, a particular item listing vector can be determined. The item listing vector can be generated by applying one or more generative AI models to each of an image of the item and a textual description of an item listing, and the item listing vector can be generated based on enhanced categories determined (e.g., using one or more generative AI models) for the item listing.

The invention discloses an AI search recommendation method, system and device combined with commodity semantic understanding and a medium, and belongs to the technical field of commodity recommendation. The method comprises the steps that semantic vectors of commodity titles and descriptions are generated; generating a feature vector of the commodity image; fusing the semantic vector of the commodity title and description with the feature vector of the commodity image to generate a comprehensive semantic vector of the commodity; generating a user interest vector; updating the user interest vector by using a recurrent neural network based on the user interest vector; and carrying out joint modeling on the comprehensive semantic vector of the commodity and the updated user interest vector to generate a recommendation result. According to the method, a content semantic driving modeling mode is adopted, recommendation judgment can still be conducted through the deep matching relation between commodity image-text semantics and user behavior preferences even under the condition that user historical behaviors are limited or new commodities are online, good cold start adaptability is achieved, and the method is suitable for popularization and application. And meanwhile, high recommendation difference and content diversity are shown for different user groups.

The invention discloses an image optimization method based on a CLIP model, and the method comprises the steps: S1, carrying out the coding initial processing of characters and images, inputting a character description and an initial image code into a CLIP model, and obtaining a corresponding semantic vector and a visual vector; s2, cross-modal contrast learning: based on positive and negative sample contrast and an adaptive attention mechanism, aligning the semantic vector and the visual vector obtained in the S1; s3, self-supervised learning is carried out, and precision improvement of semantic-to-visual mapping is completed through self-supervised learning; s4, feature refinement generation: based on the optimized semantic and visual feature vectors, generating a preliminary concept conforming to description; and S5, image output: repeating the steps S1 to S4 until a preset matching value is met, and outputting a conception image matched with the text description. According to the image optimization method based on the CLIP model, semantic mapping from characters to images is improved, so that the semantic mapping is more in line with the expectation of natural language description, and a visual conception graph with rich details is generated.

The invention provides an unstructured data processing method and system, and relates to the technical field of natural language, and the method comprises the steps: carrying out the tensor decomposition of unstructured data, obtaining a standardized feature vector, constructing a self-organizing critical state feature learning network in an isolation calculation space, constructing a feature interaction network based on quantum entanglement perception, and carrying out the recognition of the feature interaction network. And establishing a hierarchical causal graph for feature screening, realizing semantic feature vector generation by adopting multi-scale feature fusion, and establishing an intelligent protection mechanism. The feature extraction accuracy can be effectively improved, the data processing reliability is enhanced, and the system operation efficiency is improved.

The invention relates to the technical field of image analysis, in particular to a dispensing track optimization control system based on visual template conversion, which comprises a visual template analysis module, a contour point position correction module, a track vector generation module, a multi-parameter linkage feedback module and an optimal path planning module. According to the method, a boundary is extracted through clustering pixel superposition brightness and channel weight, a line segment frequency screening track is counted, feature stability and precision are enhanced through datum line construction, a deviation value is calculated through gradient direction segmentation boundary, a matching degree is improved through tangent point interpolation correction contour, and a weight factor is generated through included angle change and tool parameter normalization. A reference point is optimized through superposition increment, a path fusion tool attribute dynamic adaptation parameter is adopted, a multi-dimensional data fusion visual offset and floating weight real-time adjustment track is acquired, an execution point and an adjustment vector are superposed to calculate a distance matrix, an optimal path is screened through minimum distance and energy consumption, efficiency and consumption are both considered, and the system robustness and execution economical efficiency are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a task processing method and device based on visual attention enhancement, equipment and a medium. Visual hierarchical features are extracted, a double fovea attention module processes and fuses high-level visual features, a side suppression network obtains enhanced visual features, and a cross-modal fusion module generates fusion features by taking the enhanced visual features as query vectors and taking language components and action components as key and value vectors; and fusing the feature input decision network to generate target category and position information, generating feedback information based on actual label difference, and updating module parameters to complete a target task. According to the invention, through combination of a bionic vision mechanism and multi-modal attention fusion, the visual feature extraction and background suppression capability is improved, and the target capture efficiency and recognition precision in a complex scene can be improved.

An autonomous vehicle comprises a sensor unit configured to generate an environmental signal corresponding to a surrounding environment. A vector generator unit generates a numeric vector from the environmental signal received from the sensor unit. A pre-processing unit receives the numeric vector and generates a vector embedding from that numeric vector. The vector embedding comprises a structured semantic representation of the surrounding environment of the autonomous vehicle. A language model unit uses a machine learning language model to generate a textual data output based on the vector embedding. A controller receives the textual data output and generates one or more driving commands, and the controller operates the autonomous vehicle based on those commands.