11821results about "Text database clustering/classification" patented technology

The invention provides a notarization intelligent question and answer customer service method and system based on a knowledge graph, and relates to the technical field of information, and the method comprises the steps: collecting multi-source heterogeneous data in the notarization field, forming an entity set and a relationship set, combining to form an initial knowledge graph, carrying out a dynamic update mechanism based on time decay, and supplementing to form a dynamic knowledge graph. Carrying out vectorization expression to obtain an atlas vector; receiving a multi-modal problem, determining fusion features, performing deep semantic understanding, obtaining semantic features, determining a user intention, and forming a structured query expression; based on the user intention and the structured query expression, carrying out multi-hop reasoning in the dynamic knowledge graph, carrying out similarity calculation by utilizing graph vectors, determining a reasoning path in combination with the user intention, carrying out knowledge association reasoning along the reasoning path, obtaining a preliminary reasoning result, carrying out interpretability analysis on the preliminary reasoning result, and obtaining a final reasoning result; and generating a reasoning chain and a confidence score, forming a detailed reasoning result, and finally generating a personalized answer.

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The invention relates to the technical field of data processing, and discloses a financial transaction anomaly detection and risk assessment method and device based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source financial transaction related data to obtain a multi-dimensional transaction data set; performing feature engineering and abnormal mode recognition on the multi-dimensional transaction data set to obtain an abnormal transaction recognition result; based on the abnormal transaction identification result and the multi-dimensional transaction data, constructing a financial domain knowledge graph to obtain a relation network between transaction entities; performing multi-dimensional risk assessment on the relation network among the transaction entities to obtain a risk score and risk description data; based on the risk score, performing time sequence analysis and trend extrapolation on the risk description data to obtain a dynamic risk early warning signal; and performing scene simulation and multi-factor analysis on the dynamic risk early warning signal to obtain a risk management strategy suggestion. The efficiency and accuracy of financial transaction anomaly detection and risk assessment based on artificial intelligence are improved.

The invention provides an AI intelligent customer service response method and system based on a remote digital service, and the method comprises the steps: firstly receiving a session request data flow inputted by a user through a remote session terminal, and extracting a dynamic semantic feature sequence containing a grammatical structure, an emotional tendency and a context dependence feature; inputting the data into an intention recognition model trained based on a historical session data multi-dimensional semantic relationship, and outputting an intention category label and confidence; matching a target dialogue stream node from a preset dialogue process library according to the label and the confidence coefficient; dynamic response content is generated based on the node response logic rule, and a response data stream is returned; and finally, collecting user feedback behavior data, updating the association weight of the intention recognition model according to an interaction validity index, optimizing the semantic mapping precision of the intention recognition model to similar session requests, and realizing more accurate, intelligent and adaptive customer service response.

The invention discloses an archive data security integration management system, which relates to the technical field of knowledge maps and comprises an archive data acquisition module, a knowledge intelligent analysis module, a knowledge map dynamic construction module, a security situation evaluation module and a security communication center module. The data acquisition module structurally acquires metadata and operation behaviors, the intelligent analysis module dynamically governs data quality, the map construction module constructs a three-dimensional security model, the situation evaluation module quantifies security influence and generates a strategy, and the communication center module realizes dynamic identity authentication and bandwidth allocation. Through the knowledge graph technology, comprehensive integration and safety management of the archive data are achieved, the data quality and utilization efficiency are improved, dynamic evaluation and coping with safety threats are achieved, and the safety and integrity of the archive data are ensured. Meanwhile, through dynamic identity authentication and bandwidth allocation, the safety and efficiency of cross-domain communication are improved.

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

Techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users. The knowledge-graph system builds the knowledge graph using answers produced by an LLM for novel queries. The chatbot will continue to use the LLM to answer novel queries, but the chatbot may harness the knowledge graph to answer repeat questions to gain various efficiencies over LLM-backed chatbots. For example, the knowledge-graph system may easily debug or otherwise improve the answers in knowledge graphs, store provenance information in knowledge graphs, and augment the knowledge graphs using other data sources. Thus, the reliability and correctness of chatbots will be improved as the bugs and inaccuracies in answers provided by the LLM will be corrected in the knowledge graphs, but the chatbots can still harness the abilities of LLMs to provide answers across various subject-matter domains.

The invention provides an unstructured text data calculation method and system, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the multi-level processing of input unstructured text data, dynamically adjusting the word segmentation granularity according to the word frequency distribution, constructing a part-of-speech co-occurrence matrix in combination with context semantic information, and extracting entity information. And fusing the part-of-speech co-occurrence matrix and the entity information to generate a hierarchical semantic tag sequence. Feature extraction units with different convolution kernel sizes are adopted to extract feature representation, and cosine similarity between different semantic hierarchies is calculated to establish an association weight matrix. A semantic enhancement vector is constructed based on entity information, and adversarial training is carried out to obtain a multi-modal semantic feature matrix. And calculating the semantic similarity between the fusion feature vectors for clustering, setting difficulty weight sorting according to the complexity, consistency and ambiguity of class clusters, then inputting the class clusters into a classifier, and iteratively optimizing a classification result to obtain a calculation result.

Disclosed herein are systems and methods for graph-based dynamic information retrieval and synthesis, particularly for enhancing the response generation of chatbot applications using large language models (LLMs). The methods involve receiving a user query, generating a query embedding, and accessing a knowledge graph comprising nodes representing documents with associated document embeddings. A set of documents is retrieved based on similarity scores, and a distance matrix is constructed to calculate shortest-path distances between nodes. Documents are clustered to identify a largest cluster from which content is retrieved to formulate a response. The disclosed embodiments improve upon retrieval augmented generation (RAG) by leveraging the hierarchical relationships of a knowledge graph and node interconnectedness to provide contextually coherent information and mitigate the inclusion of information from irrelevant or superficially related documents in LLM responses.

The invention relates to the technical field of archive management, in particular to an archive full life cycle management system and method based on cloud computing. The method specifically comprises an intelligent classification module based on a format adaptation degree function and semantic density analysis, a five-stage life cycle management module, and an authority scheduling module fusing RBAC / ABAC authority control and block chain auditing, and based on a matrix decomposition archiving static coding mechanism, document security storage and analysis are realized through an orthogonal matrix and pseudo-inverse operation, and the file security is realized. The data security in a long-term archiving scene is improved; cloud storage resources are dynamically optimized according to the document life cycle state and the access frequency, and the storage cost is reduced in combination with a cold and hot layering strategy; and an AI algorithm is introduced, the archive retention value is automatically evaluated according to laws and regulations and semantic analysis, and a compliant destruction decision is realized. According to the method, through multi-model fusion metadata extraction, dynamic strategy binding and automatic state transition, the whole-process management efficiency and safety are remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses a bidding document generation method based on retrieval enhancement generation and a large language model, and the method comprises the steps: analyzing technical parameters, legal terms and score weights in a bidding demand document; retrieving matched historical bidding document fragments and technical specifications from the industry knowledge base, and generating a retrieval enhanced data set; fusing the data through a dynamic weight distribution algorithm and generating an initial bidding document draft; checking conflict terms based on the legal semantic knowledge graph and marking correction suggestions; and optimizing the bidding document structure and the key content according to the score weight. According to the method, an online learning mechanism driven by sentence vector matching, timeliness weight calculation and user feedback is adopted, so that the problems of inaccurate technical parameter extraction, low legal conflict detection efficiency and non-optimized scoring rules in the bidding document generation process are solved.

The invention relates to the technical field of archive management, and discloses an AI and encrypted storage-based archive management method, which comprises the following steps of: preprocessing original archive information by using a natural language processing algorithm to generate structured archive data, and performing multi-level classification through a dynamic classification tree model. Data are encrypted by adopting a hybrid encryption technology, and encrypted archive data blocks are constructed and stored in a distributed database. And performing parallel retrieval by using a multi-modal retrieval model, performing decryption according to user permission and scenes based on a dynamic decryption strategy, and finally transmitting data through a secure channel. In addition, the system also has the functions of file data updating and version control, log access and anomaly detection, cross-mechanism file sharing and the like. The efficiency, safety and intelligent level of archive management are improved, and the problems existing in traditional archive management are effectively solved.

A method includes receiving, from a plurality of sources, data associated with a plurality of events at the plurality of sources, standardizing the data based on a set of predefined standardization rules to define standardized data, and defining a vector representation for each event from the plurality of events based on the standardized data. The method includes assigning each event from the plurality of events to at least one cohort from a plurality of cohorts based on a similarity associated with the vector representation for that event and each cohort from the plurality of cohorts, generating, using at least one machine learning model, an ontology based on a set of cohorts from the plurality of cohorts and associated with the plurality of events, and storing the plurality of events as associated with the ontology such that the plurality of events can be filtered based on the ontology.

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

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

The invention discloses a customer service data quality inspection method and device based on dynamic reasoning, equipment and a medium, and the method comprises the steps: analyzing customer service dialogue data, and generating structured dialogue data containing an intention label and a key problem node; according to an intention label in the structured dialogue data, a framework is generated through retrieval enhancement, and matched domain knowledge is recalled in real time from a standard knowledge base so as to construct a dynamic context knowledge graph; taking the dynamic context knowledge graph as input, utilizing a thinking chain prompt template to guide a large model to carry out step-by-step logical reasoning, and outputting a preliminary quality inspection conclusion chain; performing retrieval verification on assertion nodes in the initial quality inspection conclusion chain to generate a traceable final quality inspection conclusion; and based on the final quality inspection conclusion, calculating a quantitative score of the customer service dialogue data and generating a visual thinking chain report. And through dynamic retrieval of the knowledge base and logical reasoning, the accuracy, interpretability and adaptability of customer service data quality inspection are improved.

The invention discloses a urinary surgery diagnosis and treatment data intelligent processing system based on artificial intelligence, and the system comprises the following modules: an original diagnosis and treatment data collection and preprocessing module which is used for collecting the data of a urinary surgery patient; the stage annotation text construction module is used for constructing a stage annotation text set; the image description structuring module is used for constructing a structured image description sequence; the cross-modal semantic fusion module is used for extracting a cross-modal joint semantic representation vector sequence; the named entity recognition module is used for outputting a structured medical entity set; the entity semantic graph construction module is used for constructing an entity semantic graph structure; the diagnosis and treatment relationship extraction module is used for outputting a diagnosis and treatment relationship set of a triple structure; the structured packaging module is used for outputting a structured diagnosis and treatment data unit; and the intelligent diagnosis and treatment decision module is used for performing illness state recognition, treatment path generation and reasoning suggestion output. According to the invention, multi-modal semantic modeling and graph neural reasoning are fused, and an intelligent diagnosis and treatment relation recognition system is constructed.

The invention discloses an intelligent question answering system construction method and system based on an LLM large language model, and the method comprises the steps: carrying out the corpus aggregation from a plurality of heterogeneous data sources, and dynamically capturing the text data related to a target domain; preliminarily screening the text data, and based on semantic similarity and a text quality scoring rule, removing redundant and low-quality text data or text data irrelevant to a target field; performing diversity analysis on the text data, and generating diversified text data with high similarity with real data through an adversarial generation technology; performing multi-level semantic annotation on the text data, and generating a training sample by analyzing context information in the annotated corpus; and converting the training sample into a standard format sample, and training an LLM large language model by using the standard format sample to serve as an intelligent question-answering system of a target domain. By utilizing the embodiment of the invention, the intelligent level of the intelligent question-answering system in various application scenes can be effectively improved, and further development of the intelligent question-answering technology is promoted.

Embodiments of the disclosed technologies are capable of generating diverse suggested message content. The embodiments describe generating a message plan comprising attribute data and section data. The embodiments further describe inputting the message plan as a prompt to a first generative model. The first generative model is fine-tuned using a training message plan. The training message plan comprises an ordered sequence of training attribute data and training section data. The training attribute data and training section data are extracted from historic messages or generated messages. The embodiments further describe generating, by the first generative model, message content suggestions based on the attribute data and section data.

Device, system, and method for automatically detecting and classifying personally identifiable information (PII) in documents and files. A method includes performing a deterministic rule-based search, in a plurality of stored documents, for PII data-items. If the deterministic rule-based search indicates that a particular document is more likely than not to contain a PII data-items then the method includes: extracting a textual snippet from the particular document, wherein the textual snippets surrounds the PII data-item; adding the textual snippet and the particular document to one or more training datasets utilized for training a Large Language Model (LLM) configured to find PII data-items in documents for Named Entity Recognition (NER) in those documents.

The invention discloses a large language model knowledge base question answering system based on a multi-path fusion recall retrieval algorithm, and relates to the technical field of artificial intelligence application, and the system comprises the steps of S1, data collection, S2, data preprocessing, S3, knowledge base construction, S4, analysis and clustering, S5, knowledge recall, S6, weight adjustment, and S7, weight fusion. According to the method, a dynamic weight distribution module is arranged, so that various paths of recall weights such as keyword matching, semantic similarity and a knowledge graph can be flexibly adjusted according to different semantic scenes such as a technical scene and a product query scene, a recall result can be effectively optimized, knowledge related to problems can be accurately screened, and the method is high in practicability and high in practicability. And irrelevant information interference is reduced, the answer quality and the system efficiency are improved, and the user satisfaction is improved.

The invention provides a knowledge graph construction and intelligent question and answer method and device based on deep learning. The method includes; obtaining target text data; information extraction is conducted on the target text data, entity relation data is generated, and the entity relation data comprises key data, entity data and relation data used for indicating the relation between entities; according to the entity relationship data, importing the target text data into a graph database to form a knowledge graph; obtaining an input question statement; the question sentence classification model is used for classifying the question sentences, question sentence features in the question sentences are determined, and the question sentence features comprise question sentence types and question sentence entity data; and retrieving text data matched with the question features in the knowledge graph as answer data of the question statements. According to the method, the public text information retrieval efficiency is improved, and the accuracy of intelligent question answering is improved.

The invention discloses an aerospace knowledge large model construction method based on progressive knowledge injection and retrieval enhancement generation, which comprises the following steps: collecting and preprocessing multi-source knowledge data, and constructing an aerospace field knowledge base; the method comprises the following steps: based on a DeepSeek-R1-8B model, carrying out continuous pre-training by adopting a three-stage progressive mixed course learning framework; performing supervision fine tuning through instruction data and a knowledge guiding mechanism to complete basic construction of a large model; a retrieval enhancement generation module is constructed, a'retrieval-filtering-generation 'process is formed, and the knowledge coverage and fact consistency of the model are improved through deep fusion of real-time retrieval and generation reasoning; and through a multi-dimensional quantitative index and a dynamic test mechanism, the performance of the model is evaluated, and a complete evaluation ecology from data construction to feedback optimization is formed. According to the method provided by the invention, deep integration and efficient application of knowledge in the aerospace field are realized through multi-stage collaborative optimization.

The invention provides a large language model optimization generation method based on optimal cue word selection, and belongs to the technical field of large language model optimization generation. The technical problem to be solved is to provide the improvement of the large language model optimization generation method based on optimal cue word selection. According to the technical scheme, a selected data set is preprocessed, a sequence is formed by splicing questions and answers, and a spectral clustering algorithm is adopted to divide the sequence into multiple groups of question and answer pairs with similar characteristics; sampling representative samples from each cluster, and based on professional knowledge, language logic and semantic association, constructing high-quality cue words which can guide and generate a large model aiming at a specific problem; a large-parameter online model is used, a diversified and targeted cue word set is generated for each cluster with reference to the created high-quality cue words, generated questions, candidate cue words and answers of the candidate cue words are integrated, and a data set used for fine tuning of the cue evaluation model is constructed; the method is used for generating the language model.

The present disclosure provides techniques and solutions for automatically and dynamically supplementing user prompts to large language models with information to be used by the large language model in formulating a response. In particular, entities are identified in the original prompt. A semantic framework is searched for information about such entities, and such information is added to the original user prompt to provide a modified user prompt. In a particular example, the identified entities comprise triples, and verbalized triples are added to provide the modified user prompt. The modified prompt may be hidden from the user, so that a response of the large language model appears to be in response to the original prompt.

The invention discloses an electric power knowledge system construction and intelligent question and answer method based on a lightweight large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an electric power field knowledge system, and building a reasoning logic classification system to carry out the classification marking of a knowledge base in the electric power field knowledge system; based on the power domain knowledge system, a multi-dimensional similarity matching strategy is adopted to process a problem input by a user; generating a reasoning link based on a matching result of the multi-dimensional similarity matching strategy; the reasoning link is used for extracting and combining answers from the power domain knowledge system, and the generation process of the reasoning link comprises question decomposition and answer integration. According to the invention, systematic management of electric power professional knowledge is realized, and the matching accuracy of professional problems is improved; by generating the self-adaptive multi-hop reasoning link based on the question type and combining the question decomposition and answer integration technology, the intelligent question and answer ability of the system under the scenes of power equipment fault diagnosis, maintenance scheme recommendation, risk assessment and the like is improved.

Disclosed in the present application are a knowledge graph construction method and apparatus, and a storage medium, an electronic device and a computer program product. The method comprises: acquiring first natural language text; using an extraction model to perform entity extraction on the first natural language text, so as to obtain a first entity and a first entity relationship, and determining a corresponding first entity type and first relationship type; using a semantic encoder to determine a first semantic vector and a second semantic vector respectively corresponding to the first entity type and the first relationship type; on the basis of calculated first distances between the first semantic vector and cluster centers of a plurality of entity types and calculated second distances between the second semantic vector and cluster centers of a plurality of relationship types, determining a target entity type for the first entity type and a target relationship type for the first relationship type; and on the basis of the first entity, the first entity relationship, the target entity type and the target relationship type, constructing a target knowledge graph.

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.