308 results about "Graph analytics" patented technology

Embodiments described herein relate to systems and methods for automatically generating content for a generative content interface of a collaboration platform. The system may perform an intent analysis on a natural language user input to the generative content interface to determine an intent confidence score with respect to each of a set of request classifiers, the set of request classifiers comprising a first request classifier associated with a request for an action, a second request classifier associated with a request for information, and a third request classifier associated with a request for a contact. Based on the intent confidence scores of the request classifiers, the system may select a content store in which to search for content to satisfy a user's query.

The invention relates to the technical field of geographic information systems, in particular to a GIS-based urban land planning analysis method and system, and aims to construct a multi-scale space-time cube model by integrating remote sensing, historical planning, mobile terminals and social media data, generate standardized urban temporary land data and improve the urban land planning analysis efficiency. Adaptive feature extraction is adopted to identify temporary land, a hierarchical classification system is established, abnormal changes are monitored through a time sequence model, an association diagram of the temporary land and an urban function network is constructed, an interaction relation is analyzed, finally, differentiated urban planning optimization suggestions are proposed, efficient configuration and management of urban land resources are assisted, and through fusion of multi-source data, the urban planning optimization suggestions are optimized. Multi-dimensional cognition of space, time, functions and relations is achieved, and the understanding depth of temporary land is fundamentally improved.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

A system and method for real-time team intent modeling using persistent cognitive machines with federated human profiles which processes individual team member behavioral signals through geometric intent analyzers that generate high-dimensional vector representations of individual objectives and preferences. A team intent orchestrator aggregates individual vectors into collective representations within a dynamic geometric manifold that evolves based on team coordination patterns. Federated human profiles enable privacy-preserving knowledge sharing across teams through geometric abstraction techniques that preserve coordination utility while protecting individual privacy. The system implements proactive conflict detection through trajectory analysis that identifies potential coordination issues before performance impact, and provides real-time synchronization mechanisms that maintain team coordination coherence despite individual behavioral changes. Cross-team learning capabilities enable organizational intelligence development through pattern abstraction and context-aware adaptation of successful coordination strategies. The persistent cognitive architecture maintains coordination patterns across sessions and team composition changes, enabling continuous improvement through accumulated team experience.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention provides an ecological environment sensitive area dynamic change evaluation and prediction method based on machine learning, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: obtaining and preprocessing remote sensing image data, and extracting an environment element feature sequence; adopting a hierarchical memory distillation network to extract periodic characteristics of the long-term change sequence, and combining with the short-term change sequence to carry out space-time recombination; utilizing a graph neural network to construct a dynamic association graph to analyze an environmental element coupling relationship; and evaluating the sensitivity and performing early warning based on the sudden change amplitude and the cumulative change trend. According to the invention, the dynamic change characteristics of the environmental elements can be accurately captured, and the accuracy and timeliness of early warning of the ecological environment sensitive area are improved.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intention analysis and strategy generation method, device, equipment and medium, and the method comprises the steps: obtaining interaction data, recognizing an interaction type according to metadata features, selecting a corresponding analysis model to analyze and process data, and generating a text corpus; current user information is extracted from the text corpus, user intention score analysis is executed based on an intention analysis strategy corresponding to the interaction type, and a current user intention score is generated; and generating a demand list according to the user information, the intention score and the interaction type, and generating and outputting an interaction strategy based on the user information and the demand list. Through interaction data processing and automatic intention scoring analysis, the demand list and the interaction strategy of the customer can be quickly and accurately generated, the customer information processing efficiency and accuracy are improved, the workload of manual input and analysis is reduced, and the customer service quality and the response speed are improved.

The invention provides an infrastructure security analysis method and system based on a large model, and the method comprises the following steps: collecting multi-source heterogeneous data of an infrastructure, including physical equipment sensor data, network flow data, operating system logs and external threat intelligence; converting the multi-source data into standardized feature vectors of a unified time axis through a space-time alignment module; and inputting the standardized feature vector into the fine-tuned large language model for semantic understanding, and generating an equipment behavior semantic description and threat intention analysis result. According to the method provided by the invention, a large language model and reinforcement learning collaborative architecture is provided, and the large language model is improved, so that end-to-end mapping from multi-source data to threat semantics is realized, and the attack detection rate is improved.

The invention relates to the technical field of industrial internet, and discloses a multi-dimensional data integration and dynamic risk assessment method for enterprise purchase anomaly detection, which comprises the following steps: establishing a reliability verification matrix of multi-dimensional purchase data, and extracting abnormal data points; identifying a cross correlation mode of the abnormal data points, and constructing a purchase behavior interaction graph of the target enterprise; analyzing a risk evolution path of the purchase risk factor, and calculating a risk conduction entropy value; and according to the risk conduction entropy value, generating a risk grading label of the purchase risk factor so as to output a purchase anomaly detection report of the target enterprise. According to the invention, the accuracy of enterprise purchase anomaly detection can be improved.

The invention provides a multi-source financial data intelligent classification and automatic accounting optimization processing method and system, and relates to the technical field of enterprise financial management, and the method comprises the steps: carrying out the textualized preprocessing of multi-source enterprise financial data, constructing a transaction scene map, carrying out the scene clustering, forming a transaction classification rule set, and carrying out the classification of transactions, according to the method, labels and accounting subject codes are matched for conventional transaction data, a transaction traceability graph is constructed to analyze the integrity of a transaction link, and an accounting voucher is generated to realize automatic accounting, so that the accuracy and efficiency of financial data processing are improved, the manual intervention cost is reduced, and the traceability of financial data is enhanced.

The invention belongs to the technical field of intelligent substations, and particularly relates to an intelligent nuclear phase method and system for a substation. According to the method, a dynamic priority evaluation model is constructed, multi-source data of four kinds of basic factors including voltage phase difference, environment temperature and humidity, equipment vibration and historical errors are synthesized, and a fuzzy comprehensive evaluation method is utilized to calculate weights and dynamically sort the weights; a multi-port phase scattering model is constructed based on transformer substation topology, impedance mismatch points are analyzed by means of a Smith chart, and dual-stage optimization is implemented; constructing a three-layer decision input set according to the optimization model, designing a fuzzy logic driven adaptive rule engine, dynamically outputting a working condition level and matching a nuclear phase strategy; multi-dimensional quality evaluation, dynamic updating of factor weights and optimization of fuzzy model parameters are carried out by monitoring indexes such as reflection coefficients in real time, abnormal mode recognition and compensation strategy solidification are realized by fusing LSTM and a graph neural network, a self-evolution closed-loop system is formed, and finally, crossing of a nuclear phase process from accurate matching to autonomous evolution is realized.

The invention belongs to the crossing field of computer technology and business management, and discloses a purchase return risk early warning and management method and system based on multi-mode intelligent auditing, electronic equipment and a computer readable storage medium. The method comprises the following steps: segmenting a PDF invoice image through an improved Apache PDFBox analyzer; a CLIP model and an OCR technology are combined, a cross-modal attention layer is used for correcting an OCR text, a multi-modal consistency difference value is generated, and invoice authenticity is judged; when a false invoice is detected, generating a standardized risk event, and extracting supplier, purchaser and transaction edge data from the graph database to construct a time sequence sub-graph; and identifying an abnormal mode based on the graph neural network, verifying the risk by using a time sequence risk scoring model, and executing a treatment strategy. According to the method, through multi-modal fusion, dynamic graph analysis and time sequence modeling, the problems of low false invoice detection precision, long abnormal fund tracing time consumption, risk prediction lag and the like in traditional auditing are solved.

The invention relates to the technical field of business service dynamic optimization, in particular to a customer demand dynamic analysis and consultation service optimization platform based on deep learning, which comprises a user portrait module, an intention analysis module, a knowledge graph module, a learning engine and a recommendation module, the intention analysis module is connected with the knowledge graph module, the knowledge graph module is connected with the learning engine and the recommendation module, the learning engine is connected with the user portrait module and the recommendation module, and the recommendation module is connected with the user portrait module; according to the method, the accuracy of new user demand understanding is remarkably improved, the system can still provide accurate services in the absence of historical data through dynamic extension of the knowledge graph and an incremental learning mechanism, the problem of insufficient personalization caused by user portrait missing is thoroughly solved, and the user experience is improved. And meanwhile, the security and adaptability of data processing are ensured through multi-modal analysis and federal learning.

The invention provides a social robot intention recognition system and method based on heterogeneous graph analysis, and the method comprises the steps: carrying out the unified modeling of multi-type nodes and multiple relation edges based on a heterogeneous graph structure, and systematically capturing the complex multi-dimensional behavior association in a social platform; according to the method, technologies such as a large language model and a knowledge graph are fused, fine recognition and classification of multiple types of behavior modes are supported, and meanwhile, hierarchical evaluation for different organized risks is realized by combining account behavior aggregation and emotional polarity analysis, so that a decision basis is provided for supervision and governance; according to the method, automatic collection and preprocessing of multi-source heterogeneous data and heterogeneous graph construction processes are integrated, manual intervention and repeated work are reduced to the maximum extent, the overall data processing efficiency and response speed are improved, and the real-time analysis requirement in a complex dynamic social environment is met; transparent analysis of robot control sites and behavior strategies is supported, and the interpretability of the system, the user credibility and the executive force of policy supervision are greatly improved.

A system and method for privacy-preserving communication validation and multi-attribute trust scoring is disclosed. The system analyzes communication metadata to determine pattern legitimacy by comparing current communication patterns against relationship fingerprints without accessing communication content. The system validates relationship context between communicating parties using interaction graph analysis and historical communication data. Cross-network intelligence correlation compares current patterns against aggregated patterns across voice, email, and messaging services, creating a self-strengthening security framework that recognizes emerging threat patterns while validating legitimate communication behaviors. The system generates comprehensive multi-attribute trust assessments comprising individual trust attribute scores including engagement rate, reliability index, channel preference, temporal pattern, and behavioral pattern, combined into overall trust levels. Trust context is displayed through a user interface presenting simplified, intuitive, and actionable information with progressive disclosure capabilities, enabling informed user decisions while preserving privacy. Communication processing actions provide users with appropriate engagement options tailored to specific trust assessment results.

The embodiment of the invention relates to a data query method and device based on a cloud search service, equipment and a medium, and the method comprises the steps: in response to an input statement of a user receiving the cloud search service, obtaining key information corresponding to the input statement, obtaining target meta-information corresponding to the input statement from a preset intention meta-information library, and obtaining the target meta-information corresponding to the input statement; generating model prompt information based on the target meta-information and the key information; wherein the intention meta-information base comprises a plurality of pieces of preset meta-information, the meta-information is used for defining the information of the intention to be analyzed, and the key information comprises keyword information and / or semantic information; based on the model prompt information, generating an intention analysis result through a preset intention analysis model; generating a target query statement based on the intention analysis result; and performing query processing based on the target query statement to obtain a query result corresponding to the input statement. According to the embodiment of the invention, the accuracy of the obtained query data is effectively improved, so that a more accurate and reliable query result is ensured to be obtained.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The invention relates to an electrical variable adjustment monitoring method and system for a distribution box, and the method comprises the steps: collecting the current, voltage, power factor and other core electrical variables of each distribution box in a region in real time, triggering an abnormal mark through dynamic gradient analysis, and recording time-space information; performing space-time correlation analysis based on the power distribution network topological graph, calculating an abnormal time concentration ratio and a spatial distribution density, and generating a correlation circle; determining a fault type (a local problem or an overall power grid problem) according to the characteristics of the associated circles, the number of branch lines and the change of electrical variables, and matching a corresponding processing scheme; and continuously monitoring a fault processing result, and generating a repair report until the system is stable. The system comprises a core electric variable real-time acquisition module, a power distribution network topological graph analysis module, a fault type judgment module and a fault processing monitoring module. According to the method, the anomaly detection accuracy and the fault processing efficiency are improved, and reliable operation of the power distribution network is guaranteed.

The invention relates to a cloud-edge collaborative medical image intelligent diagnosis method and device, equipment and a medium. The method comprises the following steps: firstly, obtaining original image data, and carrying out calibration processing on the original image data through a preset multi-modal image standardization model to obtain a standard image data set; performing feature extraction and grading processing on the standard image data set at an edge end to obtain a grading feature set, transmitting the grading feature set to a cloud end through a bandwidth allocation strategy, and generating a cloud end receiving feature subset; constructing a feature expression matrix based on the cloud receiving feature subset, using a network topology structure to carry out graph analysis, and extracting a path to generate a diagnosis path set; and finally, feedback diagnosis information is formed based on the diagnosis path set, and a task allocation optimization result is obtained by dynamically adjusting a cloud side task allocation proportion. According to the method, efficient processing and diagnosis optimization of medical image data are realized, efficient collaboration of cloud edge resources is ensured, timeliness and accuracy of medical image diagnosis are effectively improved, and resource scheduling requirements in different scenes are met at the same time.

The invention provides a multi-source financial data intelligent classification and automatic accounting optimization processing method and system, and relates to the technical field of enterprise financial management, and the method comprises the steps: carrying out the textualized preprocessing of multi-source enterprise financial data, constructing a transaction scene map, carrying out the scene clustering, forming a transaction classification rule set, and carrying out the classification of transactions, according to the method, labels and accounting subject codes are matched for conventional transaction data, a transaction traceability graph is constructed to analyze the integrity of a transaction link, and an accounting voucher is generated to realize automatic accounting, so that the accuracy and efficiency of financial data processing are improved, the manual intervention cost is reduced, and the traceability of financial data is enhanced.

The invention belongs to the technical field of image processing, and particularly relates to a filamentous structure topology perception fine feature extraction method and system based on a large model, and the method comprises the steps: employing a filamentous structure feature extraction large model to predict an original image of a filamentous structure, and obtaining an initial likelihood graph with a foreground probability as a likelihood value; calibrating the initial likelihood graph to obtain a corrected likelihood graph, analyzing the corrected likelihood graph and constructing a manifold; stable manifolds are obtained through continuous homology analysis, and a union set of all the stable manifolds serves as a topological skeleton diagram of a filamentous structure; using an uncertainty quantification model to predict each stable manifold confidence in the topology skeleton graph to form an uncertainty graph; and optimizing a segmented image predicted by a filamentous structure feature extraction large model in combination with a topological skeleton graph and a corresponding uncertainty graph. According to the method, a prediction result is improved by using a topology perception and uncertainty quantification method, and the accuracy and reliability of fine and complex filamentous structure feature extraction and segmentation are improved.

The invention relates to the technical field of attribute graph collection and release, in particular to an attribute graph collection and release method, system and device based on local differential privacy and a medium. Receiving disturbed data from each client; based on all disturbed data, estimating community division of the original graph structure and triangular counting of each node; obtaining a target graph structure based on community division and triangular counting; and on the basis of the target graph structure and the disturbed data, node attributes are optimized, so that node attribute distribution and community division are kept consistent, and a final publishable target attribute graph is obtained. The method has the advantages that strict requirements of local differential privacy are met on the privacy protection level, and multi-dimensional features such as a community structure, triangular counting and node attribute homogeneity are reserved in the published attribute graph at the same time; and the practical value of the synthetic data in applications such as downstream graph analysis, community discovery and personalized service is greatly improved.

The invention discloses a building plane element recognition method based on a graph neural network and a convolutional neural network, and belongs to the technical field of building plane element recognition. The identification method comprises the following steps: inputting a building plane pixel image, and carrying out preprocessing through image layer screening and information extraction to obtain a binary pixel image; vectorizing the binarized pixel image to obtain a vectorized image; performing image segmentation on the vectorized image; using the segmented vectorized image to construct a regional adjacency graph; performing regional adjacency graph optimization on the regional adjacency graph through graph analysis and regional merging by using a graph neural network, and identifying a room edge; calculating the area of the room; using the convolutional neural network image recognition model to recognize articles in the room; and performing room function prediction according to an identification result. According to the method, the problem of shape blurring caused by convolution in a traditional method is avoided. The graph neural network model training effect is improved, and the classification accuracy is improved. The method is suitable for various planar graph styles and is high in universality.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

The subject technology implements a unified query frame that may retrieve identity records from multiple, asymmetric identity spaces. The unified queries of the unified query framework may include one or more steps, one or more paths, and a resolution scheme that may extract sets of identity records from multiple identity spaces. The unified query framework may be implemented in a graph analysis system that generates one or more identity subgraphs using the extracted sets of identity records. One or more aspects of the unified queries and or identity subgraphs may be configured to customize the data retrieval process for one or more applications. One or more search operations of the graph analysis system may be optimized to improve the speed and efficiency of data retrieval and reduce compute resources and cost.

An apparatus and method for improving ray tracing efficiency. For example, one embodiment of an apparatus comprises: An apparatus comprising: a binary instrumentation engine to perform binary instrumentation of ray tracing shaders and to trace execution of the ray tracing shaders to generate execution metrics; call graph construction logic to construct a shader call graph based on the execution metrics; shader source mapping logic to map the shader call graph to shader source code to generate a source code map; efficiency analysis logic to determine inefficiencies in ray tracing shader execution based on the source code map; and optimization logic to identify optimization actions based on the inefficiencies.

The invention discloses an LCD display screen production whole-process collaborative management method and system based on the Internet of Things, particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource stiffness caused by lack of global resource allocation dependency relationship analysis in a complex production scene in an existing collaborative management method. Equipment state data and material position data of each process are acquired in real time, a resource allocation relation graph with production equipment as resource nodes and production tasks as task nodes is constructed, a resource access sequence of each production task is analyzed to identify a circular waiting path, and a process association network and a production flow network are combined to evaluate a blockage propagation risk. And finally, on the basis of the relief urgency degree, the production tasks are selected to carry out resource reallocation so as to realize the relief of the circular waiting path, and the overall efficiency of the production line and the resource utilization rate are improved.

The invention discloses an iterative collaborative learning method for node characterization and a topological structure, which comprises the following steps: acquiring initial graph data containing a node set and an initial edge set, and initializing node characterization; constructing a dynamic edge predictor, calculating the structure confidence degree between the node pairs based on the current node representation, generating a candidate edge weight matrix, and obtaining an optimized adjacent structure through a sparsification strategy; inputting the optimized adjacent structure into a graph neural network encoder, performing information propagation and aggregation in combination with the node feature matrix, and outputting enhanced node representation; the enhanced node representation is fed back to the dynamic edge predictor, and the edge weight and the node representation are iteratively updated until the convergence condition is met; and outputting a final optimized graph structure and node representation for downstream graph analysis. According to the method, through iterative collaborative optimization of the graph structure and the node representation, the graph topology can be dynamically corrected, the discrimination capability of the node representation is enhanced, the method is suitable for various complex scenes such as a noise graph and a sparse graph, and the performance of a downstream graph analysis task is improved.

The invention provides a multilevel modular personal memory large model and a construction method thereof. The model comprises a receiving module for receiving information input by a user, converting the information into a unified text and language vector, storing a personal historical language vector and context information, extracting key features, analyzing intention, retrieving by using Elasticsearch and returning related context information, and generating a reply according to the related context information. According to the method, the multi-modal data processing capability is integrated, the traditional limitation is broken through, and image, voice and text associated storage is supported; constructing a dynamic memory enhancement mechanism to prevent important information from being covered; then an intention analysis module is innovated to accurately capture deep demands; the correlation and the adaptability of memory fragments are improved by adopting a multi-modal retrieval and graph structure correlation technology; and finally, through a language habit adaptation and logical reasoning module, the output conforms to the style of the user, potential requirements are deduced, a whole-process closed loop is formed, and breakthrough is achieved in the aspects of memory management, intention understanding and interaction naturalness.