651 results about "Unstructured data" patented technology

The present invention provides a system and method for causality-augmented generative intelligence capable of autonomously discovering non-obvious actionable insights from heterogeneous and multimodal data sources. The system integrates a data ingestion unit for semantic and temporal harmonization of structured and unstructured datasets, a causal inference processor for constructing a dynamically evolving directed causal knowledge representation using perturbation-based validation, a latent representation processor that combines multimodal semantic embeddings with causal parameters to generate fused latent vectors, and a generative insight processor utilizing causally constrained generative reasoning to synthesize hypotheses anchored to verified cause-effect dependencies. A validation processor performs counterfactual assessment and observational verification to ensure retention of only those insights that remain consistent with causal ground truth.

The invention discloses a water conservancy intelligent question-answering system and method based on knowledge enhancement and data driving, and aims at water conservancy business structured and unstructured data query, through loop optimization driven by positive and negative examples, precise classification of secondary intentions of water conservancy problems is realized, knowledge questions and answers, data query and professional water conservancy subclass questions and answers can be distinguished, and the system and the method can be applied to water conservancy business. And the semantic analysis efficiency is improved. Aiming at the problem of low query accuracy of retrieval enhancement generation in the field of water conservancy, a differential water conservancy knowledge base oriented to professional books, industrial standards and laws and regulations is constructed, local and networking information is processed through a multi-source knowledge fusion and conflict resolution mechanism, and the accuracy, interpretability and traceability of question and answer content are improved. Aiming at the problems of complex operation and low semantic query accuracy in query of massive water conservancy business data and monitoring data, multi-layer constraint Text-to-SQL conversion is performed based on water conservancy business knowledge, high-precision semantic query and automatic visual output of water conservancy structured data are realized, and query efficiency is improved.

The invention discloses a dynamic portrait construction method and system fusing a large model and user behavior data, belongs to the technical field of artificial intelligence and big data analysis, and aims to solve the problems of insufficient real-time performance, difficulty in multi-source data integration and high privacy risk in the traditional technology. The method comprises the following steps: collecting basic attributes, behavior sequences and unstructured data by burying points, and processing through an Apache Flink session window and a dynamic watermark; multi-modal feature extraction (discrete feature embedding, bidirectional LSTM coding behavior sequence and BERT coding text) is carried out, and joint embedding is generated through cross-modal contrast learning; generating three types of labels, namely a static label (rule engine), a dynamic label (1.3 B parameter quantity Nano-vLLM) and a predictive label (XGBoost), and dynamically adjusting weights; and realizing global model updating through federated learning and differential privacy. The system comprises a data acquisition layer, a feature extraction layer and a label generation updating layer. The real-time performance and accuracy of the portrait are improved, the privacy of the user is protected, and the commercial value in e-commerce, finance and other scenes is remarkable.

The invention discloses an industrial equipment fault inference system based on a knowledge graph, and the system comprises a knowledge graph construction module, a fault data collection module, an inference analysis module and a response processing module. The entity extraction unit extracts equipment components, fault types and maintenance record entities from an industrial equipment operation document, equipment manual unstructured data supplementation attributes are integrated, the attributes and association weights are marked, and the relationship construction unit establishes a fault causal relationship between the entities and a component association relationship to form a multi-level knowledge network; a knowledge verification unit verifies entity attribute consistency and relation rationality, a dynamic updating unit receives data updating nodes and relation strength of each unit and receives feedback data optimization weights, and in a fault data acquisition module, a real-time monitoring unit acquires operation parameters and state signals and associates equipment identifiers.

An AI-powered rating system includes a central artificial intelligence (AI) agent framework configured to orchestrate a rating process and automatically utilize available tools for data gathering and extraction; a data processing pipeline configured to simultaneously handle multiple streams of information from diverse sources, including structured data and unstructured data; a machine learning infrastructure comprising multiple specialized artificial intelligence models, each configured to evaluate a specific type of entity; a generative AI integration layer configured to provide a natural language explanation of a rating result for the rating process and enable conversational interaction with the system, where the generative AI integration layer is further configured to transform the unstructured data into quantifiable risk signals that are integrated with a structured data processing stream; and an output generation and reporting engine configured to deliver the rating result through one or more channels and formats.

The invention relates to the technical field of financial risk assessment, and discloses a financial risk assessment system based on artificial intelligence, which comprises a financial risk assessment system. The financial risk assessment system comprises a multi-source heterogeneous data intelligent acquisition layer, a dynamic feature engineering and knowledge graph layer, a hierarchical AI risk assessment engine, a real-time risk monitoring and dynamic early warning layer and a decision support and compliance management layer. According to the financial risk assessment system based on artificial intelligence, by constructing a multi-source heterogeneous data intelligent acquisition network, internal data and external unstructured data (such as public opinion dynamics, supply chain information and the like) of financial institutions are integrated, and cross-institution collaboration of data availability and invisibility is realized by adopting federal learning; according to the method, a hierarchical AI evaluation engine is fused with causal reasoning and an interpretability algorithm (such as SHAP / LIME), so that the evaluation precision is improved, the risk cause and the contribution degree of each feature can be clarified, and the requirement of financial supervision on the interpretability of an AI model is met.

The invention discloses a business process multi-dimensional logic verification method and system based on a dynamic knowledge graph, and relates to the technical field of artificial intelligence and financial risk control. The method aims at solving the technical problems that a traditional rule engine cannot process unstructured data, and a large generative model has logic illusion and privacy risks. The method comprises the following steps: constructing a rule knowledge graph containing parameterized logic constraints (such as time sequence, mutual exclusion and numerical accumulation); converting the unstructured business operation record into a feature vector by using a locally deployed semantic model; performing global sequence alignment optimization by constructing a global semantic consistency objective function, mapping operation records to a theoretical decision path derived from a graph, and eliminating local matching ambiguity; and finally, on the basis of a mapping result, calling deterministic parameters in map edge attributes to execute strict mathematical logic verification (including complex workday calculation, mutual exclusiveness check and the like). According to the method, a'nerve-symbol 'dual architecture is adopted, accurate understanding of fuzzy semantics is achieved, logic verification certainty and data privacy security are guaranteed, and the method has a hidden process discovery capability.

The invention discloses a cross-border e-commerce commodity dynamic pricing method and system based on merchant multi-dimensional data, and relates to the technical field of cross-border e-commerce pricing, and the method comprises the steps: S1, multi-dimensional data collection: collecting structured data and unstructured data in a merchant operation process; by collecting and analyzing the unstructured data, the defect that an existing pricing model only depends on the structured data is overcome, the user price attitude and commodity value evaluation are accurately captured, the price elasticity coefficient is corrected by fusing the unstructured data and the structured data, the accuracy of price sensitivity calculation is improved, and the price sensitivity calculation efficiency is improved. Through data feedback and strategy adjustment after price adjustment, a dynamic closed loop is formed, it is ensured that a pricing strategy can adapt to user feedback and market changes in real time, the problems that an existing pricing method is inaccurate in pricing and not timely in strategy optimization are effectively solved, and finally the dual effects of merchant profit space guarantee and commodity market competitiveness improvement are achieved.

The invention discloses a welding quality on-line detection method based on machine vision, and particularly relates to the field of welding quality detection.The welding quality on-line detection method comprises the steps that multi-dimensional basic data collection is conducted, the two-dimensional form, heat distribution, micro texture, three-dimensional topological data and welding process parameters of a welding seam are obtained, and a full-dimensional original data set with time-space synchronization is constructed; performing feature decoupling on the data, respectively constructing geometric morphology, thermal, texture and three-dimensional topological feature functions, and converting unstructured data into structured feature values; integrating multi-dimensional features through a nonlinear deep fusion model, introducing thermal probability mapping, texture attention weighting and a three-dimensional residual compensation mechanism, and outputting a weld comprehensive quality value; and finally, positioning dominant defect dimensions and identifying defect types in combination with a dynamic threshold model to finish quality grading judgment. Multi-dimensional accurate detection and dynamic grading of the welding quality are achieved, different scene requirements are met, and the detection efficiency and the judgment accuracy are improved.

Disclosed herein are system, method, and computer program product aspects for semantic search in a model-based prompt builder system. A system generates a search retriever object based on a search index comprising unstructured data. The search retriever object includes metadata specifying one or more details of a vector search operation to be performed on the search index. The system obtains search results by performing the vector search on the search index based on the one or more details of the vector search operation provided by the search retriever object and a search query. The system provides the search results to a prompt generator configured to use a model to generate a reply to a prompt request requiring the search results.

The invention relates to the technical field of passenger service, and discloses a construction method and device of a dynamic extension word bank, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous original knowledge data related to civil aviation passenger service, and converting the multi-source heterogeneous original knowledge data into a standard format text; processing the unstructured data in the standard format text based on the adaptive word segmentation model and the stop word list to obtain a plurality of candidate words; performing multi-dimensional corpus feature quantitative analysis on each candidate word to determine candidate keywords; and performing similarity calculation based on the entity word segmentation and the candidate keywords, determining effective candidate keywords, and adding the effective candidate keywords into the dynamic expansion word bank. By means of the method and device, the technical problems that in the prior art, a word bank used by a civil aviation service system is usually based on a static vocabulary or depends on manual compiling and updating, the static word bank cannot be updated in real time, the characteristics of the civil aviation field cannot be flexibly handled, and the maintenance cost is high due to manual updating and maintenance are solved.

The invention provides a retrieval enhancement generation method and system in the dual-carbon field, and relates to the field of data processing. According to the method, multi-source unstructured data in the dual-carbon field is collected, after data preprocessing is carried out, a multi-granularity query problem set is formed, a dual-carbon field knowledge base is obtained, and a dual-carbon field-oriented embedding model CEMBING and a reordering model CReranker are constructed. The CEMBING model adopts a semantic partitioning method and a joint training strategy, so that semantic information of a dual-carbon field text can be effectively captured; the CReranker model adopts a negative example mining strategy and a triple loss function, candidate documents can be accurately sorted, the problems of knowledge limitation and insufficient timeliness of LLMs in the application of the dialogue system in the dual-carbon field are effectively solved, and the accuracy and efficiency of retrieval enhancement generation of the dialogue system in the dual-carbon field are improved.

The invention discloses a meteorological operation and maintenance intelligent question-answering method based on multi-mode GraphRAG. The method comprises the following steps: S1, a meteorological operation and maintenance intelligent question-answering system divides input data into unstructured data and structured data and processes the unstructured data and the structured data respectively; s2, constructing a knowledge base system composed of a vector database, a relational database and a graph database; s3, analyzing the user query text and dividing question types; s4, selecting a corresponding retrieval strategy according to the question type and starting a processing flow, and finally processing a retrieval result through a fusion and rearrangement module to obtain a knowledge evidence set; and S5, the meteorological operation and maintenance intelligent question-answering system organizes the knowledge evidence set and the user questions into structured cues and submits the structured cues to the large language model for processing to generate structured answers. According to the method, optimization is carried out according to characteristics of the meteorological operation and maintenance field, professional terms and field specific knowledge can be effectively processed, and high-quality field professional services are provided.

The invention discloses a multi-modal medical data fusion and analysis method, which is applied to the technical field of medical informatization and comprises the following steps: acquiring multi-modal medical data; wherein the multi-modal medical data comprises structured data, unstructured data and semi-structured data generated in the diagnosis and treatment process of the patient; structural data features are extracted by adopting an optimized Z-score standardization formula; using sparse attention CNN to extract unstructured data features; deepening diagnosis and treatment semantic association by adopting a Transform encoder and entity embedding fusion form, and extracting semi-structured data features; multi-modal data are fused through closed-loop design of coding dimension reduction integration, decoding reconstruction verification, exclusive loss optimization and feature standardization output, and a feature relevance verification mechanism is fused; and matching the fused multi-modal data with a data analysis task, and performing task analysis and mining. According to the invention, efficient and reliable technical support is provided for medical digital transformation and precise diagnosis and treatment.

The invention discloses a power supply service intelligent management system, and relates to the technical field of intelligent power grids, and the system comprises a power grid holographic sensing module which obtains multi-dimensional unstructured data of a power system, extracts core features through cross-domain fusion analysis, and generates a power grid real-time operation state map; the equipment health twinning module is used for constructing an equipment health model in combination with a digital twinning technology and outputting an equipment fault early warning signal and a residual life prediction value; the customer energy efficiency service module is used for analyzing the association relationship and then generating a personalized energy efficiency optimization scheme and a power utilization suggestion; and the dynamic scheduling decision module is used for generating a power grid optimal scheduling strategy and an emergency response scheme. The power grid holographic sensing, digital twinning technology, dynamic scheduling, block chain and encryption technology are utilized, the power system is monitored in real time, equipment health is evaluated, faults are predicted, power utilization, energy conservation and emission reduction are optimized, data safety and stable operation of the system are ensured, and the cooperation efficiency of power enterprises and users is improved.

The invention is applied to the technical field of engineering cost, and discloses a BIM parameterized model-based cost fine control and pre-settlement auditing system, which comprises a data acquisition module, a calculation module, a calculation module and a calculation module, the data comprises building component geometric parameters, building material market real-time price data, cost quota standard data and historical project cost auditing data, the building component geometric parameters cover the size, the material and the number, and the building material market real-time price data comprises supplier quotation, freight and tax; the cost quota standard data comprises national and local quota sub-items and rate standards. According to the construction cost fine control and pre-settlement auditing system based on the BIM parameterized model, the data acquisition module is automatically connected with a multi-source database and performs standardized processing on unstructured data, and the data verification unit is combined to perform logic verification on data such as geometric parameters of building components, building material prices and the like, so that complexity and errors of traditional manual calculation are avoided.

The invention relates to the technical field of software, and discloses a nursing shift change system based on homologous heterogeneous data fusion and a large language model, and the system comprises a shift change generation module which comprises a first generation unit and a second generation unit, calling a large language model to generate global summary information of nursing shift change; and the second generation unit is used for calling a large language model to generate corresponding detailed handover information only based on the original data of the key event. And the report synthesis module is used for integrating the global summary information and the detailed handover information according to a preset structure to generate a nursing handover report. According to the method, the panoramic data view of the patient is constructed based on the unified time axis, so that structured data and unstructured data which are originally dispersed in different systems can be organized and called in the same semantic space, and a consistent data basis is provided for subsequent data analysis and processing.

The invention relates to the technical field of data management, in particular to a data weaving semantic integration method based on a semantic network and a knowledge graph, which comprises the following steps: acquiring multi-source heterogeneous data and preprocessing to obtain standardized data, the multi-source heterogeneous data comprises structured data, semi-structured data and unstructured data from different business scenes; performing rule injection on the standardized data to generate semantic web ontology data; extracting entities and attribute values thereof in the standardized data to generate structured knowledge graph data of the instance layer; and establishing a semantic association mapping network of the semantic network ontology data and the structured knowledge graph data, and generating a semantic integration result of data weaving. According to the method, the basic differences of the multi-source data in the aspects of formats, codes and the like are eliminated, a unified semantic standard is provided, and conflicts caused by non-unified semantics are reduced.

The invention provides a construction log generation method based on an intelligent agent and a large model. The method comprises the following steps: 1, constructing a multi-source heterogeneous data acquisition layer: automatically acquiring required original data from a built-in interface, an external application programming interface, Internet of Things equipment and a manual input channel through the intelligent agent; 2, establishing a data preprocessing and structuring layer: processing unstructured data by using an AI technology, extracting key information, and packaging the key information into a standardized data unit; 3, constructing a domain model of precise construction terms and log styles; 4, generating a log first draft by the large model; 5, man-machine collaboration auditing and optimizing, wherein a man-machine collaboration interface is provided for engineers to audit, correct and confirm the generated log first draft; and 6, based on manual feedback, establishing an optimization closed loop, and carrying out continuous iterative optimization on data acquisition, a preprocessing rule, a cue word template or a large model. According to the method, full-automatic acquisition is realized, various data sources are automatically docked through the intelligent agent, and manpower is thoroughly liberated.

The invention discloses a port field knowledge graph automatic construction method based on cooperation of multiple intelligent Agents, and the method comprises the steps: processing structured data, semi-structured data and non-structured data of a port field through intelligent Agents with an autonomous decision-making capability, and achieving the entity recognition, relation extraction and knowledge fusion; a plurality of intelligent Agents with the cooperation function are used for executing knowledge discovery, new knowledge verification, conflict detection, knowledge fusion and graph updating tasks respectively, cooperative communication among the intelligent Agents is achieved through a message queue, and dynamic evolution of a knowledge graph is completed; processing the knowledge graph by adopting a customized knowledge graph embedding method and a semantic fusion algorithm designed for professional terms and knowledge structures in the port field; and storing the processed knowledge graph data based on a distributed architecture, wherein the distributed architecture supports high concurrent processing and real-time response. According to the method, the port domain knowledge graph can be efficiently and automatically constructed.

The invention discloses a sensitive data identification method, system and device based on rule matching and LLM deep semantic analysis, a medium and a product, and relates to the field of artificial intelligence, the method comprises the following steps: preprocessing data in an information data set; the information data set comprises multi-industry corpora, personal information and safety knowledge; the multi-industry corpora cover structured data format features and unstructured text semantic scenes; according to the preprocessed data, a LoRA technology is adopted to carry out field adaptation fine tuning on the large language model; the large language model comprises a rule matching layer and an LLM depth recognition layer; the rule matching layer screens and identifies the structured data; the LLM depth recognition layer recognizes unstructured data and implicit sensitive data; performing semantic recognition on the to-be-tested data according to the fine-tuned large language model, and outputting sensitive data; the sensitive data comprises information such as an identity card number, a home address and a mobile phone number, and the sensitive data can be accurately recognized.

The invention relates to the field of lubricating equipment management, and particularly provides a lubricating equipment health management method and system, a terminal and a medium, and the method comprises the steps: collecting multi-modal data, time sequence structured data including vibration and the like, non-structured data including texts and the like, and oil detection data of lubricating equipment; carrying out standardization processing on the data to obtain multi-modal features; aligning data through timestamp calibration and space mapping, and constructing a multi-modal heterogeneous graph containing multi-modal nodes and multi-type edges; generating a uniform equipment state representation vector through cross-modal diagram attention calculation; inputting a pre-trained neural network model, and outputting the fault type and probability, the remaining service life and the health score; and receiving a query through a natural language interface based on the result, and generating a visual report and / or an operation and maintenance work order. According to the invention, precise identification and early warning of lubricating equipment faults are realized, and the accuracy and operation and maintenance efficiency of equipment health management are improved.

Methods, apparatuses, system, devices, and computer program products for AI-driven data aggregation and analysis for entity scoring and assessment are disclosed. In a particular embodiment, a controller generates an entity profile for an entity by iteratively retrieving data related to an entity, the data including structured data and unstructured data, determining a structure for the unstructured data by applying the retrieved data to the AI model, the structure including a plurality of fields, and populating one or more fields of the entity profile with a respective value that is extracted from the retrieved data using the AI model. The controller stores the entity profile in a database comprising a plurality of entity profiles corresponding to different entities. The controller generates, using the AI model, a score for the entity in relation to an attribute based on an AI-driven analysis of the plurality of entity profiles.

The invention relates to the technical field of enterprise management, in particular to a dynamic performance management system based on artificial intelligence and an implementation method. The system comprises a data acquisition and processing module used for acquiring structured and unstructured data from a plurality of data sources; the knowledge graph construction module is used for constructing a knowledge graph representing an organization semantic model by utilizing the structured and unstructured data; the performance indicator construction and adjustment module is used for constructing and adjusting performance indicators of the organization by utilizing a reinforcement learning agent based on the knowledge graph; and the visualization module is used for providing a user interface to display the performance indicators and the analysis results. According to the method, KPI strategic correlation is ensured by constructing the organization knowledge graph, closed-loop adaptive optimization of performance indicators is realized by introducing reinforcement learning, and the organization work gravity center can be guided to automatically align with the strategic direction of dynamic change, so that the strategic executive force and agility of the organization are remarkably improved.

The invention relates to the field of task automatic decision making, in particular to a task disassembly analysis decision making system and method based on a knowledge graph, and aims to solve the problems that task decision making depends on a structured database, so that decision making lacks deep association, and task target disassembly is easily influenced by personal experience. A data set establishment module specially processes unstructured data, actively extracts data entities, records association relationships, finally generates a dynamic graph, splits a task target into a plurality of sub-task chains, automatically generates a plurality of execution schemes to form an alternative scheme pool, and finally generates an alternative scheme pool through real-time environment data and resource constraint data. It is ensured that evaluation is carried out under the dynamic condition close to reality, the feasibility and accuracy of decision making are improved, deduction is carried out through the graph neural network, chain reactions and potential conflicts possibly occurring in the scheme execution process can be simulated, quantitative risk coefficients are output, risk evaluation is made to be quantitative from qualitative, and the risk evaluation efficiency is improved. And finally, an optimal execution scheme is obtained.

The invention relates to the technical field of image data processing, in particular to an oral cavity knowledge graph construction method and device, equipment and a storage medium. Preprocessing the oral CT original sectional image data based on a preset adaptive threshold method and a preset rigid registration algorithm to obtain standard image data; performing feature extraction on the standard image data based on a preset deep learning model and a preset principal component analysis method to obtain a structured feature vector; constructing the structured feature vector, the structured data and the unstructured data according to a preset entity linking method, a preset inference rule and a preset graph neural network model to obtain an optimized knowledge graph; an optimized knowledge graph is constructed through CT image preprocessing, deep learning feature extraction and multi-source data fusion, an image-to-structured knowledge conversion link is realized, and a comprehensive, reliable and convenient knowledge data support is provided for precise diagnosis and treatment of oral diseases.

The invention discloses a financial risk prediction method based on multi-modal dynamic alignment and a graph neural network. The method comprises the following steps: firstly, acquiring and processing structured data and unstructured data in the financial field to obtain preliminary feature representation; then, structural features are enhanced through event attention enhancement and periodic coding, unstructured features are enhanced and deepened through domain adaptability, and dynamic alignment and fusion are performed on the two enhanced feature representations by using a bidirectional cross attention mechanism to generate unified fusion feature representations; then, dynamically updating the edge weight of the financial knowledge graph based on the fusion feature representation; and finally, simulating a nonlinear propagation process of the risk on the dynamic map by using a map neural network, predicting a risk state of each mechanism, and calculating a systematic risk index. According to the method, the accuracy, timeliness and interpretability of financial risk prediction are remarkably improved.

Described is a system that receives data from a variety of external data repositories and identifies unstructured data within the received content. The unstructured data is processed to generate textual representations. A chat message is displayed in a user interface, prompting the first user to submit a query. Upon receiving the user's query, the system generates a modified version of the query and identifies portions of the textual representations. A content block is then generated from these portions and input into a machine learning model trained to generate responses using content blocks. The system generates a response to the user's query and displays the response within the user interface.

The invention provides a deep processing method and system for a digital delivery result, and relates to the technical field of data processing. A delivery result file set is disassembled and recombined into a unified object main data set and a three-dimensional entity set; each object attribute record and each object relation record have a unified object identifier, a source identifier and evidence positioning information at the same time, and multiple sets of coding calibers of the same object can be gathered to the same unified object identifier; refined model replacement is executed by taking a unified object identifier as an anchor point in three-dimensional treatment, continuity of a composition relationship and a connection relationship is kept, and after project handover and commissioning, online and field inconsistency found by operation and maintenance personnel is converted into a revision chain capable of being rechecked, judged and audited, so that the operation and maintenance efficiency is improved. Therefore, the problems that the unstructured data is difficult to reuse, the coding calibers are different, the object relationship is easy to lose and the data in the operation and maintenance period is not credible are solved.

This application is directed to constructing a knowledge graph using generative artificial intelligence. A system can include one or more processors coupled with memory to identify a plurality of items of unstructured data. The system can provide, for one or more generative artificial intelligence models, a first prompt to cause the models to output a plurality of first level categories of a hierarchical data structure for the items. The system can receive the first level categories, each corresponding to a subset of the items grouped by semantic similarity, and evaluate each category according to taxonomy criteria. The system can provide a second prompt to generate second level categories for each first level category, receive the second level categories, and construct a knowledge graph data structure linking the categories and their respective subsets to relate each item of unstructured data with corresponding categories according to the hierarchical data structure.