55 results about "Knowledge transformation" patented technology

The invention discloses an automatic feasibility research report generation method fusing a knowledge graph and a large model, and relates to the technical field of leather inlaying, and the method comprises the following steps: extracting and cleaning information from multi-source heterogeneous data, and carrying out standardization processing on terminologies; constructing a knowledge graph, and converting the knowledge into embedded vectors which can be processed by a computer; according to user information, related sub-graph information is accurately retrieved from the knowledge graph, and cue words containing professional knowledge are dynamically combined; the large model generates a first draft according to cue words, a system automatically checks, and when conflicts are found, the model is required to be regenerated; and integrating the content which is verified to be correct with related standard terms, automatically adding a data reference source, and generating a document which is standard in format and clear in risk point. According to the method, real-time and automatic professional knowledge conformity checking and self-correction can be carried out on technical parameters and logic assertions output by the model in the generation process.

Embodiments are directed toward a blockchain system including a global state and an assemblage of blocks, each block representing a collection of state transformation records, each state transformation record describing a state transformation performed on the global state, where preceding blocks referenced by any given block contain state transformation records describing state transformations performed on the global state prior to the evaluation of the given block, and where at least one state transformation record is a zero-knowledge transformation record encoding a zero-knowledge state transformation description, which zero-knowledge transformation record comprises at least the following elements: paths identifying the locations of the elements of a discrete data subset, a revised data subset, and a transition proof implemented as a non-interactive zero-knowledge proof, which transition proof proves that the transition from the discrete data subset to the revised data subset follows the established rules of the blockchain system.

The invention relates to a knowledge retrieval enhancement method and system based on a biochemical knowledge graph. The method comprises the following steps: collecting biological and medical literatures and constructing the biochemical knowledge graph based on the collected literatures; obtaining a query text input by a user, performing biological and medical literature matching through the query text and the biochemical knowledge graph, and screening feature context information; and inputting the query text and the feature context information into a large language model, and performing answer generation based on knowledge graph enhancement. According to the method and the device, the unstructured knowledge in the original biomedical literature is converted into the structured graph which is used as a semantic enhancement basis of the large language model before the answer is generated, so that the correlation of the retrieval content and the accuracy of the generation result are improved.

The invention relates to the technical field related to medical systems, in particular to a medical teaching and research integrated system based on a common bottom layer, and aims to break through information islands among medical treatment, teaching and scientific research and realize closed-loop linkage and dynamic knowledge conversion of a whole process. The system comprises a diagnosis and treatment decision subsystem, a scientific research queue management subsystem and a teaching subsystem, integrates data through a three-in-one data engine, and realizes information sharing and cooperative work. The diagnosis and treatment decision subsystem provides diagnosis and treatment support for severe diseases based on an expert thinking chain. And the scientific research queue management subsystem is responsible for screening cases, sorting data, implementing quality control and statistical analysis, and adjusting an expert thinking chain according to scientific research results. And the teaching subsystem carries out teaching by using a standardized case presentation mode. Through a feedback closed loop of clinical discovery, scientific research verification, knowledge updating and teaching feedback, rapid propagation and application of medical knowledge are promoted, and the medical service quality and the medical education level are improved.

The invention relates to the technical field of artificial intelligence, and discloses an event prediction method and system based on multi-modal fusion, and the method comprises the steps: constructing a knowledge graph encoder, and converting domain expert knowledge into learnable vector representation; constructing a multi-granularity feature extraction network, and extracting features from different microcosmic, mesoscopic and macroscopic scales; realizing a knowledge-guided attention mechanism, and dynamically adjusting feature scale importance; constructing a prototype learning module, and establishing prototype representation of the abnormal category; a knowledge migration mechanism is constructed, and the generalization ability of the model to novel anomalies is enhanced; and multi-granularity abnormal event detection and early warning are realized, and a detection result and interpretable analysis are output. According to the method, through combination of knowledge guidance and multi-scale feature learning, efficient identification and early warning of social abnormal events under the condition of data scarcity are realized, and the method is suitable for the fields of public place safety monitoring, urban traffic safety management, large-scale activity safety guarantee and the like.

The invention relates to the field of data processing, in particular to a government affair field data query method and device based on multiple agents, and the method comprises the steps: receiving a user question, and carrying out intention recognition through a large language model to obtain a structured query intention; after intention recognition fails, a supplementary question request is initiated by using a clarification mechanism, and a structured query intention is acquired based on the supplementary question request; obtaining priori knowledge, converting the priori knowledge into priori knowledge embedded vectors, and summarizing the priori knowledge embedded vectors into a vector index; searching a plurality of pieces of reference data and database information corresponding to the question rewritten by the user according to the question rewritten by the user; querying the corresponding data table by using the large language model, and generating a complementation prompt word of the SQL query statement by using the prompt word template; and screening out a target SQL query statement according to the completion prompt word, obtaining a query result, and returning the query result to the user. According to the method and the device, the SQL query statement generation accuracy can be improved.

The invention discloses a power report generation method, system and equipment based on adaptive learning and a medium, which are applied to the field of power reports, and comprise the following steps: converting professional power knowledge into prompt lexical elements by utilizing a prompt technology driven by power knowledge, and taking the prompt lexical elements as context information; through a cross-modal feature enhancement mechanism, searching historical report fragments similar to the current equipment state from a historical fault report database, and fusing the historical report fragments with the preprocessed power operation data to generate enhanced feature representation; the enhanced feature representation and prompt lexical elements are input into a report generation model, a self-adaptive reflection learning loss function is adopted, the loss weight is dynamically adjusted according to the learning state of the data category in the training process, and the report generation model is optimized; and generating a power field equipment inspection report according to the optimized model. According to the invention, the professionality and the accuracy of report contents are improved, and the intelligent level and the practicability of the power report generation system are enhanced.

Embodiments are directed toward a blockchain system including a global state and an assemblage of blocks, each block representing a collection of state transformation records, each state transformation record describing a state transformation performed on the global state, where preceding blocks referenced by any given block contain state transformation records describing state transformations performed on the global state prior to the evaluation of the given block, and where at least one state transformation record is a zero-knowledge transformation record encoding a zero-knowledge state transformation description, which zero-knowledge transformation record comprises at least the following elements: paths identifying the locations of the elements of a discrete data subset, a revised data subset, and a transition proof implemented as a non-interactive zero-knowledge proof, which transition proof proves that the transition from the discrete data subset to the revised data subset follows the established rules of the blockchain system.

The invention relates to the technical field of employee training, and discloses an artificial intelligence-based employee training assessment method, device and equipment, and the method comprises the steps: obtaining the basic data, work behavior data and historical training data of an employee, carrying out the fusion of all data, and constructing a multi-dimensional employee portrait; based on the performance goals, determining training goals of the employees, and generating personalized training contents; a knowledge system is constructed according to personalized training content, real-time knowledge recommendation is performed in actual work, and a practice result is recorded; generating personalized assessment content for intelligent assessment to obtain an assessment result; and evaluating the employee training effect according to the practice result and the assessment result. The employee portrait is constructed through multi-source data fusion, accurate mapping from performance goals to personalized training is achieved, the matching degree of training content and employee ability short boards is improved, the knowledge conversion efficiency is enhanced, closed-loop management is formed in combination with real-time knowledge recommendation and scene practice, and the talent training efficiency is improved.

The invention relates to the technical field of image data processing, in particular to an oral cavity knowledge graph construction method and device, equipment and a storage medium. Preprocessing the oral CT original sectional image data based on a preset adaptive threshold method and a preset rigid registration algorithm to obtain standard image data; performing feature extraction on the standard image data based on a preset deep learning model and a preset principal component analysis method to obtain a structured feature vector; constructing the structured feature vector, the structured data and the unstructured data according to a preset entity linking method, a preset inference rule and a preset graph neural network model to obtain an optimized knowledge graph; an optimized knowledge graph is constructed through CT image preprocessing, deep learning feature extraction and multi-source data fusion, an image-to-structured knowledge conversion link is realized, and a comprehensive, reliable and convenient knowledge data support is provided for precise diagnosis and treatment of oral diseases.

The invention belongs to the technical field of network security, and discloses a construction method of a network security knowledge graph based on SecureBERTPlus, and the method comprises the steps: achieving the knowledge conversion of threat intelligence through the cooperative processing of a data preprocessing module, a semantic information extraction module and a knowledge graph construction and optimization module; and a complete knowledge engineering link from original data to decision support is formed. According to the construction method adopted by the invention, synchronous optimization of entity recognition and relation extraction is realized through a joint extraction framework; semantic features of terms in the network security field are accurately captured through a field-adaptive SecureBERTPlus pre-training model, time sequence dependence in an attack chain is modeled by using a BiLSTM layer, a cross-sentence key relationship is focused by means of an attention mechanism, and ATTamp is injected based on a CRF layer; a CK rule constrains a decoding process, and a hierarchical clustering HAC based on context embedding is particularly introduced to realize entity dynamic disambiguation and semantic fusion.

The invention relates to a program design aided teaching agent system based on retrieval enhancement generation, and aims to solve the problems of knowledge fragmentation, low retrieval precision, deviation of generated content from teaching materials, insufficient teaching guidance and the like of a traditional teaching system. A knowledge unit construction technology combining hierarchical semantic segmentation and position coding, a precise matching mechanism combining two-stage hybrid retrieval and cross encoder reordering, a structured problem-document splicing fidelity technology and a multi-round progressive multi-round guide strategy based on a CDIO concept are adopted. Knowledge unit division with complete semantics is realized through an HAC-BERT + KMeans algorithm (the accuracy is improved by 28%), BM25 and dense retrieval weighted fusion (the weight is 0.4: 0.6) is combined with cross encoder reordering (NDCG at10 reaches 0.735), textbook format information (the content reuse rate is 87%) is reserved through structured splicing, and finally a complete technical scheme from knowledge organization and accurate retrieval to progressive teaching guidance is formed. And the intellectualization level and the knowledge conversion efficiency of program design teaching are obviously improved.

The invention relates to the technical field of electric digital data processing, and discloses a method for detecting log abnormity of an electric power dispatching automation system, which comprises the following steps of: constructing a fixed depth analysis tree through historical log data, and extracting a log template based on word segmentation similarity; converting knowledge in the power dispatching field into vectors and storing the vectors into a knowledge base; retrieving knowledge base associated domain knowledge of the log template, inputting the large model to carry out abnormity judgment and marking a template label; after real-time log preprocessing, a template label is inherited through a parse tree matching template library and a self-adaptive threshold strategy, and online template updating is triggered for unmatched logs; and the context and high-frequency parameters of the abnormal log are aggregated, a dynamic cue word is constructed in combination with a retrieval result and a template feature weight, a large model is input for multi-dimensional root cause analysis, and template mechanism optimization is driven based on the cue word. The problems that in the prior art, manual maintenance is difficult, the generalization ability is weak, and the detection speed is low are solved, and the purposes of efficient detection, high accuracy and self-adaption are achieved.

The invention provides a question answer determination method and device, electronic equipment and a computer storage medium, after query information of a user is received, knowledge fragments related to the query information are extracted from a knowledge base; for each knowledge fragment, extracting a first-order logic rule corresponding to the knowledge fragment from a formalized logic rule base; then, constructing a constraint graph according to all the first-order logic rules; and finally, inputting query information of a user into the language model, outputting to obtain a candidate answer set, converting knowledge into a formalized logic rule, and directly utilizing a constraint graph to constrain a decoding process in a language model generation stage, so that high-accuracy answer generation is realized.

The application provides a gas intelligent customer service response method, and relates to the technical field of gas intelligent customer service, and comprises the following steps: receiving a query request input by a user, retrieving structured data and / or unstructured data corresponding to the query request from a gas industry knowledge base, and generating retrieval enhanced data; the gas industry knowledge base comprises gas user information, gas equipment information, gas safety specifications and operation processes; based on the query request and the retrieval enhanced data, a corresponding natural language reply is generated, and the natural language reply is output to a user terminal. The application retrieves the gas industry knowledge base, including safety specifications, equipment information and operation processes, converts structured and unstructured knowledge into retrieval enhanced data, realizes deep integration of the knowledge base, solves the problem of inconsistent decision-making caused by the dependence on manual experience reply after the existing system reads the query request, and realizes machine intelligent response.

The invention provides a fuel gas intelligent customer service response method, and relates to the technical field of fuel gas intelligent customer service, and the method comprises the following steps: receiving a query request input by a user, retrieving structured data and / or unstructured data corresponding to the query request from a fuel gas industry knowledge base, and generating retrieval enhanced data; the fuel gas industry knowledge base comprises fuel gas user information, fuel gas equipment information, fuel gas safety specifications and operation processes; and generating a corresponding natural language reply based on the query request and the retrieval enhancement data, and outputting the natural language reply to the user terminal. According to the method, by retrieving the fuel gas industry knowledge base, including safety specifications, equipment information and operation processes, structured and unstructured knowledge is converted into retrieval enhanced data, deep integration of the knowledge base is achieved, the problem that decision making is inconsistent due to the fact that an existing system relies on manual experience reply after reading a query request is solved, and machine intelligent response is achieved.

This invention discloses a system for converting tacit knowledge into explicit knowledge based on knowledge graphs and large models. It includes a knowledge system ontology modeling and management module for constructing and maintaining the knowledge system ontology model in large model applications; a data access and knowledge graph construction module for mapping and loading multi-source heterogeneous data onto the knowledge system ontology; an ontology virtual business environment engine module; a tacit knowledge mining and explicit knowledge generation module that, in a virtual environment, uses graph analysis and large model reasoning to mine tacit knowledge and generate explicit knowledge; and a business strategy planning and workflow orchestration module that transforms explicit knowledge into executable business strategy plans and workflow orchestrations. Through the knowledge system ontology modeling and management module, a systematic knowledge framework is constructed, achieving systematic accumulation of enterprise tacit knowledge, effectively preventing knowledge loss, and improving knowledge reusability and scalability.

This invention relates to a fully localized hybrid retrieval and knowledge graph construction method and system for standard gas R&D and production, belonging to the interdisciplinary field of artificial intelligence and industrial control. The method includes: acquiring multi-source heterogeneous documents and performing text parsing, mixed Chinese and English word segmentation, and terminology standardization; employing a hybrid retrieval strategy that weights and fuses BM25 keyword retrieval and vector semantic retrieval to obtain fused retrieval results; automatically extracting entities and relations from the fused retrieval results to generate triplet data; constructing a knowledge graph based on the triplet data, with nodes bearing source document identifiers and version tags; and deploying the embedded model, text units, semantic vectors, and knowledge graph on a local server. This invention achieves the fusion of precise matching and semantic recall, ensures core data security, transforms unstructured knowledge into a computable and traceable knowledge network, and significantly improves the efficiency and intelligence level of knowledge retrieval in standard gas R&D and production.

This invention provides a construction AI-based risk analysis and decision-making method for safety management. Through a knowledge base construction mechanism involving intelligent parsing, vectorized storage, and incremental updates, it transforms scattered safety knowledge from projects into digital assets, enabling the continuous accumulation of enterprise safety knowledge assets. A RAG-enhanced large language model question-answering system equips each on-site personnel with a personal AI expert, providing near-instantaneous responses to on-site issues and improving efficiency. By employing a performance quantitative analysis model, an Attention-LSTM time-series risk prediction model, and a hazard clustering analysis algorithm, it uncovers management shortcomings and predicts risk trends from massive amounts of data, making safety management decisions more data-driven and moving away from experience-based, extensive management. AI time-series prediction identifies high-risk areas and hazard trends seven days in advance, generating actionable control recommendations, shifting safety management from post-event remediation to pre-event prevention, significantly reducing the probability of accidents.

This invention relates to the field of electrical digital data processing technology and discloses a method for detecting log anomalies in power dispatch automation systems. The method includes: constructing a fixed-depth parse tree using historical log data and extracting log templates based on word segmentation similarity; converting power dispatch domain knowledge into vectors and storing them in a knowledge base; retrieving knowledge related to the log templates from the knowledge base, inputting it into a large model for anomaly detection, and labeling the templates; after real-time log preprocessing, inheriting template labels by matching the template library with the parse tree and using an adaptive threshold strategy, triggering online template updates for unmatched logs; aggregating the context and high-frequency parameters of the abnormal logs, constructing dynamic prompt words based on the retrieval results and template feature weights, inputting them into a large model for multi-dimensional root cause analysis, and optimizing the template mechanism based on the prompt words. This method solves the problems of difficult manual maintenance, weak generalization ability, and slow detection speed in existing technologies, achieving efficient detection, high accuracy, and adaptability.

The invention belongs to the technical field of artificial intelligence, discloses an expert knowledge and data fused intelligent aid decision-making model training method, and aims to solve the problem that expert knowledge and data are difficult to synergistically utilize by an intelligent model, the expert knowledge is converted into a feature and strategy bound decision-making sequence, so that the expert knowledge becomes a data sample; a way is provided for utilizing expert knowledge; a business target state is adopted to evaluate the strategy effect, the expert sample weight and the parameter learning rate are dynamically adjusted according to the model training process, the expert knowledge utilization effect is improved, and meanwhile, new expert samples are dynamically injected in a model evaluation mode; a sample fusion matching mode is adopted to eliminate redundancy of expert samples and data samples, the model training process is accelerated, a sample cluster with a small number of samples is identified through a density clustering mode, the number of similar samples is increased by taking the sample cluster as a starting point, and the robustness of an auxiliary decision-making model is enhanced through continuous improvement of sample richness.

The invention discloses a laser cutting equipment after-sales intelligent training method and system based on a knowledge graph, and the method comprises the steps: constructing a laser cutting equipment after-sales knowledge graph fusing quantifiable parameters, and converting fragmented maintenance knowledge into a structured system; generating a systematic training content library based on the knowledge graph; initial training content is distributed according to post attributes of students, and a digital user portrait is constructed by collecting interaction data; dynamically matching the user portrait with a training content library to calculate an adaptation degree; and screening personalized training content for the trainee according to the adaptation degree, and continuously optimizing a user portrait and matching logic based on feedback data. The technical problems that in the prior art, training knowledge is fragmented, trainee ability evaluation is fuzzy, and training content lacks personalized adaptation are solved.

The invention provides a distribution network fault positioning method. By constructing a combined reasoning model of a knowledge graph model and an expert system model, accurate positioning of a distribution network fault is realized. The knowledge graph expresses an electrical relationship between equipment nodes in a triple form, and converts a dispersed equipment topology into a structured and visual network relationship to provide bottom support for fault path tracing; the expert system model integrates a knowledge base and an inference module, converts expert knowledge such as historical fault cases and equipment behavior rules into executable logic inference rules, and ensures that fault diagnosis conforms to actual operation experience. A target fault area is quickly locked based on topological constraints, a fault node candidate set is reasoned in the area through rule matching, target nodes are screened in combination with the similarity of fault events and reference events, precise positioning from local to concrete is achieved, clear orientation is provided for operation and maintenance, and the positioning efficiency and accuracy are remarkably improved.

This invention relates to the field of medical dispute resolution and discloses an AI-powered virtual third-party neutral evaluation and mediation method and system for medical disputes. The AI-powered virtual third-party neutral evaluation and mediation method includes: accepting case details, treatment process, and the demands of both parties from medical institutions; initiating privacy data desensitization verification to remove all personal identity information; calling upon a preset virtual third-party neutral evaluation role to conduct an objective comparative analysis of the treatment behaviors involved in the dispute, in conjunction with clinical treatment guidelines; extracting the core points of contention between the doctor and patient, transforming professional medical knowledge into easily understandable content, and simultaneously generating multiple versions of on-site prepared statements; generating a standardized evaluation opinion with a compliance disclaimer for use by medical institutions in on-site mediation; and archiving the dispute resolution process without privacy protection to complete the closed-loop dispute resolution process. This invention has the advantage of improving the efficiency of dispute resolution.

The invention relates to the technical field of large model adjustment, and discloses a drawing large model adjustment method and system based on multi-modal knowledge driving, and the method comprises the steps: firstly obtaining a text prompt inputted by a user, carrying out the deep analysis of the text prompt, and extracting the structural representation of the intention of the user; then, based on the structured intention, external multi-modal knowledge is actively retrieved and integrated, so that richer and more accurate semantic information and factual basis are introduced for the generation process. The rich multi-modal knowledge can then be converted into refined attention modulation parameters using a knowledge-attention translation module. The parameters directly and dynamically influence the working mode of an attention mechanism in the large drawing model, and knowledge-driven fine-grained feature generation control is realized. Therefore, the complex user intention can be more accurately understood, so that the image which is highly consistent with the external knowledge and more accurate in details is generated, and the quality and controllability of the generated image are greatly improved.

The invention provides an LLM-based human resource intelligent training method and system, and the method comprises the steps: obtaining historical employee training information, and carrying out the preliminary processing, so as to train a first LLM training model; obtaining enterprise training demand information, outputting a training knowledge base according to the first LLM training model, training employees according to the training knowledge base, and obtaining a first training result; calculating a training matching rate according to a training result, and performing specific fine adjustment on the first LLM training model to obtain an enterprise adjustment model; calculating a staff knowledge conversion rate, obtaining staff knowledge short board information, setting a training vector according to the staff knowledge short board information, performing secondary specific fine adjustment on the first LLM training model through the training vector, and obtaining a staff adjustment model. Historical data utilization and model training and fine tuning are carried out to finally meet the double requirements of enterprises and employees.

The invention relates to the technical field of agricultural data analysis and mining, in particular to an agricultural data assembly line type intelligent analysis and mining system, which adopts the technical scheme that automatic construction and execution of an agricultural data analysis process are realized through an intelligent engine driven by metadata and guided by knowledge, and the system can intelligently recommend analysis operators and dynamically assemble an assembly line. And an analysis path is adaptively adjusted based on data state change, and finally automatic conversion from original data to knowledge discovery is realized. Deep fusion of domain knowledge and data driving is realized, agricultural professional knowledge is converted into a form which can be understood by a machine through semantic tags and ability portraits, an SC-MPARank hybrid sorting model is provided, advantages of symbol constraints and a neural network are combined, interpretable intelligent recommendation is provided, a dynamic and evolvable analysis pipeline is constructed, and the analysis efficiency is improved. The recommendation effect is continuously optimized through a feedback learning closed loop, a decoupling and extensible system architecture is designed, and a new operator can be conveniently integrated into the system.

This invention discloses a project compliance audit method and system based on collaborative reasoning using an expert knowledge base and a large model. First, a large model is used to analyze a collection of laws and regulations and audit practice documents, automatically extracting compliance audit-related knowledge. Then, this compliance audit-related knowledge is used to construct an expert knowledge base comprising a legal and regulatory knowledge base, an audit case library, an audit experience library, an audit practice library, and a compliance audit rule library. This invention achieves the function of automatically constructing and dynamically expanding the expert knowledge base by extracting legal and regulatory knowledge and audit knowledge using a large model. Furthermore, the reliability of the expert knowledge base and factual data is ensured through a confidence level mechanism and human-machine collaborative verification. Simultaneously, the use of audit evidence traceability chain information enables full-link traceability from audit conclusions to original evidence, greatly improving knowledge conversion efficiency and enhancing the interpretability and reliability of compliance audits, making it suitable for widespread promotion and use.

The invention relates to the technical field of education, and discloses a multi-sensory linkage educational digital oil painting system which comprises a knowledge visualization sketch layer, an intelligent auxiliary terminal layer and a multi-sensory linkage assembly layer. The knowledge visualization sketch layer is used for presenting a digital oil painting sketch fused with educational information, and a color theory mark, a stroke skill prompt and a famous painting background knowledge pop-up window are embedded in the sketch; and the intelligent auxiliary terminal layer comprises a touch operation module, a data processing module and an educational resource library. In the invention, the knowledge visualization sketch layer is embedded with education information such as a color theory and stroke skills through dynamic labeling, audio explanation, tactile feedback and scene smell of the multi-sensory linkage component layer are combined, a hippocampus memory area is activated by relying on a right brain image memory advantage and an alpha wave regulation and control technology, and multi-sensory synchronous input is combined, so that a hippocampus memory area is activated. A knowledge conversion path conforming to the Ebbinghaus multi-channel memory model is constructed, and the memory retention rate is remarkably improved.