1718 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 power plant operation and maintenance knowledge intelligent query method based on a large language model and an RAG technology. The method comprises the following steps: constructing a power plant operation and maintenance knowledge vector library covering structured, semi-structured and unstructured data; receiving a natural language question of a user, inputting an improved instruction to align a preprocessor, and generating a question semantic vector and an intention tag; relevant knowledge fragments are retrieved and sorted through a semantic matching retriever in combination with the intention labels; constructing a large language model cue word structure based on the retrieval result and the original question, generating candidate answers and recording a reference path; and finally, performing term specification and consistency verification according to the expert rule base, and outputting a structured and traceable final answer. According to the invention, the improved RAG technology is fused to realize intelligent query of the operation and maintenance knowledge of the power plant.

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention discloses an informatization project management system based on big data analysis, which belongs to the field of big data and comprises a data acquisition module, a time sequence modeling module, a task coupling analysis module, a risk clustering identification module, a resource allocation prediction module and the like. The data acquisition module asynchronously and parallelly acquires structured and unstructured data and uniformly encodes the structured and unstructured data; the time sequence modeling module constructs a multi-dimensional time sequence based on an autoregressive residual network; the task coupling analysis module fuses the task trajectory and the dependency relationship to generate a task influence directed graph; the risk clustering identification module identifies risks through variational graph auto-encoder mapping; the resource allocation prediction module constructs a dynamic resource priority based on a graph attention mechanism; a progress deviation traceability module identifies a deviation causal chain; the knowledge graph decision-making module corrects resource priorities and path strategies in a cross-graph manner; and the project global control module dynamically adjusts key paths and resource configuration and performs closed-loop self-correction. The beneficial effect is that the intelligent level and the risk response capability of project management are improved.

The invention discloses an AI-based automatic financial information processing method, and relates to the field of financial automation, and the method comprises the steps: achieving the automatic collection and storage of structured and unstructured data through the access of enterprise multi-source financial data; systematic preprocessing is carried out on the collected multi-source heterogeneous financial data, and a unified and high-quality financial data set is constructed; based on natural language processing and a knowledge graph technology, performing text semantic understanding, transaction automatic classification, field standardization and label generation on the cleaned and integrated financial data; comprehensively quantifying enterprise operation and financial performance based on the structured transaction data and the semantic annotation result; based on historical financial indexes, establishing a multi-model architecture to predict key financial variables; and based on the structured data, the prediction result and the historical rule, identifying potential financial abnormity and risk behaviors, and realizing intelligent early warning. According to the method, the intelligence, the real-time performance and the accuracy of financial information processing can be remarkably improved.

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

The invention relates to the technical field of risk management and early warning, and discloses a risk early warning method comprising the following steps: collecting multi-source data including structured data, unstructured data and time series data; the invention provides a risk early warning system, which is used for performing cleaning, standardization processing and fusion on multi-source data to generate a unified data set for analysis, and comprises a data acquisition module used for acquiring the multi-source data including structured data, unstructured data and time sequence data; the data processing module is used for carrying out cleaning, standardization processing and fusion on the multi-source data and generating a unified data set, the electronic equipment comprises a memory, a processor and a communication module, and the processor implements the risk early warning method during execution. Through multi-source data fusion, a dynamic threshold model and deep learning analysis, the risk is accurately identified, the method adapts to the dynamic environment in real time, false alarms and missing alarms are reduced, and the long-term monitoring adaptability is improved.

A system and method for structuring and analyzing multimodal, unstructured data, as well as an article of manufacture and processor-readable storage medium implementing the same, may be used to provide a content creator with a tool to understand what actionable steps they could take to make a video perform well or go viral. Embodiments include a user interface simultaneously including a region for accepting a user query and a region that displays insight cards. The insight cards are generated based on retrieving a plurality of streaming contents, sorting the plurality of streaming contents into a viral set using one or more virality metrics, generating at least one token from the viral set, and performing a search of the viral set as a function of the at least one token to produce one or more results. The results are presented through the one or more insight cards in the user interface.

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

The invention relates to the technical field of construction engineering cost management, and discloses an intelligent engineering cost calculation system based on multi-source heterogeneous data fusion, and the system comprises a multi-source data collection module which is used for collecting structured data and unstructured data from a design file, a market database, a construction monitoring system, a contract document, and a historical project library; and the heterogeneous data fusion module is connected with the multi-source data acquisition module and analyzes the risk terms in the contract text by adopting a natural language processing technology. According to the invention, the multi-source data acquisition module is used for widely collecting data in multiple aspects of design, market, construction, contract and the like, the problems of data splitting and information isolated island in traditional cost management are solved, integration of multi-source heterogeneous data is realized, and the heterogeneous data fusion module utilizes advanced technologies of natural language processing, image recognition and the like, so that the cost management efficiency is improved. Contract texts and design drawings can be efficiently analyzed, the processing capacity of unstructured data is improved, and the error rate and omission rate of manual interpretation are reduced.

The invention relates to an enterprise-level simulation knowledge graph construction method based on multi-modal data integration, and belongs to the technical field of knowledge graphs. The method comprises the following steps: integrating structured data, semi-structured data and unstructured data through a multi-modal data warehouse; performing knowledge extraction on the semi-structured data and the non-structured data to obtain entities and relationships, and performing knowledge fusion; storing the fused entities and relationships by using a graph database, and constructing a simulation knowledge graph; a vector database is embedded in combination with a simulation knowledge graph, semantic extension search is realized through multi-modal joint search, similar cases are searched through a simulation result graph, and a simulation scheme comparison matrix is automatically generated. The multi-modal data is effectively integrated, the comprehensiveness and accuracy of knowledge graph construction are improved, more powerful, efficient and intelligent support is provided for simulation analysis of enterprises, and the enterprises can be assisted in rapidly making scientific decisions in complex and changeable business scenes.

The embodiment of the invention discloses a database multi-source heterogeneous data synchronization method and device.The method comprises the steps that a data change event is monitored through an RPA robot deployed in a server where a source database is located, and when change operation of structured data or unstructured data is detected, an initial data set is generated; performing fragmentation processing on the initial data set to generate a plurality of data fragmentation tasks, and allocating the data fragmentation tasks to distributed computing nodes to execute parallel format conversion operation so as to generate a target data set; detecting a task execution state of the distributed computing node, and when a generation completion event of a target data set is detected, activating a data synchronization channel and transmitting the target data set to a target database; and performing conflict detection and identification based on an RPA conflict processing strategy configured in the target database, when a primary key conflict or a uniqueness constraint conflict is identified, updating the target database based on preset conflict merging indication information, and generating a data synchronization log.

An unstructured data query-response pair generation system (generation system) populates a knowledge base of query-response pairs for queries of natural language content in unstructured data by prompting a first large language model (LLM) text extracted from the unstructured data. An unstructured data chatbot (chatbot) leverages the knowledge base by augmenting prompts to a second LLM responding to user queries for natural language content in the unstructured data with query-response pairs having queries that are semantically similar to the user queries. The knowledge base and LLMs are updated based on user feedback correcting responses, continually improving quality of the generation system and chatbot.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method and device based on factor weight optimization, equipment and a medium. Comprising the following steps: receiving structured data and unstructured data, and extracting a basic key factor set and monitoring object information in the data to generate a data feature set; executing multi-dimensional analysis and identification value determination based on the data feature set, and generating an analysis level and an identification value parameter; and performing weight correction processing on the basic key factor set, generating an optimized factor coefficient matrix, fusing the optimized factor coefficient matrix with associated equipment source data, and outputting a processing result. According to the method, structured and unstructured data are processed in a unified manner, so that the data integration capability is enhanced; multi-dimensional elements are extracted in combination with a pre-training model, and the key factor recognition effect is improved; and the analysis level and the identification value parameter are combined to drive weight correction and fuse the optimization matrix and the equipment source data to generate a processing result, so that the processing efficiency and the result accuracy are improved.

The invention relates to the technical field of project risk management, in particular to a project risk monitoring method and system based on a large language model, and aims to guide a language model to complete risk identification in a professional context by analyzing a natural language supervision request of a user, identifying a task field, matching a corresponding knowledge graph and a rule base, generating a reasoning configuration set and guiding the language model to complete risk identification in a professional context. Through a multi-modal fusion mechanism, unstructured data such as contract texts, drawing images and progress logs are coded in a unified mode, context modeling and rule reasoning of cross-modal information are achieved in combination with a large language model guided by a strategy, hidden risks needing image-text linkage judgment are effectively recognized, the analysis capacity for complex semantic association is improved, and the method is suitable for large-scale popularization and application. And furthermore, through a reinforcement learning mechanism, a supervision sample is constructed according to user feedback, a reward signal is generated, language model strategy parameters are optimized in real time, and continuous evolution and self-adaptive updating of a risk monitoring model are realized.

The invention discloses a cloud data anomaly detection and safety response system based on artificial intelligence, relates to the technical field of data processing, and solves the problems that firstly, an incremental compression algorithm is difficult to store and preprocess multi-source heterogeneous data; secondly, it is difficult to fuse statistical analysis and a deep learning model, locate outliers and analyze abnormal semantics in unstructured data, and then it is difficult to effectively predict a potential attack path; then, on the premise that data security and traceability are guaranteed, correlation analysis of cross-node anomalies is difficult to achieve so as to identify distributed attacks; and finally, an attack and defense confrontation model is difficult to construct for safety response, and the safety response effect is difficult to evaluate. According to the method, cloud environment data are processed through an adaptive probe cluster and the like, multiple models are fused to generate anomaly detection features and predict attack paths, and response and evaluation are carried out through reinforcement learning and digital twinning by means of cooperative detection such as federated learning and the like.

The invention discloses an urban safety risk assessment method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: constructing a multi-source data collection network, and obtaining data from a government department database, Internet of Things equipment, a social media platform and a traffic monitoring system in real time; preprocessing the collected multi-source data, wherein the preprocessing comprises data cleaning, format standardization, unstructured data semantic analysis and sentiment analysis; a cross-department data security sharing mechanism is established, and the traceability and security of data exchange are ensured through a block chain technology; constructing a dynamic risk assessment model, analyzing multi-source data relevance based on a deep learning algorithm, and dynamically adjusting the weight of each risk factor; and generating a visual risk assessment report, and pushing the visual risk assessment report to related departments in real time through an early warning system. According to the invention, through multi-technology fusion and a dynamic optimization mechanism, the accuracy, real-time performance and cooperation efficiency of urban safety risk assessment are significantly improved.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

Provided in the present invention are a method for training a natural language processing model, and a method for generating subsequent text of a dialogue. The method for training a natural language processing model comprises: acquiring multiple types of heterogeneous sample data and a pre-constructed natural language processing model, wherein the heterogeneous sample data includes structured data, unstructured data, a knowledge graph and expert experience data; performing data encoding fusion on the structured data, the unstructured data and the knowledge graph, so as to obtain encoding fused data; and on the basis of the encoding fused data and the expert experience data, training the pre-constructed natural language processing model, so as to obtain a trained natural language processing model. The present invention enables a knowledge graph to be placed in a model, not as an independent retrieval corpus, but as a method for knowledge enhancement, thereby improving the efficiency of a natural language processing model.

A system is adapted to automatically report the trustworthiness of an entity. The system includes a processor and a computer readable medium carrying instructions. The instructions include receiving unstructured data pertaining to an entity from public sources, and receiving structured data pertaining to the entity from at least two databases. The instructions also include merging the structured data and the unstructured data into a single document; splitting the single document into chunks; creating embeddings corresponding to the chunks; and storing the embeddings in a vector store. The instructions also include receiving a natural language user query regarding trustworthiness of the entity; converting the query to a query embedding; based on the query embedding and a similarity calculation, fetching a relevant embedding from the vector store; with a large language model (LLM), generating a query response regarding the trustworthiness of the entity; and communicating the query response to the user.

An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.

The invention discloses an enterprise multi-modal data intelligent processing system fused with an RAG technology and an intelligent processing method of the enterprise multi-modal data intelligent processing system, and relates to the technical field of enterprise-level multi-modal data intelligent processing. And the data processing module is configured to respectively process the structured data and the unstructured data through the dynamic heterogeneous encoder and output unified semantic representation by adopting a cross-modal adversarial alignment mechanism. According to the enterprise multi-modal data intelligent processing system fused with the RAG technology, the problem of enterprise multi-modal data splitting is solved through dynamic adversarial semantic alignment and a stepped fusion mechanism. Semantic gaps are eliminated through self-adaptive convergence of cross-modal features in a hidden space, deep association of heterogeneous data is achieved based on concept mapping and credibility arbitration of an ontology network, key information of unstructured data is accurately extracted and converted into structured knowledge, and the accuracy of cross-modal association analysis and decision reliability are improved.

The invention belongs to the technical field of artificial intelligence, and discloses an intelligent resource scheduling optimization method based on artificial intelligence. The method comprises the following specific steps: S1, multi-modal data acquisition and integration; s2, unstructured data analysis is introduced; s3, constructing and applying a dynamic demand prediction model; s4, carrying out cross-department resource priority modeling and optimization; s5, deploying a real-time elastic scheduling engine; s6, designing and interacting a man-machine collaborative decision-making interface; and S7, performing closed-loop feedback optimization and block chain evidence storage auditing. The system has three outstanding advantages that firstly, multi-modal data acquisition and integration help to analyze illness conditions, realize precise medical treatment and optimize operation; 2, cross-department resource priority modeling and optimization are carried out, sorting is carried out according to an algorithm, equipment is shared, ethics are embedded, treatment is guaranteed, and efficiency and satisfaction are improved; and thirdly, man-machine collaborative decision-making interface design and interaction, AR visualization, man-machine game, real-time feedback, management improvement, scheduling guarantee and coping capability enhancement are realized.

The invention discloses a question and answer system based on a semantic vectorization knowledge graph and approximate nearest neighbor clustering, and the system comprises a data uptake and preprocessing module which is used for the input and preliminary processing of an unstructured text; the knowledge graph construction module is used for constructing a dynamic knowledge graph according to the primarily processed data; the knowledge graph enhancement module is used for expanding representation of entities in the knowledge graph by adopting an information enhancer and converting each entity node and relation node in the knowledge graph into a high-dimensional semantic vector by using an embedded model; during work, the general knowledge graph can convert structured and unstructured data into semantic vectors, the semantic vectors are stored locally in the form of the knowledge graph, and cross-domain knowledge accurate retrieval is supported in combination with a fine-grained tree pruning technology. The knowledge integration model is based on an approximate nearest neighbor clustering method, so that integrated output of a plurality of large language models can be realized, and the hit rate of answers with relatively high credibility is effectively improved.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal data-based event analysis method, which comprises the following steps of: obtaining attribute data, monitoring data, environment data and biological characteristic data of a user, and performing standardization processing on the attribute data, the monitoring data, the environment data and the biological characteristic data; combining semantic analysis and structured fusion of the text records to form a unified input data set; and performing multi-modal fusion modeling on the unified input data set by using an intelligent decision model, generating a potential event analysis result of an individual state, generating a visual report containing potential event levels, main influence factors and intervention suggestions based on the result, and finally sending the report to a user terminal. According to the method, the multi-source structured data and the multi-source unstructured data are integrated, and the intelligent decision-making model is introduced to realize feature level fusion and semantic enhancement modeling, so that the accuracy and timeliness of individual state analysis are effectively improved, and the personalized expression of an analysis result is enhanced.

The invention discloses a financial risk assessment method based on big data, and relates to the technical field of finance, and the method comprises the following steps: S1, obtaining structured data, unstructured data and real-time streaming data of a target entity through a multi-source heterogeneous data collection module; s2, constructing an association relationship graph, and modeling risk propagation paths of a target entity and associated nodes thereof based on a graph neural network; s3, performing feature alignment and joint representation learning on the structured data, the unstructured text data and the time series data through a multi-modal data fusion module; and S4, based on the causal inference model, separating causal features and hybrid variables of the target entity risk event, generating causal risk factors, quantifying risk infection paths between nodes by setting an enterprise guarantee network and a supply chain relation graph dynamically constructed in a graph neural network, effectively identifying hidden risk nodes, and improving the risk assessment efficiency. And the chain reaction risk caused by the default of the associated enterprise is reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a multi-source data-based customer portrait generation method, device and equipment and a medium, and the method comprises the steps of obtaining structured data and unstructured data of a target customer in a data source; separately performing privacy desensitization processing on the structured data and the unstructured data to obtain structured desensitization data and unstructured desensitization data; extracting multi-modal features and time sequence features of the structured desensitization data and the unstructured desensitization data, and performing feature fusion on the multi-modal features and the time sequence features to obtain fusion features; constructing a target label set according to the fusion features, and analyzing real-time label weight distribution of the label set by using a federal learning model; and updating the target label set according to the real-time label weight distribution to obtain a real-time label set, and generating a real-time portrait according to the real-time label set. The accuracy of a customer portrait generation result can be improved.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

A method for AI-driven natural language search includes receiving a user query for one or more items from a user, processing the user query by searching against at least one relational database associated with the query, where the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items, generating one or more query results based on the processing of the user query, where the one or more results include at least one item identified from the plurality of items and a justification for explaining an irrelevance of the at least one item, and transmitting the one or more query results to a user device for presentation to the user.

The invention discloses an operation data risk intelligent identification and evaluation method based on a large language model. The method comprises the following steps: S1, collecting operation data of a data source; s2, preprocessing the unstructured data in the operation data; s3, performing semantic analysis on the unstructured data by using an improved BERT large language model, and identifying potential risk signals; s4, preprocessing the structured data, extracting feature information, and fusing a risk signal identification result with the feature information; s5, risk assessment is carried out based on an improved XGBoost algorithm; s6, performing quantitative evaluation on the identified risk factors; s7, when the operation data is updated, performing real-time risk identification and evaluation on the new operation data; and S8, when the risk assessment result triggers a set early warning threshold value, generating risk early warning. According to the method, the improved BERT model and the XGBoost algorithm are combined, industrial data risk identification and evaluation are realized, and the method has the advantages of high accuracy, high real-time performance and high automation degree.