11033 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.

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

The invention provides a road traffic anomaly detection method and related equipment based on holographic perception, and the method comprises the steps: carrying out the time-space registration and preprocessing of video streams, point clouds, vehicle motion states, traffic parameters and environment data collected by multi-source sensor equipment of a target road network, and obtaining a multi-source data stream; performing feature acquisition and fusion processing through a preset target detection model and a feature extraction network to obtain target feature data and scene semantic information; establishing a space-time diagram data structure according to the target feature data and the scene semantic information, and performing target trajectory analysis through a space-time attention mechanism; and carrying out association analysis processing in combination with the analysis result and the environment data to obtain target behavior characteristics and interaction relation data, and realizing road traffic anomaly detection through the target behavior characteristics and the interaction relation data. Through multi-source data fusion and a deep learning model, the accuracy of environment perception is improved, and multi-dimensional information in a complex road network is captured for road traffic anomaly detection.

An example operation may include one or more of storing user data in a storage device, receiving inputs via prompts displayed on a user interface of a software application, identifying a plurality of goals within the received inputs and generating a plurality of data structures corresponding to the plurality of goals, respectively, executing a generative artificial intelligence (GenAI) model on the plurality of goals and the stored user data to generate a plurality of custom images of the plurality of goals, and displaying a plurality of identifiers of the plurality of data structures corresponding to the plurality of goals on a user interface, and simultaneously display the plurality of custom images next to the plurality goals, respectively.

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).

This disclosure improves computer functionality by enabling various hierarchies of chatbot application programs operative based on data structures containing unstructured texts. Therefore, such hierarchies enable some chatbot application programs to manage other chatbot application programs, which improves virtual assistance, reduces programming efforts, customizes output by user types, and enhances process management.

Systems and methods for implementing trained embedding mappings for improved retrieval augmented generation are disclosed. A system can maintain a dataset comprising a first set of embeddings corresponding to a first embeddings space and stored in association with a set of query results for the first set of embeddings. The set of query results can correspond to a second embeddings space. The system can train a transformation data structure using the first set of embeddings and the set of query results. The transformation data structure can be used to transform the first set of embeddings to the second embeddings space. The system can execute a search operation for the second embeddings space by applying the transformation data structure to a second set of embeddings corresponding to the first embeddings space.

Systems, methods, and computer-readable media for modeling cyber resilience data using cyber resilience identities and associated metadata are disclosed. A system can include one or more processing circuits configured to receive or identify cyber resilience data, encrypt a portion of the cyber resilience data, and generate a metadata object including metadata of cyber resilience data. The processing circuits can generate a cyber resilience identity including at least a link with the metadata object, a unique identifier (UID), and a performance event dataset. The processing circuits can encapsulate the cyber resilience identity within a control structure restricting one or more updates and redemptions of the metadata object, determine at least one access data structure being compatible with the control structure, and broadcast, using the control structure, the cyber resilience identity to a ledger or distributed ledger.

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 vertical field document question-answering method and system based on a knowledge graph enhanced large model, and the method comprises the following steps: S1, construction of a knowledge graph: collecting related data of a vertical field, and cleaning and sorting the related data; performing knowledge extraction on the processed data, and designing a domain ontology according to a knowledge extraction result to construct and store a knowledge graph; s2, questioning and answering based on the knowledge graph: extracting key entities and relationships in the input text, and querying related information from the knowledge graph according to the key entities and the relationships; and dynamically constructing a prompt based on the knowledge graph query result and the question, inputting the prompt into the large model, and generating an answer. The method has the advantages that data of professional fields are structured by constructing the vertical domain knowledge graph, authoritative and reliable background information support is provided during question answering, and it is ensured that model answering is more scientific and accurate.

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 invention relates to the technical field of data transmission, and discloses a signal I / O control device based on multiple protocols and a data transmission method thereof. The method comprises the following steps: initializing a multi-protocol signal I / O control device, and selecting a main controller; performing protocol characteristic analysis and protocol pre-configuration to obtain a plurality of pre-configured protocol schemes; performing data feature analysis and priority division on the data to be transmitted to obtain a multi-level priority data structure; carrying out transmission strategy optimization to obtain a target transmission strategy; network state change information is monitored in real time, a protocol switching mechanism of a DPPS algorithm is triggered, and dynamic protocol adjustment is carried out; in the data transmission process, exception detection is executed to obtain exception information, and a two-stage exception handling mechanism is executed to perform fault recovery and transmission performance evaluation to obtain a transmission performance evaluation report, so that timely transmission of important data is ensured, and the quality and reliability of data transmission are improved.

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.

The present disclosure relates to systems and methods for automatic generation of datasets for record objects using one or more large language models. A system can identify a plurality of electronic activities. The system can generate, using one or more large language models, a first set of text strings from the electronic activities. The system can store, in one or more data structures, a first association between the first set of text strings and the record object. The system can identify a second electronic activity. The system can generate, using the one or more large language models, a second set of text strings based on data corresponding to the second electronic activity and the first set of text strings. The system can store, in the one or more data structures, a second association between the second set of text strings and the record object.

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 relates to an AI-driven document data structured storage and retrieval method, which comprises the following steps of: analyzing and understanding document contents by utilizing a natural language processing technology, extracting structured question and answer knowledge, and constructing a structured question and answer knowledge base; a natural language processing technology and a large pre-training language model are utilized to analyze and understand a query request and a historical dialogue of a user, real intentions and demands of the user are extracted, and structured demand data are generated; and according to the user input and the demand analysis result, vectorizing the user input by using the pre-training language model, extracting related information from the vector database through a dynamic retrieval strategy, and generating and outputting a final answer. According to the method, deep analysis is performed on the document through a natural language processing technology, structured question and answer knowledge is extracted, and the performance and user experience of a question and answer system are improved by utilizing the semantic understanding ability of a large model.

The invention discloses an engineering drawing label identification method and system based on multi-modal information extraction, and the method comprises the steps: obtaining an engineering drawing image, carrying out the preprocessing of the image, and setting a data structure Schema for controlling an extraction target and a relation mode; detecting a drawing label area in the drawing through the trained drawing label detection model to obtain a bounding box coordinate of each drawing label, mapping the detected bounding box coordinates back to a coordinate system of the original image, and cutting out a corresponding drawing label image from the original image; performing text recognition on the cut-out signature image, and extracting text content and corresponding textbox coordinate information; and inputting the clipped signature image and the text recognition result into a trained multi-modal information extraction model, performing information extraction according to a set Schema, and outputting a structured extraction result. According to the method, the extraction target can be flexibly defined, unified extraction of the two-tuples and the three-tuples is supported, and the extraction accuracy is high.

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.

The invention relates to the technical field of building information modeling (BIM), in particular to a three-dimensional modeling method and system of a high-altitude curtain wall, multispectral data, structured light scanning data and thermal imaging data are collected through an unmanned aerial vehicle, the data are subjected to multidimensional analysis and fusion, three-dimensional modeling is performed through a multi-modal feature fusion algorithm, and the three-dimensional modeling method and system of the high-altitude curtain wall are obtained. And carrying out global and local geometric correction, material feature optimization and thermal feature correction on the model by utilizing a dynamic feedback and recursive optimization mechanism. And finally, generating high-precision three-dimensional model data through a multi-channel fusion algorithm. The method effectively solves the problems of insufficient geometric precision, limited material processing and neglect of thermal characteristics in the prior art, remarkably improves the precision and reality of the model, and is suitable for the fields of building design, structural analysis, energy consumption evaluation and the like.

An apparatus for generating a three-dimensional (3D) model of anatomical object via machine-learning, wherein the apparatus includes a process and a memory containing instructions configuring the processor to receive a set of images of an anatomical object pertaining to a subject, generate an 3D data structure representing the anatomical object as a function of the set of images using an anatomy modeling model, generate an initial 3D model of the anatomical object and refine the generated initial 3D model of the anatomical object as a function of the 3D data structure representing the anatomical object.

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.