8277 results about "Data structure" patented technology

The invention provides a pre-control and monitoring method for a whole process of actual grouting engineering based on digital geological model, comprising: extracting discontinuous fracture surfaces in a geologic body structure, to build a underground geologic body structure model; build a multi-source geologic body attribute model, optimizing and solving grouting simulations of a disaster high-risk region by using multiphase flow calculation method, and controlling grouting equipment by using optimized grouting parameters to carry out an actual grouting engineering; obtaining a multi-factor diffusion range of variable coefficients by adjusting physical values of the parameters in the grouting simulation and attribute data structure model by using a control variable method, learning and capturing complex mapping relationship between different parameters by using a neural network, optimizing the grouting parameters in the actual engineering in real-time, so as to realize the pre-controlling, monitoring and optimization of the whole process of the actual grouting engineering.

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

A knowledge management system may generate a plurality of prompts based on divisions of documents of unstructured text, each prompt relevant to a division of unstructured text. At least one prompt is generated such that a corresponding division of unstructured text is a response to said at least one prompt. The system may generate prompt embeddings for the plurality of prompts corresponding to the plurality of documents of unstructured text. The system may generate prompt-embedding clusters to group similar prompts from one or more documents of unstructured text. The system may receive a query. The system may convert the query to one or more query embeddings. The system may identify one or more prompts that are relevant to the query based on comparing the one or more query embeddings to the prompt embeddings. The system may identify one or more documents in one or more prompt-embedding clusters.

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The disclosed embodiments provide systems and methods for continuous exposure and attack surface management using a data fabric. Data from multiple heterogeneous cybersecurity sources, including vulnerability scanners, threat intelligence, cloud security tools, and endpoint monitoring systems, is ingested and integrated into a semantically harmonized representation, such as a security knowledge graph. This unified data model normalizes, correlates, and contextualizes diverse cybersecurity information, enabling comprehensive and real-time assessment of an organization's cybersecurity risk posture. Automated workflows trigger proactive remediation actions based on dynamically calculated exposure metrics. Additional embodiments leverage the same data fabric architecture to support specialized cybersecurity use cases, including unified vulnerability management (UVM), cyber asset attack surface management (CAASM), continuous threat exposure management (CTEM), and asset exposure management (AEM).

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

The invention relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) using a gateway router that dynamically coordinates the agents based on prompt characteristics, user context, and / or real-time operational factors. Received inputs (e.g., prompts) are segmented into subcomponents (e.g., sub-queries), which are routed / mapped to candidate agents based on the output parameters of the subcomponent (e.g., performance thresholds, cost thresholds) and operational parameters (e.g., cost, performance metric values, user access restrictions, timing restrictions) of each agent. The gateway router maintains dynamic routing data structures for each agent that are continuously updated based on environmental stimuli (e.g., geo-political stimuli, sensor stimuli, agent stimuli). For example, the gateway router causes agents to dynamically switch between rule engines identified by the routing tables in response to detecting environmental stimuli. Responses from the candidate agents are aggregated into an output that is responsive to the input.

Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and / or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).

The disclosed embodiments provide systems and methods for automated mapping of raw data into a data fabric. An innovative approach leveraging Artificial Intelligence (AI)-powered tools and a data fabric to automate the ingestion, transformation, and integration of raw data into a unified model is introduced. By automating the data mapping process, organizations can reduce reliance on manual methods and accelerate their ability to utilize robust insights for exposure management and attack surface reduction. The disclosed solution provides a scalable architecture for unifying cybersecurity signals across cloud and hybrid environments, enabling real-time decision-making and improved organizational resilience against cyber threats

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention discloses a water conservancy project construction monitoring data supervision system and method based on multi-source data fusion, and relates to the technical field of engineering construction. The multi-source data fused hydraulic engineering construction monitoring data supervision system and method comprises the following steps: S1, acquiring hydraulic engineering construction monitoring data, and preprocessing the hydraulic engineering construction monitoring data; s2, constructing a sliding window, extracting key structure and environment variables, comprehensively analyzing a structure disturbance degree, and dividing different disturbance state sections according to the structure disturbance degree; s3, an unstable state time period is extracted, a disturbance feature sequence is constructed, highly-suspected disturbance fragments are identified, response deviation between the structure and the environment is analyzed, highly-credible abnormal fragments are screened, and an abnormal data structure is generated; and S4, the coupling relation between the structural response and the environmental disturbance is analyzed in a combined mode, and a dynamic unbalance index used for measuring the unsteady state intensity is constructed. The problem that the abnormal recognition false alarm rate is high due to the fact that monitoring data are disturbed frequently in a complex construction scene is solved.

The invention provides a cache information processing method and device of a port platform and a medium, and belongs to the technical field of port distributed information processing. An update request is received, and authentication and flow limiting processing are performed on the request; obtaining a cache key prefix and a data structure type, routing a cache key to a fragment node based on a consumption write-back event, executing a write-in operation in a distributed cache service, and setting survival time and multi-copy synchronization; delivering the message body to the message middleware and obtaining delivery confirmation; the messages are pulled from the queue according to the configured concurrency, deduplication verification is carried out on idempotent identifiers of the messages, and data persistence operation is executed after the verification message version is higher than the current version of the database; index collection points are implanted into all modules of the whole link, and the request delay, the cache hit rate and the message accumulation depth index are visually displayed. The reasonable distribution and optimization of system resources are realized; the consistency and integrity of the data are guaranteed, and the reliability of message transmission is improved at the same time.

Systems and methods for generating a workflow data structure are provided. The system includes one or more processors; and one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising: receiving input data comprising a corpus of documents, a user query, and query context data; processing the corpus of documents to generate training data; training a large language model (LLM) using the training data; classifying, using the LLM, the user query to at least one content cluster of a plurality of content clusters based on the query context data; constructing, using the LLM, a workflow data structure as a function of the classifying; and generating, using the LLM, a query response as a function of the user query, the query context data, and the workflow data structure.

Various embodiments relate to normalizing raw data by mapping the raw data to a computer-readable tag. A computer-readable tag may be an identifier that at least partially represents a category (e.g., a department) and / or the raw data itself. In response to receiving the raw data, some embodiments perform the mapping by, for example, performing natural language processing (NLP) on each particular department's raw data to associate natural language words in the raw data to its corresponding computer-readable tag and then populating, at a data structure that includes the computer-readable tag, an entry with data (representing the raw data) in a standardized format. In this way, regardless of whether different sets of raw data come from disparate sources that have diverse formats, protocols, or structures relative to each other, the normalized data and standardized form makes the data compatible.

The embodiment of the invention provides a multi-source heterogeneous data integration method and device fusing a large model conversion operator. The method comprises the steps that heterogeneous data are collected from a multi-source heterogeneous data source, preprocessing operation of data cleaning is carried out, and preprocessed data features are obtained; inputting the preprocessed data features and the target format into a large language model, and generating a conversion operator including data structure analysis, field mapping and type adaptation; and based on the conversion operator, performing distributed parallel data conversion and integration in a distributed computing framework, and integrating and verifying the integrated data. According to the scheme, by introducing the intelligent reasoning ability of a large language model, the efficiency optimization of distributed calculation and the quality verification mechanism of the whole process, the core problems of the traditional data integration technology in the aspects of rule stiffness, high manual dependency and quality control deficiency are systematically solved.

The invention provides a building construction scheme intelligent auditing system based on a large model, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps that a construction scheme template library and a building construction standard specification library are constructed, a building specification knowledge graph is constructed, a to-be-audited construction scheme document is received and subjected to structural division and preprocessing, semantic vector data are obtained, matching operation is conducted on the semantic vector data and graph nodes in the building specification knowledge graph, and the graph nodes in the building specification knowledge graph are obtained; and outputting audit target data, analyzing and generating an analysis result data structure based on the audit target data, generating an audit report according to an analysis result, and completing recording and tracking of an audit process. The method has the beneficial effects that automatic auditing and whole-process tracking of the construction scheme are realized by constructing the structured template and the standard specification knowledge graph in combination with semantic vector matching and rule work efficiency comparison, the accuracy, consistency and efficiency of auditing are effectively improved, and the informatization and intelligence level of building construction management is remarkably enhanced.

The invention discloses a logistics traceability system and method based on a block chain technology, and relates to the technical field of logistics traceability, and the method comprises the steps: constructing an event graph structure containing a plurality of behavior event nodes, and calculating a structure closure signature value; judging whether the original data of the behavior event node is written into the block chain or not according to the responsibility structure weight value; constructing a verification path mapping graph based on the structure closure signature value and the responsibility structure weight value; when a traceability request is received, calling the verification path mapping graph, performing consistency verification on the original data of the target behavior event node and the structure mapping hash value, and generating a traceability verification result; according to the method, the credibility of the behavior event data structure is improved, meanwhile, the on-chain storage cost is effectively reduced, and the technical contradiction between full-uplink data and non-uplink data is solved.

Systems and methods are described for providing for display a user interface to facilitate creation of a three-dimensional (3D) environment. The disclosed techniques may generate the 3D environment based on one or more inputs received via the user interface, and perform visual processing of the 3D environment to obtain a natural language description of the 3D environment. The disclosed techniques may determine a context of the 3D environment based at least in part on the natural language description and may store the context in a data structure. A 3D content library may be queried, based on the natural language description of the 3D environment, to identify at least one recommended 3D object that is relevant to the stored context of the 3D environment. The disclosed techniques may providing for display, at the user interface, selectable option(s) to add the at least one recommended 3D object to the 3D environment.

A parallel processing unit comprises a plurality of processors each being coupled to a memory access hardware circuitry. Each memory access hardware circuitry is configured to receive, from the coupled processor, a memory access request specifying a coordinate of a multidimensional data structure, wherein the memory access hardware circuit is one of a plurality of memory access circuitry each coupled to a respective one of the processors; and, in response to the memory access request, translate the coordinate of the multidimensional data structure into plural memory addresses for the multidimensional data structure and using the plural memory addresses, asynchronously transfer at least a portion of the multidimensional data structure for processing by at least the coupled processor. The memory locations may be in the shared memory of the coupled processor and / or an external memory.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention relates to the technical field of energy consumption analysis, in particular to an electric vehicle energy consumption analysis system and method, and the system comprises an operation factor correlation module, a speed slope coupling extraction module, a slope interference correction module, an energy consumption stability discrimination module and a deviation trend recognition module. According to the method, the associated gradient and vehicle speed data are extracted and combined with the vehicle mass to form a three-dimensional data structure, energy consumption distribution can be predicted and evaluated more accurately, and power fluctuation caused by gradient change can be effectively identified by associating electric power output with the gradient of the gradient and the vehicle speed change rate. By comparing the deviation between the actual power state and the ideal power state and correcting gradient interference, the accuracy of energy consumption data is improved, the fine granularity of electric vehicle energy consumption management is enhanced, and by comparing the electric power and kinetic energy changes in a cross mode, power abnormal deviation can be recognized. And more reliable data support is provided for energy-saving optimization of the electric vehicle.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention relates to the technical field of natural language processing, in particular to a knowledge graph fused reservoir emergency plan large language model review method. The method comprises the following steps: acquiring a to-be-examined reservoir emergency plan, and performing data structured processing; a first neural network model is combined with a multi-scale context sensing mechanism to comprehensively capture local and global semantic information, and knowledge graph representation of a reservoir emergency plan text is constructed; a second neural network model is adopted to process the chart type information, and a reservoir emergency plan multi-modal knowledge graph is constructed; and meanwhile, an open source large language model of a Transform architecture is finely adjusted by adopting comparative learning, a special reservoir emergency plan large language model is constructed, and complementary enhancement of language model semantic embedding and knowledge graph structure representation is realized through a bidirectional enhancement mechanism. And finally, in combination with a graph attention mechanism, embedding the multi-modal knowledge graph into the fine-tuned large language model for comprehensive review, and generating a review report.