616 results about "Triplestore" patented technology

The invention relates to the technical field of information data analysis. The invention provides a public policy case analysis knowledge graph fusion reasoning method and system. The method comprises the following steps: generating standardized preprocessing data; performing extraction processing on the standardized pre-processed data to generate a structured triple set; processing the structured triple set to generate a multi-dimensional knowledge graph; constructing a hybrid inference engine, and processing the multi-dimensional knowledge graph to generate inference result data; performing incremental updating processing of nodes and relationships on the multi-dimensional knowledge graph, and performing parameter optimization processing on the hybrid inference engine to generate an updated knowledge graph and an optimized inference engine; and reasoning result data are processed, and a visual analysis result is output, so that the problems of limitation of a rule engine on causal reasoning, semantic fuzziness and logic illusion of a large language model in the policy field and insufficiency of a single-field knowledge graph on cross-field interaction influence revelation are solved.

The invention discloses an industrial innovation knowledge graph dynamic construction method based on a large language model, and belongs to the technical field of data processing. According to the method, multi-source heterogeneous data are integrated, cleaning, format conversion and standardization processing are performed, entities, relationships and attributes are automatically extracted by adopting a large model driven zero sample extraction technology, a high-precision triple library is constructed by combining entity embedding alignment and context anaphora resolution, and a knowledge graph is generated based on a graph database. According to the method, incremental data are captured in real time through a dynamic sensing layer, graph dynamic updating is achieved through an atomization updating mechanism, and data timeliness is guaranteed in combination with timestamps and multi-source verification. According to the method, the knowledge graph construction efficiency and coverage rate are improved, the defects that a traditional method is high in manual dependency degree, long-tail knowledge is missing, updating lags and the like are effectively relieved, and accurate support is provided for technical innovation decision making.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention discloses a rule base dynamic construction method and device based on a large language model and a medium, and relates to the field of rule base construction.The method comprises the steps that on the basis of a preset layered template structure, meta-knowledge injection is conducted through a field knowledge graph corresponding to standard data, and dynamic cue words are generated; outputting a corresponding semantic triple through the large language model, and performing semantic enhancement on the semantic triple; performing symbolization processing and vectorization processing to obtain a rule vector, and generating a corresponding specified rule; and locally and dynamically updating the rule subset in the rule base through data updating of an external specified knowledge base, and dynamically updating the rule weight of the specified rule determined through the confidence coefficient. Through deep collaboration of the large language model and the symbol system, a logic verification layer is introduced through local dynamic updating to carry out formalized constraint on a large language model generation result, and the rule logic completeness is ensured while the generation capability is reserved.

The invention discloses a fault root cause analysis method and device for a distributed software system and a storage medium, and the method comprises the steps: obtaining fault data of the distributed software system, and extracting key information from the fault data; processing the key information according to a triple model to obtain a knowledge graph corresponding to the key information; when a fault analysis task is received, performing abnormal chain reasoning based on the knowledge graph, and generating a fault reason description text of the distributed software system based on an abnormal chain reasoning result; and inputting the fault reason description text into the large model to obtain a fault analysis result which corresponds to the fault analysis task and is deduced by the large model. According to the scheme, the technical problems that a traditional fault detection and root cause analysis method is low in analysis efficiency and inaccurate in root cause positioning are solved, and rapid and accurate fault attribution is achieved through the knowledge graph and the large language model.

The invention relates to a method for constructing a subject textbook knowledge graph by using a large language model, and the method is realized through six steps: firstly, introducing a self-prompt framework, generating relation synonyms, synthesizing samples and sentence variants through three rounds of dialogues, and providing rich semantic guidance for subsequent relation extraction; secondly, guiding a large language model to accurately extract core knowledge point entities from teaching materials, exercises and PPT texts by means of a professional field instruction template; then, respectively extracting an attribute triple and a relation triple of the knowledge points by applying a multi-round dialogue mode and combining with a synthetic sample prompt; then, inputting the extracted triad into a verification module, and ensuring the accuracy through iterative verification; and finally, generating an entity embedding vector by utilizing an MPNet model subjected to subject knowledge fine adjustment, calculating entity similarity through a dynamic weighted pooling mechanism, judging entity pairs with high similarity, performing knowledge fusion if the entity pairs represent the same concept, and otherwise, reasoning a potential missing relationship and complementing the knowledge graph. According to the method, the knowledge graph of the course of the specific subject can be automatically constructed from the unstructured text efficiently and accurately, and powerful support is provided for teaching and learning of related subjects.

The invention discloses a financial document automatic auditing method and device and a medium, and relates to the technical field of financial reimbursement auditing. The method comprises the following steps: receiving a financial document to be audited and an associated attachment document, respectively extracting a first entity set, and extracting a second entity set from unstructured content; constructing a dynamic knowledge graph state space based on the entity set, wherein the dynamic knowledge graph state space comprises an entity vector generated by an entity embedding algorithm and a relation vector generated by a relation coding algorithm; defining a reinforcement learning action space, wherein the reinforcement learning action space comprises three types of atomic operations of newly adding and deleting a triple and adjusting confidence; in combination with the real-time document flow, the historical case library and the audit result data, dynamically evolving the knowledge graph through atomic operation, and calculating a value return value of each operation; pre-judging accumulated return values of different operation sequences by utilizing a Monte Carlo tree search algorithm, and pruning a low return sequence; executing the optimized operation sequence to update the knowledge graph; and finally, based on the updated atlas, triggering a logic verification rule to generate an auditing result.

The invention relates to the technical field of big language models, and discloses a big language model training method based on knowledge graph enhancement, comprising the following steps: S1, constructing a multi-source heterogeneous knowledge graph; s2, coding the mixed attention heterogeneity map; s3, bidirectionally mapping a pre-training task; and S4, position specific gating fusion. According to the big language model training method and system based on knowledge graph enhancement, a same proton graph is established for a structured triple and text entity description, nodes are connected across graph edges to form a heterogeneous graph, associated edges are established through entity linking and syntactic analysis, multi-source knowledge is modeled in a unified mode, and the problem of low fusion efficiency is solved; mixed attention coding adopts a layering mechanism, a semantic level calculates weights according to type compatibility, a node level calculates similarity aggregation features through cosine distance and path length, entity vectors are generated through pooling, map structures and semantics are explicitly learned, reasoning accuracy is improved, and the problem of knowledge understanding superficial layer is solved.

The invention discloses a multi-table combined natural language query SQL (structured query language) generation method, which comprises the following steps of: 1, constructing a database meta-knowledge graph, and establishing a triple storage comprising a table structure, a primary and foreign key relationship and business description for each data table; 2, receiving a natural language query request, and calculating the topic relevancy between query semantics and each database table through a pre-trained topic matching model; 3, dynamically constructing a view, and logically associating the database tables of which the theme relevancy exceeds a threshold value to form a temporary view; 4, generating a context enhancement prompt, and combining the temporary view structure, the field semantic description of the view and the view content sample to form a structured prompt; 5, inputting the natural language query and the structured prompt into the large language model to generate candidate SQL statements; and step 6, executing verification and iterative optimization on the candidate SQL statements, verifying logic correctness through SQL execution plan analysis and result sampling, and triggering and prompting a reconstruction mechanism when detection is abnormal.

The invention discloses a knowledge graph construction method and apparatus, and a medium. The method comprises the steps of collecting a to-be-processed text for constructing a knowledge graph in a specified field; screening out a target text from the to-be-processed text, wherein the correlation between the target text and the specified field meets a preset condition; processing the target text through a pre-constructed target large language model to construct an initial triple; filtering the initial triple to obtain a target triple; and constructing the knowledge graph through the target triple. Therefore, through strict correlation screening conditions, texts irrelevant to the specified field are removed, the influence of noise data on knowledge graph construction is reduced, and the quality and accuracy of the knowledge graph are improved. On the basis of the target texts, the knowledge triples are efficiently and accurately extracted from a large number of target texts by means of the powerful language understanding and generating capacity of the large language model, the labor cost overhead of manual extraction is reduced, and the knowledge graph construction efficiency is improved.

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an execution method of a large model graph retrieval enhancement system oriented to software and hardware monitoring operation and maintenance, which comprises the following steps: S1, inputting fault information to the graph retrieval enhancement system; s2, a graph construction and updating module receives and processes the fault information, generates a triple, and writes the triple into an operation and maintenance / fault knowledge graph; s3, the fault information is input into a graph retrieval enhancement module for two-stage filtering retrieval, and sub-graph information is screened out; s4, the prompt word construction module converts the fault information and the screened sub-graph information into structured natural language prompt segments; and S5, a reasoning generation module performs natural language question and answer to generate a fault analysis result. Based on the above scheme, the execution method enhances the adaptive capacity of knowledge reasoning and fault positioning, gives play to the generalization reasoning capacity of a large language model while ensuring the accuracy, and enhances the practical value.

The invention relates to the technical field of knowledge questions and answers, in particular to a method and system for detecting and repairing large language model output illusion, which comprises the following steps of: dividing answer content into a plurality of sentences, calculating the confidence coefficient of each sentence, judging whether the confidence coefficient of each sentence exceeds a set threshold value or not, performing fact extraction on the sentences with the confidence coefficient lower than the set threshold value, obtaining triple data of each sentence; generating a subgraph A of each sentence based on the triple data of each sentence; extracting a sub-graph B from the local knowledge graph based on the subject in the triple data of each sentence; calculating the similarity between the sub-graph A and the sub-graph B, and if the similarity is smaller than a set threshold value, repairing the sub-graph A according to the sub-graph B to obtain a repaired sub-graph A; according to the triad data corresponding to the repaired subgraph A, obtaining a repaired sentence; and after all the sentences with the confidence lower than a set threshold value are repaired, final answer content is obtained. And the accuracy and reliability of model output are improved.

The invention provides a government affair hotline service knowledge graph construction method and system based on a large language model, and relates to the technical field of natural language processing and government affair informationization, and the method comprises the steps: obtaining a government affair hotline service scene, and defining a mode layer of a multi-scene knowledge graph; unstructured government affair hotline question and answer data are collected, semantic fusion, logic extraction and ambiguity elimination are carried out on the data based on a chained thinking mechanism of a large language model, and a fused government affair hotline question and answer set is generated; driving a large language model to identify knowledge entities in the set by adopting a step-by-step prompt strategy, extracting semantic relationships among the knowledge entities by adopting a semantic association analysis method, constructing triple knowledge data based on the knowledge entities and the semantic relationships, storing the triple knowledge data into a graph database, and performing dynamic updating and multi-dimensional retrieval on the multi-scene knowledge graph to obtain a multi-scene knowledge graph set; therefore, a large language model and a knowledge graph technology can be fused, government affair data are efficiently integrated, and the intelligent level of government affair service is improved.

A system using global and local-aware denoising framework for commonsense knowledge graph noise detection is provided. The system includes an input receiver, a triple encoder, a global noise detector, a local noise detector, and a comprehensive evaluation scorer. The triple encoder receives the CSKG information from the input receiver and extract semantic information from free-text formatted nodes in the CSKG information, so as to obtain structured triples. The global noise detector identifies high-frequency patterns through rule mining and uses a rule encoder to generalize learned rules and guide noise detection. The local noise detector utilizes the structured triples output by the triple encoder to aggregate neighboring triple information around both the head and tail nodes via graph networks. The comprehensive evaluation scorer provides a comprehensive score based on global noise detection and local noise detection, so as to indicate a noise level for the CSKG information.

The invention belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

The invention provides a fluororubber field knowledge graph construction method based on a large language model, and the method comprises the steps: obtaining process related knowledge data of the fluororubber field, and carrying out the classification, and obtaining the classification knowledge data of the fluororubber field; based on a preset ChatGLM model, performing knowledge extraction according to the fluororubber field classification knowledge data to extract key entities and relationships; carrying out deep analysis on the key entity and the relationship by utilizing semantic correlation and context association analysis, and enhancing the connectivity of the key entity and knowledge in the relationship through semantic similarity and association strength to obtain fused knowledge data; explaining and expanding the fused knowledge data by using a ChatGLM model to obtain expanded triple data; and performing knowledge fusion on the triple data, and performing graph storage by using a Neo4j database to obtain a knowledge graph. According to the method, the knowledge graph construction efficiency is remarkably improved through automatic knowledge extraction and processing of the large language model, and the time cost of manual labeling and data processing is reduced.

The invention discloses a multi-agent cooperation enhancement method, system and equipment based on a knowledge graph, and the method comprises the steps: obtaining original data in an external environment, carrying out the preprocessing and feature extraction of the original data, generating a knowledge triple, storing the knowledge triple in a local knowledge graph, and submitting the knowledge triple to a shared knowledge graph for knowledge updating; when a to-be-executed task is received, decomposing the to-be-executed task by utilizing the large language model and querying global knowledge in the shared knowledge graph and local knowledge in the local knowledge graph to obtain a plurality of sub-tasks; a bipartite graph minimum cost matching algorithm is adopted to match a plurality of sub-tasks with the capability and availability of each agent to generate a preliminary task allocation scheme, and a large language model is utilized to optimize the preliminary task allocation scheme to generate an optimal task allocation scheme; and sending each task allocation knowledge fragment in the optimal task allocation scheme to a corresponding agent for collaborative execution through a semantic communication protocol.

The invention particularly relates to a document abstracting method based on domain knowledge and a multi-granularity graph network, which comprises the following steps: S1, constructing a knowledge coding module, carrying out domain knowledge graph transfer learning, extracting triple structure features in combination with a capsule network, and generating a text entity coding module; s2, constructing a knowledge fusion module, converting a document text into a semantic vector, and adaptively fusing the semantic vector with external knowledge; s3, constructing a word-level semantic graph, and aggregating neighborhood information through a semantic graph attention mechanism to obtain word embedding representation containing context; s4, constructing a sentence-level semantic graph, performing self-attention calculation on the word-level semantic vector in the S3 to obtain semantic node representation of the sentence-level semantic vector, and performing feature aggregation on sentence-level nodes to obtain global semantic information of the document; and S5, constructing an output layer, and predicting according to the document feature vector in the S4 to obtain a final extraction type abstract result. The method has the advantages that redundant information in the document is removed, and ambiguity of text semantics is eliminated.

The invention discloses a government affair data processing method, system and device and a storage medium, and relates to the field of intelligent government affair decision, and the method comprises the steps: generating a tamper-proof block chain evidence storage triple through a multi-chain collaborative evidence storage mechanism based on a structured policy vector and government affair data; performing real-time decision matching by adopting a cosine similarity algorithm according to the block chain evidence storage triad, calculating a similarity value of the government affair item state vector and the structured policy vector, and generating a decision execution record when the similarity value reaches a policy compliance reference requirement; based on the decision execution record, starting a multi-chain collaborative auditing mechanism, identifying and positioning illegal operation nodes and generating an auditing report; according to the audit report, the audit data and the policy efficacy weight are optimized through a policy analysis engine, and a government affair data optimization proposal is generated; the policy knowledge graph is constructed by fusing the aging characteristics and the regional parameters through a dynamic quantization algorithm, and the structured policy vector is generated, so that the efficacy evaluation deviation of a static model is effectively solved.

The invention discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention discloses a transformer fault diagnosis method and system based on a fusion knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured text and a semi-structured text; constructing a joint entity relationship extraction model and a question and answer module, wherein the joint entity relationship extraction model comprises a coding layer, a head entity recognition layer and a tail entity and relationship joint recognition layer; the question and answer module comprises a LangChain model and a large language model, and the output of the LangChain is used as the input of the large language model; obtaining a fault problem text, inputting the fault problem text into the LangChain model, outputting professional knowledge, inputting the professional knowledge and the fault problem text into the large language model, and outputting a professional fault diagnosis answer; and interactively combining the professional fault diagnosis answer with the high-quality knowledge triple text, and outputting a final fault diagnosis answer. According to the method, the problems of weak information relevance, low decision generation efficiency and the like in the operation and maintenance process of the transformer can be solved in a targeted manner, and the health management level of the power transformer is greatly improved.

The invention belongs to the technical field of new-generation information, and particularly relates to a power plant metal supervision entity relationship extraction method based on dual coding, which comprises the following steps: multi-modal data preprocessing: collecting and cleaning data, and carrying out data labeling; constructing a dual coding joint learning model: designing a network layer architecture, and training the dual coding joint learning model; constructing and querying a dynamic knowledge graph: extracting a model to generate a triple, and performing time sequence evolution analysis, causal reasoning interface and dynamic updating; and incremental knowledge updating and dynamic model optimization: an incremental learning and feedback module forms a bidirectional closed loop between the knowledge graph and the relationship extraction model, and continuous evolution of the system is ensured through dynamic knowledge updating and adaptive model optimization. According to the method, through bidirectional feature modeling of dual paths, collaborative representation optimization between entities and relationships is realized while context information is captured, so that the requirements of complicated data types and diversified semantic associations in an engineering scene are met.

The invention discloses an industrial field-oriented incremental knowledge graph updating and consistency verification system, which comprises the following steps of: monitoring original industrial data to obtain new data, old data and a data change set; respectively converting the new data and the old data into structured data according to a preset knowledge extraction rule set, and respectively mapping the structured data into triads according to a preset mapping rule to obtain a triad change set; calculating update priorities of a plurality of attributes in the triple change set, and obtaining a first update atlas according to the update priorities; performing multiple consistency verification on the first update atlas, and modifying the first update atlas according to a verification result to obtain a second update atlas; and constructing a knowledge change link diagram according to the first update map and the second update map, and carrying out rollback verification on the knowledge change link diagram. The invention relates to the technical field of industrial knowledge maps, and solves the technical problems of high resource consumption, difficulty in guaranteeing knowledge consistency and the like in a traditional full-quantity updating mode.

The invention belongs to the technical field of RAG questioning and answering, and particularly relates to an efficient and accurate RAG questioning and answering method and device based on a knowledge graph and a storage medium. The method comprises the following steps: reading contents in a text file, dividing the contents to obtain text blocks, and obtaining an initial triple by using a large language model; performing post-processing on the initial triad to obtain an optimized triad, and constructing a knowledge graph; building an index key value pair based on the optimized triad and the text block to which the triad belongs, and storing the index key value pair in a vector database; performing mixed knowledge retrieval based on the index key value pair to obtain mixed knowledge beneficial to question answering: unstructured knowledge from the text and structured knowledge from the knowledge graph; and taking the mixed knowledge as the context of question answering, and reasoning by using a large-scale language model. According to the method, the response time is remarkably shortened, the unstructured knowledge and the structured knowledge are effectively integrated, and the accuracy of low-level retrieval and high-level retrieval is remarkably improved.

The invention provides a construction method and device of an instruction generation agent based on a knowledge graph, a medium and equipment, and the method comprises the steps: obtaining a text, extracting a triple in the text, connecting the triple data to a graph database, and constructing a text knowledge graph; performing statistical analysis on the text knowledge graph from the aspects of entities, relationships and graph structures based on the text knowledge graph; based on the statistical analysis result, an instruction generation agent is constructed, an agent planning module, an agent tool module, an agent storage module and an agent execution module are constructed, and the agent execution module executes instruction generation operation based on the reasoning planning path and the cue word template of the corresponding category. And evaluating the quality of the instruction pair and executing an instruction rewriting operation on the data which is unqualified in evaluation. According to the method, the triple chain is constructed as the context knowledge point by utilizing the structural relationship of the knowledge graph, so that the illusion generated by the intelligent agent is reduced, and the quality of instruction data is ensured.

The invention provides a tunnel risk reasoning method fusing a knowledge graph and a large language model, which comprises the following steps of: obtaining structured monitoring data and unstructured text data, adopting methods such as field standardization for the structured monitoring data to realize a unified format, adopting methods such as sentence segmentation and word segmentation for the unstructured text data to realize the unified format, and obtaining the structured monitoring data and the unstructured text data; the method comprises the following steps of: extracting entities from data by utilizing a model, extracting a relationship between the entities based on the entities, forming basic triads, forming a sub-graph by the basic triads, integrating to form a knowledge graph, generating a natural language, extracting the sub-graph related to the natural language from the knowledge graph, and converting the sub-graph into a sub-graph in a vector form by utilizing a graph embedding algorithm. The entities and the relation paths of the entities serve as explicit reasoning clues, the natural language, the sub-maps in the vector form and the explicit reasoning clues are input into a large language model, natural language output is generated, multi-source data information is integrated, and high-precision and interpretable tunnel risk early warning is output through the large language model.

The invention discloses a water conservancy knowledge graph intelligent question-answering system and method based on a large language model, and relates to the technical field of water conservancy information, and the method comprises the following steps: carrying out the fusion processing of multi-source water conservancy data in advance, constructing a triple knowledge graph containing a water conservancy field entity type and a relation system, and dynamic updating of the knowledge graph is realized through an incremental learning algorithm. According to the method, the defects of a traditional method in semantic understanding are effectively overcome, deep semantic association of professional query can be accurately captured, and answer deviation caused by keyword matching limitation is avoided. Meanwhile, a dynamic updating mechanism of the knowledge graph can integrate new knowledge such as new projects and industry standard updating in real time through an incremental learning algorithm and a time decay function, obsolete out-of-time information synchronously, ensure that a knowledge system of the system is synchronous with development of the water conservancy industry, and solve the problems that a traditional system is high in updating cost and long in period.

The invention discloses a multi-modal data fusion financial service information processing method and system, and the method comprises the steps: S1, collecting enterprise text data and enterprise table data, and carrying out the preprocessing, and obtaining cleaned text data and cleaned table data; s2, calculating an enhanced text embedding vector, extracting a semantic representation feature vector, decoding a text triple set, and generating a text semantic vector; s3, calculating a standardized field set and a row-level entity primary key, outputting an exception risk vector, and then calculating a table exception mark and a table semantic vector; s4, calculating an entity alignment gate, and calculating an entity alignment feature vector; calculating an entity matching degree score, and finally calculating a cross-modal conflict mark; and S5, calculating a cross-modal fusion vector through the multi-layer perceptron, and then calculating a knowledge representation vector. According to the method, the problem of low entity alignment precision caused by data structure difference, field semantic conflict and insufficient entity recognition precision of a traditional method can be solved.

The invention discloses an intelligent construction and tracing method and device for an attack graph, and relates to the technical field of network security. The method comprises the steps of performing semantic analysis and entity relationship extraction on a multi-source heterogeneous security log according to a predefined structured security data model, and generating a standardized security entity relationship triple set; based on the set, taking an entity in an initial alarm as a starting point, and adopting an iterative closed loop driven by a large language model to dynamically construct an attack graph; and carrying out attack technique and tactics mapping and threat attribution based on the final map, and generating a response strategy of priority ranking. According to the method, automatic and high-precision source tracing and response of the attack chain are realized, and the problems that the prior art depends on static rules and semantic segmentation and lacks dynamic reasoning capability are effectively solved.