77 results about "Association mining" patented technology

The invention discloses a digital archive online management system based on big data. The digital archive online management system comprises a digital acquisition layer which is used for carrying out semantic perception and structured extraction on heterogeneous archives; and the intelligent classification layer is used for mapping the collected archive entities, attributes and relationships to a dynamically expanded domain knowledge graph based on a knowledge graph construction technology and a graph neural network, realizing association mining and dynamic classification of cross-modal archives through node embedding and link prediction, adapting to evolution requirements of archive themes in combination with a weak supervised learning mechanism, and realizing dynamic classification of the cross-modal archives. A classification system with causal interpretation is formed; the storage retrieval layer is used for encoding the generation time, the space trajectory and the event causal chain of the archive into space-time causal metadata; the archive utilization layer is used for actively pushing associated archives and generating a personalized analysis report by analyzing user behavior preferences and scene requirements; and the backup layer is used for dynamically sensing the threat type and calling an evolutionary algorithm to adjust the backup frequency, the storage position and the recovery path.

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 method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

The invention discloses a multi-modal data processing method and device based on a large model, equipment and a medium. Performing feature extraction and association mining on the multi-modal data to obtain multi-modal data features; according to a retrieval sequence of the multiple text matching methods, sequentially using the multiple text matching methods to perform text retrieval on the multi-modal data features in a pre-constructed knowledge base to obtain a first associated sub-graph; based on the semantic similarity between the multi-modal data features and multi-modal data in a pre-constructed knowledge base and a similarity threshold value, determining a second associated sub-graph; performing standard evaluation on the plurality of candidate results in the first associated sub-graph and the second associated sub-graph, and sorting the plurality of candidate results according to an evaluation result to obtain a retrieval result; inputting the retrieval result and the multi-modal data features into a large model for information analysis to obtain prompt information and a target tool; and sending the prompt information and the target tool to the terminal, and receiving a data processing result of the terminal.

The invention relates to a network fault root cause positioning system based on multi-modal learning and causal inference, and belongs to the technical field of network fault diagnosis and positioning. The system comprises a data processing and association mining module, an intelligent fault diagnosis and evaluation module, a root cause positioning module and a system optimization module which are connected in sequence. The data processing module collects data through edge nodes, constructs a hierarchical knowledge graph and outputs a feature matrix; the diagnosis module performs fault detection by adopting a multi-modal model of a fusion graph neural network, integrates a small sample learning mechanism and outputs a fault event with confidence; the positioning module constructs a causal graph based on a knowledge graph, fuses multi-source evidences and realizes root cause tracing through a random walk algorithm; and the optimization module adjusts diagnosis parameters by utilizing reinforcement learning, expands a sample set based on the generative adversarial network, and realizes continuous optimization of the model through an automatic assembly line. Closed-loop self-optimization from fault sensing to root cause positioning is realized, and the network fault management capability is improved.

The embodiment of the invention provides a three-dimensional point cloud data analysis method and system based on artificial intelligence, and relates to the technical field of three-dimensional point cloud data analys.The method comprises the steps that firstly, a three-dimensional point cloud data set containing space coordinates, a collection time sequence and other space-time attributes is obtained, and then a three-dimensional point cloud fragment space-time association evolution structure is constructed; the spatial-temporal association degree is determined and dynamically updated through a spatial-temporal neighborhood cooperative propagation mechanism, then multi-level cross-fragment feature interaction enhancement processing is executed, multi-dimensional spatial-temporal features are extracted and fused to generate an enhanced cross-fragment feature interaction vector, then a pre-training model is called, multiple rounds of global association mining are performed through a spatial-temporal multi-layer interaction module, and the spatial-temporal association degree is obtained. And finally, the space-time structure association rule is analyzed to generate an application instruction, and the application instruction is sent to the target processing terminal, so that the three-dimensional point cloud data analysis efficiency is improved.

The invention discloses a multi-modal data association relationship mining method based on potential feature perception, which belongs to the field of multi-modal data analysis and feature association modeling in artificial intelligence and data mining technologies, and comprises the steps of multi-modal data acquisition and preprocessing, multi-modal feature mining based on potential semantic alignment, multi-modal data analysis and feature association modeling. Performing multi-stage feature fusion and time sequence association representation learning, and constructing a cross-modal semantic association graph. According to the method, under the conditions of noise interference, unbalanced sample distribution and weak semantic association of the multi-modal data, robust fusion and semantic consistency expression of the multi-modal features in a potential space can be realized through adaptive anomaly correction and a multi-level feature alignment mechanism, mismatching caused by noise pollution and shallow association is avoided, and the robustness of the multi-modal features is improved. And accurate mining of the high-order potential semantic relationship is realized. Meanwhile, the semantic edge and the time sequence edge can be subjected to separation modeling according to the internal structure of the multi-modal data under the conditions of modal isomerism and time sequence overlapping, and meanwhile, a unified cross-modal association graph is constructed. Furthermore, in order to improve the accuracy of time sequence relation modeling, time sequence comparative learning and dynamic consistency constraint are utilized, effective distinguishing between real time sequence dependence and multi-mode repeated representation is achieved, and the precision and robustness of multi-mode correlation analysis are remarkably improved.

The invention relates to the technical field of artificial intelligence, and particularly provides an answer generation method, system and device based on an RAG technology and a storage medium, and the method comprises the steps: receiving a question text input by a user; based on the question text, performing semantic retrieval in a pre-constructed knowledge base, calculating semantic similarity between the question text and knowledge fragments in the knowledge base, and retrieving target knowledge fragments of which the semantic similarity meets a preset threshold value; performing splicing processing on the question text and the target knowledge fragment to form an input prompt word; and inputting the input prompt word into a pre-trained generative large language model, generating a corresponding answer text by the generative large language model, and outputting the answer text. According to the method, three breakthroughs of semantic understanding, association mining and result optimization are realized, and multi-modal retrieval is supported.

The invention relates to a space-time alignment and semantic association modeling method for cross-platform geographic information data, and belongs to the field of big data and natural language processing. The method comprises the following steps: step 1, carrying out space-time uncertainty modeling and probabilistic representation, and quantifying time and space uncertainty of multi-source data; 2, combining an alignment decision mechanism with adaptive learning to complete multi-evidence intelligent flexible alignment fusion; step 3, carrying out deep semantic analysis and real-time association discovery based on multi-modal semantic understanding and association mining; and 4, dynamically updating and reasoning the knowledge graph, and performing real-time analysis and real-time reasoning. According to the method, for a sudden search task, a new data source can be rapidly integrated, the knowledge graph is enriched, global situation visual control is achieved, multilevel reasoning based on the knowledge graph is achieved, efficient and accurate geographic information data intelligence is supported, and the task response efficiency is improved.

The invention discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, and relates to the technical field of reinforcement learning, and the method comprises the steps: inputting an initial data packet into a dynamic interaction model, carrying out explicit association mining through a semantic enhancement layer, optimizing a rule matching path through a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topological structure derivation on the dynamic commodity knowledge graph to generate a graph topological analysis report and a metadata list, and performing knowledge reasoning integration on the graph topological analysis report and the metadata list to generate an intelligent navigation engine; calling an intelligent navigation engine to execute multi-path semantic query and rule verification on the dynamic knowledge graph to generate a candidate classification scheme set; and performing multi-target collaborative optimization on the candidate classification scheme set to generate a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. According to the invention, through the dynamic interaction model and multi-target collaborative optimization, the rule adaptation efficiency in a complex scene is improved.

The embodiment of the invention provides a multi-modal data processing method and system based on a knowledge graph, and aims to solve the problems of semantic association deficiency and potential association mining difficulty in multi-source information processing. The method comprises the following steps: firstly, acquiring a multi-source information set containing different expression form information units and source identifiers, and performing entity mapping on the multi-source information set and a preset semantic association network to generate an information unit set with semantic association tags; performing cross-source feature association processing to obtain a comprehensive feature set; then, a semantic reasoning rule is called for association extension, and an extension feature set is generated; and finally, based on the extended feature set, generating an information processing result conforming to an application scene and feeding back the information processing result to a corresponding interface, thereby effectively integrating multi-source information and mining potential association.

The invention relates to the technical field of intelligent fire protection and big data analysis, and discloses a knowledge graph construction and association analysis method and system for fire protection supervision and communication global data. In order to solve the technical problems of heterogeneous fire-fighting data and single association, the invention provides a domain ontology-driven knowledge graph automatic construction and deep analysis method. The core innovation of the method is that a multi-source heterogeneous data fusion model is adopted to unify semantic representation of multi-modal data such as fire-fighting documents and Internet of Things perception; performing entity relationship joint extraction by utilizing the pre-training model, and efficiently constructing a fire-fighting knowledge graph of massive triples; and a graph neural network is introduced to carry out deep association mining and reasoning, a risk conduction path is quantitatively evaluated, and potential threats are predicted. According to the invention, discrete fire-fighting data is improved into a computable and reasonable knowledge system, and deep perception and intelligent decision-making of a fire-fighting safety situation are realized.

The invention discloses a search optimization system based on knowledge association of a knowledge base, which improves the correlation, comprehensiveness and depth of search results, and meets the high-quality knowledge retrieval requirements under the scenes of intelligent search, knowledge management and the like. According to the technical scheme, the system comprises a user interaction layer module used for achieving direct interaction between a user and the system, receiving query input and displaying optimized search results; the core function layer module is used for completing intention accurate analysis, deep association mining, quantitative extension retrieval and association dominant sorting by taking knowledge association as driving force; the knowledge association support layer module is used for providing underlying technical support of knowledge association mining, quantification and sorting; and the auxiliary data layer module is used for providing auxiliary data for field adaptation and user personalization so as to enhance the processing precision of the core function layer module.

The invention provides a knowledge graph construction method and system for intelligently fusing multi-modal data, and the system employs a layered architecture, and comprises a data input layer, a mode special extractor, a multi-modal aligner, a graph construction / mining device, and a multi-modal retrieval service layer. The modular design enables the system to have good expandability and maintainability, and each module can be independently developed and optimized. The characteristics of different modal data are fully utilized, and the efficiency and accuracy of feature extraction are improved. Through the accurate alignment processing, the accuracy of entities and relationships in the knowledge graph can be effectively improved, and the alignment error is reduced. The deep association relationship among the entities in the basic knowledge graph is mined through the configured deep association mining engine, so that potential knowledge hidden in data can be found, and the content of the knowledge graph is enriched. A user can obtain knowledge from multiple angles, different application requirements are met, and the utilization efficiency of the knowledge is improved.

The application discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, relates to the technical field of reinforcement learning, and comprises the following steps: inputting initial data packets into a dynamic interaction model, performing explicit association mining in a semantic enhancement layer, optimizing rule matching paths in a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topology structure derivation on the dynamic commodity knowledge graph, generating a graph topology analysis report and a metadata list, integrating knowledge reasoning on the graph topology analysis report and the metadata list, and generating an intelligent navigation engine; calling the intelligent navigation engine to perform multi-path semantic query and rule verification on the dynamic knowledge graph, generating a candidate classification scheme set; performing multi-objective collaborative optimization on the candidate classification scheme set, generating a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. The application improves the rule adaptation efficiency in complex scenarios through the dynamic interaction model and multi-objective collaborative optimization.

The application provides a kind of intelligent environmental damage questionnaire generation and semantic analysis method and system, comprising: obtaining structured environmental data through government department interface, combining AI search engine to capture network public data;Data is cleaned and normalized, and a standardized environmental database is constructed;Based on NLP technology, a semantic model is constructed, customized survey questions are dynamically generated according to the type of interviewee and event scene classifier, and question priority is optimized;Key entities are extracted from interview text using a pre-trained model;Through the environmental damage ontology library, the entity is mapped to the preset pollution category, and a dynamic causal correlation graph is generated;The integrity and consistency of the graph are verified using logical verification rules, and a structured survey report is automatically output.The application solves the defects of traditional questionnaire design rigidity, information fragmentation and insufficient implicit association mining, significantly improves the accuracy and efficiency of environmental damage investigation, and provides intelligent support for pollution tracing and responsibility definition.

The invention discloses a microbial data semantic processing method and system based on a graph model, and relates to the technical field of knowledge graph and microbial information processing, and the method comprises the steps: obtaining microbial data from a heterogeneous data source, and carrying out the standardization processing; constructing a domain ontology model conforming to microbial taxonomy specifications; implementing entity disambiguation and unified identifier mapping by utilizing a pre-training language model; a semantic relation triple is extracted in a mode of combining remote supervision and deep learning; constructing a microorganism knowledge attribute graph and storing the graph in a graph database; knowledge reasoning and function prediction are carried out by using a graph attention network; the method supports natural language semantic retrieval, and solves the problems that heterogeneous microorganism data integration is difficult, the naming disambiguation precision is insufficient and the function association mining capacity is limited.

The invention provides a bid inviting and purchasing penetration type supervision AI large model construction method and system fused with multi-modal data. The method belongs to the technical field of artificial intelligence and intelligent auditing. The method comprises the steps of performing multi-source data fusion processing on internal system data and external data related to bid invitation purchasing, generating a cross-domain associated data set, performing implicit associated feature mining, obtaining implicit associated data among bidders, deploying a graph neural network, and constructing an association relationship mining network. According to the industrial design review method based on artificial intelligence, through multi-source data fusion and implicit association mining, design details and potential association can be accurately captured, and the comprehensiveness and accuracy of design review are improved.

The invention relates to the technical field of material informatics, and discloses a method for constructing a knowledge graph based on a material database, which comprises the following steps: S1, collecting multi-source basic data and carrying out standardization processing, identifying a knowledge graph entity based on the standardized data and establishing entity attributes, defining an explicit association relationship between the entities and generating corresponding relationship data; s2, performing association mining on the basis of the explicit association relationship and the entity attributes to obtain a hidden association relationship used for representing potential relationships between entities; and S3, fusing the entity, the explicit association relationship and the hidden association relationship, and constructing a material database knowledge graph. According to the method, raw materials, a formula, a process, performance and an application scene serve as an entity framework, explicit modeling is conducted on the relation between entities, and a computable association network is constructed, so that research and development personnel can achieve cross-link retrieval, reasoning and positioning based on unified semantics, and the efficiency and consistency of formula development and performance analysis are improved.

The invention relates to the technical field of retrieval enhancement generation, and provides a retrieval enhancement method based on domain-term association mining and term closure expansion, and the method comprises the steps: obtaining an original term set covering a professional domain from a professional domain resource text; obtaining a terminology set; obtaining a terminology set screened by the reflection mechanism; obtaining a final terminology set; generating a plurality of term pairs; the screened term pairs are obtained; obtaining a term relation graph; receiving a natural language question input by a user, and constructing a term set corresponding to the natural language question; obtaining a term extension closure set; generating a candidate paragraph set; inputting the natural language question and the candidate paragraph set into a semantic reordering model, outputting a correlation score of each paragraph in the candidate paragraph set by the semantic reordering model, and taking the first K paragraphs with the highest correlation scores as a final support content set; and the large language model outputs an answer text corresponding to the natural language question.

This invention belongs to the field of text retrieval technology, specifically referring to an automatic retrieval system for digital economy texts. The system includes a basic task instance identification module, a key instance association mining module, a tag local score estimation module, a global tag configuration module, a noise tag configuration generation module, and a precise digital economy text retrieval application module. This solution uses two dedicated multilayer perceptrons to calculate semantic and syntactic dependency weights and fuse them to obtain the final association strength, achieving precise capture of digital economy instances and their associations. It combines global tag configuration to quantify the tightness of instance tag associations, and uses the noise tag configuration generation module to transform the global optimal tag configuration problem into a binary classification problem. By constructing a contrastive loss function and optimizing it through backpropagation, it efficiently solves the problem of computational explosion, achieving concept-based precise retrieval and intelligent decision support, providing users with an efficient digital economy information acquisition experience.

The application provides a data weaving method for multi-source heterogeneous data integration and management, which comprises the following steps: collecting data from accessed multi-source heterogeneous data sources to generate original multi-source heterogeneous data streams; performing standardization processing on the original multi-source heterogeneous data streams to generate standardized multi-source heterogeneous data sets; performing active content scanning processing on the standardized multi-source heterogeneous data sets to determine business metadata, and performing blood relationship tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to perform normalized constraint on the enhanced business metadata to obtain normalized business metadata eliminating semantic ambiguity across data sources, performing implicit association mining processing on the normalized business metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logical abstraction processing on distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention provides a graph neural network and rule engine combined hidden association mining and logical reasoning method and system, and the method comprises the steps: constructing a dynamic semantic relation graph containing alarm and resource nodes, and initializing an edge weight through employing a historical processing proportion; a gating coefficient based on area matching, resource states and strategy configuration is introduced into graph neural network propagation, and edge weights are dynamically modulated to excavate a hidden association path; converting the path into a business state transition sequence containing a field sequence, evidence intensity and a relative weight; and finally, fusing the evidence intensity and the relative weight through a rule engine to carry out logic judgment, and triggering resource scheduling. According to the method, deep fusion of data-driven mining and rule controllable execution is realized, and the accuracy, interpretability and engineering landing capability of linkage decision making are remarkably improved.

The invention discloses a power master station service-oriented fault positioning method and device, a medium and equipment, and belongs to the technical field of fault positioning. The method comprises the steps of obtaining multi-source data classified according to service topology levels; asynchronous decoupling is carried out on the data through a distributed message bus, and data cleaning and unified identification processing are carried out based on a power master station service rule and a service scene label system; inputting the processed data into a correlation analysis model trained based on a business dependency relationship and historical fault data, and carrying out cross-dimension correlation mining to identify a fault root cause; and in combination with a power master station exclusive network topology model and a graph computing technology, tracing a fault propagation path and deducing an influence range of the fault on the associated power business, and finally generating a fault positioning result containing a fault root cause, the propagation path, a fault influence score, a business influence range and a disposal priority. According to the invention, the problem that the fault of the power master station service cannot be accurately and efficiently positioned in the prior art is effectively solved.

The invention relates to the technical field of data processing, and discloses a hierarchical data classification processing and deep correlation analysis system and method based on a knowledge graph, and the system comprises an acquisition module which is configured to obtain multi-modal evidence obtaining data, and carries out the preprocessing of the multi-modal evidence obtaining data; the data processing module is configured to respectively construct data sets according to the preprocessed multi-modal evidence obtaining data; the graph construction module is configured to extract main body data of the data sets, divide the data sets with the same main body data into the same category, compare all data in the data sets with the same category, and construct a knowledge graph according to a comparison result; and the association analysis module is configured to update the knowledge graph according to the multi-level association mining result. According to the method, the data processing efficiency is improved, and the mining capability of deep association of the data is enhanced by constructing and continuously optimizing the knowledge graph.

The invention discloses an enterprise data quality evaluation and growth ability analysis method and system based on experience reuse. The method comprises the following steps: calculating a data quality evaluation score of enterprise operation data through a normalization method; performing clustering optimization on the score data by using a K-means + + algorithm, and dividing the data into three quality grades of priority, suboptimal and general; selecting preferential and suboptimal data, combining with Horkes process analysis, utilizing a random forest model to predict relevance, filtering low-predicted-value data, and forming a new data set; and based on the new data set, generating a centesimal system enterprise growth ability score by adopting a data quality evaluation index and a weight in combination with a Sigmoid operator. The system comprises a data quality evaluation module, a data clustering and grading module, a data association prediction module and an enterprise growth ability scoring module. According to the method, whole-process optimization of data quality from static evaluation to dynamic grading and from association mining to growth analysis is realized, and the accuracy and reliability of enterprise growth capability evaluation are improved.

The invention discloses an enterprise multi-level relation rapid retrieval method and system based on a knowledge graph, and relates to the technical field of enterprise management, and the method comprises the steps: collecting internal and external multi-source heterogeneous data of an enterprise, controlling the collection time, and carrying out the legality verification of the collected data; receiving the verified data, constructing an enterprise knowledge graph, and optimizing a knowledge graph association rule based on monthly newly added data; based on the knowledge graph, receiving a retrieval request through a query interface, and returning a retrieval result; and applying the retrieval result to cost-benefit analysis of association between enterprises, including association cost accounting, association benefit evaluation, cost-benefit association mining, intelligent prediction or abnormal early warning traceability. The method has the advantages that the accuracy and efficiency of enterprise relationship risk identification, investment decision support and strategic planning are effectively improved by constructing the multi-dimensional associated knowledge graph between the enterprises, and comprehensive data support and intelligent decision support are provided for the enterprises to manage complex commercial relationships.

The invention relates to the technical field of human resource intelligent management, and discloses a post task analysis-based competency model construction method and system, and the method comprises the steps: obtaining target post related data, high-performance employee behavior data and enterprise strategy related data; performing weight fusion and normalization processing on the target post related data to obtain standardized feature data; performing clustering, feature extraction and association mining on the standardized feature data and the high-performance employee behavior data to obtain capability element related information; in combination with the weight corresponding to the enterprise strategy related data, carrying out dimension integration and priority ranking on the capability element related information to obtain hierarchical competency related information; and introducing talent evaluation feedback data, and correcting model parameters through a Bayesian iterative algorithm to obtain a dynamic adaptive post competency model. According to the method, the adaptation degree of the model, post tasks and enterprise strategies is improved, and an accurate decision basis is provided for talent management.

This invention discloses a highly robust web crawler system, belonging to the fields of big data acquisition and artificial intelligence technology. The system is a closed-loop intelligent system with multiple modules working collaboratively. It includes a multi-data source database construction and pre-training module, a real-time monitoring module for crawler operation status, a data semantic analysis and association mining module, a data request-matching evaluation and parameter optimization module, and a dataset generation and delivery module. Through tight coupling between modules and effective transmission of data flow and control flow, the system achieves full-process automation and intelligence from data discovery, intelligent crawling, dynamic adaptation to high-quality delivery. The system features high robustness and anti-crawling capabilities, strong data semantic understanding and association accuracy, and low manual costs.

The application discloses a cross-domain user portrait construction and content matching method based on federal learning, relates to the technical field of federal learning, and realizes high-precision learning of user dynamic representation by modeling full-dimension interaction context of users, items, domains and time windows through a four-order time sequence hypergraph and realizing user dynamic representation based on a time sequence hypergraph neural network THGNN; a double-path federal security aggregation mechanism is designed, cross-domain security exchange and association mining based on a user interest track evolution operator are carried out while generating user cross-domain global fusion representation through security aggregation, and accurate capture of user cross-domain behavior lag influence is realized; by constructing a cross-domain evolution mode knowledge base, group-level general evolution modes and user individual-level personalized evolution rules are deposited, cross-domain cold start problems are effectively alleviated, and model generalization performance is greatly improved.