3203results about "Text database clustering/classification" patented technology

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention relates to the technical field of electric power engineering information processing, and discloses a power grid power transformation engineering knowledge graph construction and retrieval method and system, and the method comprises the steps: carrying out the dynamic adaptive partitioning of a power grid power transformation engineering related document, and obtaining semantic coherent and independent text blocks; extracting entities and relationships based on the text blocks, and complementing implicit entities and relationships through a multi-round refining mode; performing fusion and disambiguation on the extracted and complemented entities and relationships to form a unified knowledge graph; performing hierarchical clustering on the formed knowledge graph to generate a multi-granularity community structure and a corresponding community report; intention resolution and pre-judgment guidance are carried out aiming at fuzzy questions of the user, and a retrieval strategy is optimized; and executing multi-hop semantic retrieval based on the optimized retrieval strategy, recalling related knowledge and generating answers. According to the method, automation, precision and intelligentization of power grid power transformation engineering knowledge graph construction and full-link retrieval can be realized.

The invention discloses a big language model generation content security test system and method in a black box scene. The system comprises a jailbreak prompt word library module used for storing jailbreak prompt words for performing security test on a big language model; the violation question and answer pair module is used for storing violation question and answer pairs covering different types; the response acquisition module is used for obtaining a data request packet according to query content formed by the jailbreak prompt word and the query request; the security analysis module is used for calculating the similarity between response data corresponding to the query request and an expected violation answer, taking the similarity as a security score, and inputting the security score into the adaptive optimization module; and the adaptive optimization module is used for optimizing the jailbreak prompt words output by the jailbreak prompt word bank module by using a genetic algorithm according to the security score output by the security analysis module. According to the method and the device, the security of the large language model generation content can be effectively tested.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention relates to the technical field of electrical digital data processing, and discloses a system conflict point intelligent review system based on a knowledge graph and a large language model, which comprises the following steps: constructing a dual-mode storage space containing an unstructured index and a structured logic graph, analyzing target text extraction features and triggering graph-based generation logic; converting the topological structure of the associated sub-atlas into a natural language instruction sequence to construct a forced logic constraint template, filling the template with a text, and inputting a pre-training language model to generate a verification result; according to the method, the discrete atlas topology is mapped into the linear logic constraint, random divergence of the generative model is restrained on the calculation principle, and precise decoupling and dynamic evolution of unstructured semantics and structured logic are achieved.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention discloses a dynamic feature retrieval generation and management method based on a cross attention mechanism, which belongs to the technical field of natural language processing, and comprises the following steps: step 1, converting knowledge base document fragments into atomic knowledge units, each atomic knowledge unit comprising question and answer pairs, codes as key value pairs, and adding dynamic priority weights; self-attention query is replaced with a double-channel query structure, one channel is used for cross attention, a cross attention query vector is generated through linear transformation, and key value pairs of atomic knowledge units are used; and step 3, training a cross attention adapter, freezing the weight of the language model, optimizing parameters of the adapter, and dynamically adjusting a loss function by using pre-judgment parameters based on input sequence context complexity. By means of the method, deep correlation description of user input and knowledge fragments can be achieved, semantic ambiguity is eliminated, knowledge injection and context information are balanced, distortion is avoided, and the strict requirement for semantic precision in the professional field is met.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention relates to the technical field of artificial intelligence dialogue systems, in particular to an intention correction and optimization method and system for multi-round dialogue context memory. According to the method, joint semantic coding is carried out on user input and historical dialogues, key semantic elements are extracted to construct an intention evolution relation graph, context consistency verification is carried out on an initial intention recognition result, and an intention correction candidate set is generated when conflicts are detected; and dynamically adjusting the context coding weight of the historical dialogue based on the corrected intention recognition result. According to the method, the intention recognition accuracy and context coherence in multiple rounds of conversations are effectively improved, and the semantic migration risk is reduced.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-agent traceable analysis method, device, equipment and medium, and the method comprises the steps: receiving a target theme and a data source list, collecting a multi-source document, and carrying out the preprocessing of the multi-source document to generate a preprocessing document set; configuring an analysis agent based on a semantic clustering result, and setting an analysis direction to form an analysis agent set; generating a structured note and index data table, and executing cross-document comparison to form an analysis output set; and receiving a feedback instruction to adjust the analysis agent set, triggering incremental processing to update the analysis output set, generating a theme research and judgment report, and keeping mapping consistency. According to the method, the multi-source document is fused through semantic clustering and a multi-agent cooperation mechanism, semantic association and traceable analysis are achieved, agent configuration is optimized in combination with interactive feedback, and the accuracy and the intelligent level of report generation are improved.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention relates to the technical field of artificial intelligence in the education industry, in particular to an intelligent composition quality evaluation method and system based on a large language model, and the method comprises the steps: carrying out the text normalization and semantic unit segmentation of a composition, extracting a semantic vector through a first large language model in combination with a context enhancement strategy, and positioning a semantic fracture risk position; recognizing composition core elements through a second large language model, and mapping the composition core elements back to the semantic unit sequence; constructing a demonstration logic diagram, extracting a core demonstration path and abstracting the core demonstration path into a logic role topological graph; in combination with a pre-constructed writing specification knowledge graph, comparing structural compliance, connection strength and an expected support relationship, identifying and demonstrating logic defects, and generating a global deduction item list; semantic clustering is carried out on illegal items to form an error label set, comprehensive weight is calculated in combination with historical data of students, and core weak items are positioned; according to the application, the logic analysis depth of intelligent evaluation of the argument is remarkably improved, and the pertinence and practicability of teaching feedback are improved.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

Disclosed herein, is a method and system for selecting a LLM for response generation to user queries. The method includes receiving a user query from a user device. The method includes determining, for the user query, a query type from a set of query types through a fine-tuned text classification model. The method includes retrieving a plurality of document embeddings based on the user query and the query type from a vector database through a semantic search technique. The method includes preparing a prompt using the user query and the plurality of document embeddings. The method includes inputting the prompt to an LLM selected from a set of LLMs based on the query type. The method includes generating, via the selected LLM, a response to the user query based on the prompt.

The invention relates to a forest fire knowledge modeling method based on named entity recognition and relation extraction, and the method specifically comprises the following steps: firstly collecting original text data in the field of forest fire, and constructing a text data set through preprocessing and entity and relation labeling; constructing a named entity recognition and relation extraction model, and realizing entity recognition and relation extraction of forest fire knowledge through multi-granularity adaptive word segmentation, dynamic standardization and pre-training language model encoder feature enhancement to obtain triple global confidence; adopting a multi-task joint training framework to calculate a loss function of the model, and performing end-to-end model parameter optimization on the model based on the loss function; inputting a new forest fire text into the trained model, and screening a high-confidence relation triple; and finally, constructing the structured forest fire field knowledge graph based on the extracted triple. According to the method, forest fire field knowledge can be efficiently excavated, and accurate knowledge support is provided for forest fire prevention and control and the like.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention discloses an information extraction data annotation and training system based on a large language model, and relates to the technical field of information extraction, and the system comprises a data set construction module which is used for constructing a pre-training data set and a fine tuning data set; the model continuous pre-training module is used for carrying out continuous pre-training on a preset general large language model based on the pre-training data set to generate a field adaptive pre-training model; the model fine tuning module is used for performing supervised fine tuning training on the domain adaptive pre-training model through a two-stage course learning strategy based on the fine tuning data set, and generating an information extraction model; the retrieval enhancement generation module is used for performing entity-semantic retrieval on an input text based on a preset knowledge base, outputting context information related to the input text, and outputting structured information of the input text based on the context information and an information extraction model, the problems of insufficient generalization ability, poor field adaptability and disastrous forgetting of a general large language model are solved, and the accuracy and robustness of information extraction are improved.

The invention relates to the technical field of document automatic processing, and discloses a bidding and tendering document generation and automatic review method and system based on a large language model.The method comprises the steps that firstly, a source heterogeneous constraint document is obtained and analyzed, and a structured semantic unit is obtained; secondly, based on the structured semantic unit, constructing a dynamic abstract semantic graph; then, based on the topological structure of the dynamic abstract semantic graph, controlling the large language model to generate contents node by node by adopting a multivariate constraint guide mode, and performing instant verification; when a user modifies the content, executing a bidirectional consistency propagation algorithm based on a graph, reversely mapping text change into node attribute update, and automatically detecting global logic conflicts; and finally, combining the final text of each node in the graph, and outputting a complete bidding and tendering file. According to the method, through structured modeling and bidirectional consistency maintenance, the accuracy, controllability and logic consistency of the generated content are improved, and automatic consistency maintenance of the whole life cycle of the document is realized.

The invention discloses a medical review and publication quality control system based on deep learning and medical knowledge, and relates to the technical field of medical information processing, and the system comprises a medical knowledge enhancement module which is used for carrying out the named entity recognition and relation extraction technology based on a multi-source fusion medical knowledge system, constructing a structured medical knowledge graph and carrying out intensive training; the multi-dimensional examination and analysis module is used for performing multi-dimensional evaluation on the manuscript based on the medical knowledge graph; the intelligent interaction and feedback module is used for displaying the evaluation result of each dimension through a visual interface; and the publishing quality management and control module is used for sequentially executing whole-process publishing supervision of standardized monitoring, quality inspection and abnormal intervention on the manuscripts passing the examination and analysis. The manuscript reviewing efficiency is greatly improved, and the requirements of high-frequency and rapid publishing of medical scientific research are met; the problem of review difference caused by subjective factors of a manuscript reviewer is solved, and the stability and reliability of the assessment result of the same manuscript are ensured through a standardized and quantitative assessment system.

The invention belongs to the technical field of nuclear power station file management, and particularly relates to an intelligent nuclear power station file classification and retrieval system based on artificial intelligence. The file acquisition module is responsible for uniformly acquiring and accessing multi-modal files in the nuclear power station; the preprocessing and natural language processing module is used for carrying out deep processing and semantic analysis on the collected multi-modal document; the dynamic classification module is used for carrying out multi-dimensional automatic classification on the multi-modal document based on a deep learning model and a nuclear power industry knowledge graph; the dynamic knowledge updating module is used for continuously monitoring new documents and user feedback and keeping adaptability to industry changes; the intelligent retrieval module is used for providing a query function for a user through semantic vectorization and a large-scale rapid retrieval technology; and the intelligent recommendation module is used for providing targeted document recommendation for the user through association analysis and collaborative filtering. According to the method, the problems of low classification efficiency, insufficient retrieval accuracy, weak dynamic adaptive capacity and limited multi-modal data processing capacity are solved.

The invention relates to the technical field of artificial intelligence multi-agents, in particular to a proposal review method and system based on evidence verification and multi-agent cooperation. A cross validation mechanism based on a review information pool is introduced. The cross validation mechanism is a multi-agent round table cooperation mechanism, and a preliminary review result generated by an agent is not directly output, but passes multiple rounds of mutual verification, supplementation and correction until a preset convergence condition is met. According to the evidence verification process, cognitive deviation and knowledge blind areas possibly existing in a single agent are effectively eliminated, and the omission ratio and the misjudgment rate of the review result are remarkably reduced, so that the accuracy, the integrity and the reliability of a final review report are ensured; the technical problems of low efficiency and low accuracy caused by insufficient review depth, single angle and lack of verification in the prior art are effectively solved, and the review efficiency and decision quality of enterprise project proposals are remarkably improved.

A system and computer-implemented method facilitate expansion of knowledge. The system allows for characterization of natural language documents and of search queries to locate those documents. A natural language processing (NLP) analyzer finds subject-verb-object (SVO) triplets in received text and assigns initial hierarchical classifications to word components of the SVO triplets. An SVO analyzer generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens. The tokens represent respective hierarchical classifications of the at least one hierarchical classification selected. The token stream produced may represent a natural language (NL) document to be stored to facilitate matching the NL document to a subsequently independently specified query. Alternatively, the token stream produced may represent a query and the token stream is used for generating a response to the query.

The invention discloses a model reasoning scheduling method and electronic equipment, and relates to the technical field of artificial intelligence, input information to be processed is acquired, and the actual length of the input information is determined, so that searching is performed in a pre-configured preset length set based on the actual length, and a length decomposition strategy matched with the actual length is obtained; the length decomposition strategy comprises a target combination of one or more preset lengths and is used for scheduling reasoning calculation of input information.

Various embodiments of the present disclosure provide prompt engineering and text quality assessment techniques for improving generative text outputs. The techniques include identifying a training cluster for an input document, generating a candidate prompt for a generative machine learning model based on the training cluster and a prompt template, providing the candidate prompt to the generative machine learning model to receive at least a portion of a candidate document, generating a plurality of quality metrics for the candidate prompt based on the candidate document, and selecting the candidate prompt from a plurality of candidate prompts based on the plurality of quality metrics.

The invention relates to the technical field of newborn medical big data risk assessment, and discloses a newborn disease risk assessment method based on big data analysis. The method comprises the following steps: acquiring an initial multi-modal health record of a newborn from a multi-heterogeneous medical data source; carrying out fusion analysis on the data, and constructing a risk assessment map driven by an event; starting a continuous risk tracking process, performing hierarchical risk scanning by using the map, and identifying a stable risk cluster, an evolution risk cluster and an isolated risk signal; deducing a hierarchical risk management and control plan based on the spatial distribution and time evolution mode of the risk cluster; at the end of each period, integrating latest clinical treatment feedback and monitoring readings, and carrying out reverse calibration on entity node states and relation edge strength in the atlas; and dynamically recombining a measure execution sequence and resource allocation in the plan according to the calibrated atlas. According to the invention, dynamic and continuous evaluation and self-adaptive intervention planning of the neonatal health risk are realized.

The invention relates to the technical field of building information, and provides a knowledge base construction and retrieval method and system based on a multi-source text in the building field, and the method comprises the following steps: a knowledge base construction stage: constructing a multi-dimensional metadata feature vector for a multivariate text based on a standard classification index table; the method comprises the following steps of: converting and segmenting a document, splicing an end clause with all superior title texts by utilizing a context inheritance algorithm to form a text unit with complete semantics, and performing dynamic filtering based on an analyzed query intention and a metadata vector at a user retrieval stage; then, in the screening set, performing fusion calculation on semantic vector similarity, keyword matching degree and authority offset weight based on effectiveness attribute and implementation time, and performing mixed retrieval and reordering on the text units; and finally selecting a text unit according to a sorting result and inputting the text unit into the large language model to generate answers. According to the method, high-precision and high-compliance intelligent retrieval and question answering of building domain knowledge are realized.

The invention relates to the technical field of literature processing, and discloses a literature review generation method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a research theme input by a user, and forming a reference literature set; according to the number of literatures in the reference literature set, a clustering strategy is selected, a structured review is generated based on a clustering result, and generation of the structured review comprises generation of alternative titles, construction of a multi-level outline, generation of research status paragraphs according to the outline and generation of brief reviews; receiving editing operation of a user on the outline, the research status paragraph and the brief review in the process of generating the structured review, and performing quality evaluation and optimization processing on the research status paragraph and the brief review; and when the current paragraph is researched to be generated, the reference literature is matched and the reference mark is inserted, a standard reference literature list is generated, and a traceability link from the in-text reference to the original literature is established. According to the method, literature review with a rigorous structure and coherent logic can be generated, and disordered stacking of information is avoided.

The invention provides a large language model-based finance and taxation data intelligent dialogue and query system, and relates to the technical field of finance and taxation data processing and natural language processing.The method comprises the following steps of: firstly, constructing a middle adaptation layer for connecting a large language model and a finance and taxation service database, and converting database information into a form which can be recognized by the large language model; and performing finance and taxation field special training on the large language model, so that the large language model can process finance and taxation problems of a user and generate a query instruction. And executing data query through the middle adaptation layer to obtain an original numerical result. The model is called to analyze the result to generate preliminary interpretation, and a finance and tax rule text retrieval library is combined for correction to form a final result and push the final result to the user. The finance and taxation data query efficiency and accuracy are improved, and comprehensive and professional finance and taxation information is provided for users.

Techniques for identifying a document layout are disclosed. In one embodiment, attribute data associated with an electronic document is accessed. A machine learning model is then applied to the attribute data. The machine learning model is configured to classify the electronic document based on the attribute data and feature sets of a plurality of document classes. Based on the document class predicted by the machine learning model, the system identifies a layout associated with the document class. The layout specifies layout elements and content types associated with the layout elements. The system extracts and stores information from the electronic document according to its content type.

The invention relates to the technical field of artificial intelligence intelligent agriculture, and discloses an agricultural legal question and answer intelligent interaction system based on a large model. According to the system, analysis and semantic anchor point marking are carried out on an unstructured legal document, and a knowledge unit with a hierarchical label is constructed. And fusing the units by using a large model to generate a context-associated knowledge graph. When a user inputs a question, the system retrieves related units from the map through multiple rounds of intention matching, and calls a large model to generate a multi-path answer sequence. And after the answer is subjected to legal logic and fact accuracy verification, the system dynamically optimizes the semantic association weight of the atlas according to a result, and finally outputs a reliable response. According to the scheme, deep structuring of agricultural legal knowledge and closed-loop optimization of a question and answer process are realized, and the accuracy and adaptive ability of professional consultation are improved.