1204 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.

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 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 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 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 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.

Techniques for operating a chatbot system for enterprise-level conversational agents are disclosed. These techniques are performed by an application or cloud service executing on one or more computing devices. An enterprise system can deploy conversational agents onto user devices to run as chat interfaces for logging analytics question-answering. One example application or cloud service may be a multi-model chat mechanism configured to support these chat interfaces with backend functionality. In response to an incoming question, the chat mechanism first consolidates the question with any conversation history and then, classifies the user's question as either a question regarding unstructured document data, a question regarding structured log data, or a hybrid question. Based on the classification, the chat mechanism can generate a proper large language model (LLM) response.

A system for extracting a number of data elements from one or more unstructured data sources. The system may separate the text from the tables in a document, such that only the table data may be sent to the large language model (LLM), when the LLM only needs to review the table data. The system generates chunks from the document. The system associates unique identifiers with each chunk to provide traceability. The system identifies relevant chunks from the documents and includes the relevant chunks with a request to extract the data elements in a prompt to the LLM. The system also includes a request for the LLM to report the chunks used during extraction of the data elements. The reported chunks are stored with the extracted data for verification, auditing, and error control.

The invention discloses an employment information matching method and system based on data analysis, and particularly relates to the field of employment matching, and the method comprises the steps: collecting structured and unstructured data, and carrying out the semantic feature extraction through employing a BERT model and BiLSTM-CRF; in the preprocessing stage, entity standardization is realized through a knowledge graph, and a job seeker portrait and post model including a skill matrix and an occupational development trajectory is constructed; in the feature engineering stage, extracting four core features of skill matching degree, salary expectation integrating degree, commuting tolerance and occupational development goodness of fit; the salary integrating degree is quantitatively evaluated through a bidirectional tolerance model, a random forest model with time decay is used for dynamic weight, feature weight is generated based on historical successful cases, and a real-time feedback mechanism is introduced to adjust a weight coefficient; finally, the matching degree function fuses the weighted features and the industry trend factors, and the model is continuously optimized through a three-level updating mechanism.

The invention discloses an enterprise data link management and value management method and system, and the method comprises the steps: carrying out the real-time capturing of multi-source heterogeneous data of all business systems in an enterprise through a distributed data collection engine, and recognizing the types of original data in different formats through a preset data source adapter; if structured data is detected, a relational database connector is adopted for extraction, and if the structured data is recognized as unstructured data, a document analysis module is started for content extraction, and an initial data set containing metadata tags is obtained; performing format conversion and field mapping on the initial data set according to a pre-established data standardization rule base, eliminating duplicate records and abnormal values through a data cleaning algorithm, performing automatic evaluation on a data quality grade by adopting a naive Bayes classifier, if a data quality score is lower than a preset threshold value, triggering a data recovery process, and if the data quality score is lower than the preset threshold value, performing data recovery. And standardized data meeting a unified standard is obtained. The normativity and value utilization efficiency of data management are effectively improved.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention provides a supply chain purchase bid invitation project multi-mode intelligent auditing method, and belongs to the technical field of electronic purchase. Comprising the following steps: defining a review rule system comprising rule contents, review points and review logic; in the bidding stage, bidding files and quotation information uploaded by suppliers are received, and after bid opening, bid invitation files, bidding files of entered suppliers and corresponding review rule systems are input into a multi-mode large model server; performing unstructured data analysis on the bidding document and the bidding document by using a multi-modal large model in combination with an OCR (Optical Character Recognition) technology, and identifying and matching response contents in the bidding document through intention; pushing the extracted content to a corresponding business auditing agent, and performing judgment according to an auditing point and auditing logic; and summarizing the review results, and generating an intelligent review report containing the number of conformity items, the number of non-conformity items, conclusions of the review points and judgment bases. The examination efficiency and accuracy are improved, and human errors and compliance risks are reduced.

The invention discloses a fireman occupational health risk dynamic prediction method based on multi-modal data fusion, relates to the technical field of occupational health risk assessment, and aims to solve the problems of information loss, difficulty in capturing cross-modal complex dependence, lack of interpretability of prediction results and the like when unstructured data is processed in the prior art. The method comprises the following steps: intelligently analyzing a multi-modal document, and analyzing unstructured physical examination information into structured data; performing semantic standardization and knowledge graph dual verification of domain knowledge enhancement; the time-space-static cross-modal attention deep fusion network is used for modeling physiological, dynamic and situational data; and carrying out interpretable risk prediction and attribution. By adopting the technical scheme, the method can realize early, accurate, dynamic and high-credibility prediction of the occupational health risk of the firefighter, provides quantitative attribution, and improves the transparency and application value of the system.

A Retrieval-Augmented Generation (“RAG”) framework may include an RAG vector data store with information about vector embeddings. An RAG pre-processing platform may access unstructured data and perform vector embedding to generate a vector embedding for unstructured data to be stored in the RAG vector data store. The RAG pre-processing platform may also access structured data from the knowledge base and create a summary and metadata about the structured data to be stored in the RAG vector data store. An RAG retriever platform may receive a user prompt from a user, perform vector embedding, and retrieve context-relevant information for unstructured and structured data by searching for similar embeddings in the RAG vector data store. An RAG reader platform may combine the context-relevant information with the user prompt and an RAG prompt to create a LLM prompt. A context-aware response is then output to the user.

Disclosed are systems and methods for maintaining data integrity (e.g., of a database, a data lake, etc.). In some embodiments, one or more processing circuits identify an update, insertion, or deletion of unstructured data stored in an upstream source. The processing circuits determine a new schema of the unstructured data using an inferring function and further determine a divergence between the new schema and a previous schema of the unstructured data based on comparing the new schema to the previous schema of the unstructured data. In an example, the processing circuits store the structured data in a database. The database includes an established data channel between the database and a downstream source, and the structured data is a unique representation within the database of the update, insertion, or deletion of the unstructured data.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention discloses a data governance agent system and an implementation method, and relates to the field of data processing, and the system comprises a data analysis module, a data storage module, a user response module, a quality monitoring and feedback module and a field large language model module. The data analysis module performs unified analysis modeling on structured and unstructured data; the data storage module adopts a graph database and a vector database to store knowledge data; the user response module supports natural language interaction and intelligent question and answer; the quality monitoring and feedback module optimizes data quality through closed-loop feedback; and the field large language model module constructs an optimization model adaptive to the vertical field. Through multi-modal analysis, knowledge graph construction, double-library collaborative storage and model dynamic optimization, a data barrier is broken through, manual dependence is reduced, the data treatment efficiency, quality and intelligent level are improved, and the method is suitable for specialized data treatment requirements of industries such as manufacturing and finance.

The browser tab page data storage method supporting data encryption comprises the steps that browser tab page interaction behaviors are monitored in real time to generate a dynamic factor, and a session key dynamically changing along with the life cycle of a tab page is generated in combination with a process ID of the tab page; based on a self-adaptive encryption strategy, respectively encrypting structured data and unstructured data of the label page; wherein the structured data are encrypted according to field sensitivity levels, and the unstructured data are encrypted through mapping binding of a secret key and the data; the encrypted data and a timestamp voucher of the session key are stored in an associated mode, decryption operation is only allowed to be completed in a label page process memory generating the session key, and the session key is not stored in a landing mode; and when the label page is closed, synchronously clearing the decryption data in the session key and the process memory. According to the method and the device, the defects that the dynamic generation of the key cannot be realized and the key cannot fall to the ground at present are overcome.

The invention relates to a structured text analysis method and system based on title recognition and hierarchical abstracts, and the method comprises the steps: receiving a text, and obtaining sample data of a training language model; the vector database can perform vector dimension adjustment on the data of the stored text. The vector database takes vectors as basic storage units, and converts unstructured data into high-dimensional vectors through an embedding technology. And calling the data storage type information of the title text data set to determine the data storage type of the title text data set matched with the text storage mode. And obtaining a structure type of sample data of each text by utilizing a semantic segmentation script. And segmenting the document according to the identified chapter titles, and subdividing each chapter into a plurality of logic paragraphs to form structured text block units. The multi-level abstract generation model matched with the target model can fully mine level information and semantic association in the document. Therefore, the effect of analyzing the structured text by the intelligent question-answering system is improved.

A system and method for field service assistance for telecommunications operations are described, which utilize a data acquisition module configured to receive multimodal data inputs including structured and unstructured data from field operations. A preprocessing module normalizes the multimodal data inputs to generate pre-processed data. A vectorization module transforms the pre-processed data into numerical vector representations using domain-specific embedding models trained on telecom equipment data, implementing convolutional neural networks for image feature extraction and transformer-based encoders for text vectorization. A contextual retrieval module retrieves contextually relevant historical data from a vector database by computing similarity metrics between current job vectors and stored job completion vectors. A response generation module processes the numerical vector representations and retrieved contextual data using an evolutionary algorithm engine to generate structured job summaries and real-time field recommendations.

The invention relates to the field of resource association analysis, and discloses a knowledge graph-based archive decision support and association analysis method, which comprises the following steps of: firstly, acquiring structured and unstructured archive data; preprocessing is carried out through data cleaning, duplicate removal and format conversion, semantic disambiguation is carried out on historical fuzzy words in unstructured data, and entity normalization processing is carried out on multi-category names of the same character; secondly, based on the preprocessed structured data, entities, relationships and attributes are extracted, and entity association evidences are supplemented through a cross-modal alignment technology; performing consistency verification, and storing the verified entity relationship network into a graph database to form an archive knowledge graph; then, in combination with a graph traversal and time sequence analysis algorithm, a hidden association path is mined, and trend prediction is carried out; and finally, generating a decision evidence chain according to an association mining result. And establishing a knowledge graph dynamic updating mechanism, and automatically reconstructing the entity association network when a file or a historical research result is newly added.

The invention discloses a retrieval enhancement generation method for multi-source heterogeneous data fusion, and relates to the technical field of cross-modal retrieval generation, and the method comprises the following steps: collecting structured data and unstructured data, respectively carrying out standardization processing and semantic cleaning, and outputting a multi-source heterogeneous data set; constructing double-view knowledge block representation by using a multi-source heterogeneous data set, and respectively generating a retrieval view and a generation view; carrying out weight adjustment and fusion on the retrieval view and the generation view according to the current query content, and outputting a fusion semantic vector; performing approximate vector matching retrieval on the knowledge fragments by using the fused semantic vector, performing semantic reordering on a retrieval result according to semantic correlation, and outputting an ordered knowledge fragment set; according to the method, the context adaptability and semantic expression capability of the generated input are enhanced, so that the accuracy of knowledge retrieval and the quality and pertinence of text generation are remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention discloses an asset configuration recommendation system, method and device based on multi-modal data fusion, relates to the technical field of asset configuration, and can be applied to financial science and technology business scenarios, and the method comprises the following steps: obtaining multi-source heterogeneous data including structured data and unstructured data; preprocessing the multi-source heterogeneous data and generating an environment feature vector; designing a reward function based on the environment feature vector, utilizing the reward function to drive a reinforcement learning framework to simulate an asset configuration decision process, and in the simulation process of investment decision, iteratively optimizing strategy parameters through reward feedback to realize dynamic configuration of an asset configuration strategy; and according to the personalized parameters and the transaction behavior characteristics of the target object, carrying out weight calibration on an asset configuration strategy output by the reinforcement learning strategy module, and generating a customized asset configuration recommendation result for the target object. According to the invention, dynamic updating of asset configuration can be realized, and the accuracy and adaptability of asset configuration are improved.

The invention discloses a knowledge base knowledge association fusion method based on a knowledge graph, and the method comprises the steps: integrating structured, semi-structured and non-structured data through a cross-modal alignment technology, and constructing a multi-source heterogeneous data association network of a unified semantic space; newly added external data are fused to a multi-source heterogeneous data association network in real time by using a dynamic attention mechanism, and entity conflicts are eliminated by combining rule reasoning and a machine learning model; hidden association among entities in the multi-source heterogeneous data association network is mined based on the graph neural network, and a knowledge graph logic chain is complemented; based on the knowledge graph, intelligent question and answer and risk assessment decision scenes are supported through a hybrid retrieval architecture and an inference engine; and automatically updating and associating the knowledge base of the network extension knowledge graph by adopting an incremental learning technology. Natural language questions and answers are supported, accurate answers are generated through knowledge reasoning, and user experience is remarkably enhanced.

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.