54 results about "Knowledge utilization" patented technology

The invention discloses a federal large model knowledge collaborative training method supporting multi-modal heterogeneous clients, which comprises the following steps: each client receives a model initialization parameter issued by a central server, and applies adaptive differential privacy noise to independently train a heterogeneous lightweight model based on local private data; updating the model to which the noise is applied and uploading a modal identifier of the model to a central server side; after model updating and modal identification of each client are received, based on a modal perception weighted consensus fusion mechanism, knowledge of each client is fused to update a global large model; and the central server side issues the updated presentation layer parameters of the global large model to the client side for initialization of the next round of local training. According to the method, on the premise that a public data set or specific task setting is not needed, comprehensive compatibility of data isomerism, client dynamic participation, model diversity and privacy protection requirements is achieved, and the adaptability, stability and knowledge utilization efficiency of large model federation training are remarkably improved.

The invention relates to a knowledge graph enhanced multi-modal file retrieval method, and belongs to the field of artificial intelligence and multi-modal information retrieval. The invention aims to solve the problems of multi-modal information semantic segmentation, weak semantic reasoning ability and low semantic matching precision in the existing multi-modal archive resource retrieval process. Through four stages of archive multi-modal data preprocessing and feature extraction, knowledge graph construction and enhancement, semantic retrieval request analysis and intention modeling, and multi-modal semantic matching and sorting, a semantic relationship is enhanced by utilizing a knowledge graph, so that the semantic relevancy and context consistency of a retrieval result are remarkably improved; a user is allowed to input and inquire in various forms such as texts, images and voices, and semantic extension retrieval is supported. According to the method, more efficient and accurate archive resource retrieval can be realized, and the user retrieval experience and archive knowledge utilization are improved.

The invention discloses a pollutant toxicity assessment method and device based on RAG enhancement and knowledge graph reasoning, equipment and a medium, and relates to the technical field of pollutant assessment, and the method comprises the steps: constructing a standardized toxicological knowledge graph through multi-source data collection and preprocessing; by combining RAG enhanced retrieval and knowledge graph reasoning, a toxicity reasoning path and an evaluation index are rapidly generated, meanwhile, interpretable content is provided, multi-source heterogeneous data fusion and self-adaption to spectral characteristic differences of different sites are achieved, the problems that traditional evaluation is low in efficiency, high in cost, insufficient in knowledge utilization and the like are effectively solved, rapid and accurate evaluation is achieved, and the evaluation efficiency is improved. And the credibility and the interpretability of the evaluation result are enhanced.

The invention relates to a knowledge graph construction and retrieval optimization method based on water affair data, and the method comprises the steps: taking the unified knowledge expression of a water affair object and an event thereof as a target, determining a water affair business field and business range, and constructing a water affair knowledge graph conceptual model covering a static knowledge ontology and a dynamic event ontology; performing knowledge extraction on the obtained multi-source heterogeneous water affair data, converting the data into a unified triple form, and constructing a data layer of a water affair knowledge graph; and on the basis of the data layer, node, path and region query is performed on the knowledge graph by adopting a graph algorithm, and semantic enhancement retrieval and reasoning are performed in combination with a large language model. By constructing the water affair knowledge graph and introducing knowledge extraction and retrieval enhancement capability of a large language model, dynamic modeling and semantic reasoning of water affair objects, events and relationships thereof are realized, high-risk area identification, emergency path planning and emergency early warning are supported, and the knowledge utilization rate and intelligent decision level in the water affair field are improved.

The invention relates to the technical field of big language model reasoning, and provides a robust retrieval enhancement method and device oriented to a big language model. According to the method, semantic analysis is carried out on user query in a real scene, a key entity is positioned, then a core entity is taken as an initial retrieval point, a credible iterative retrieval strategy is executed, associated information in a knowledge graph is obtained step by step, factual verification is carried out, the limitation of a traditional RAG framework on the key entity in the real scene is broken through, and the reliability of the key entity in the real scene is improved. Robust reasoning and knowledge utilization of the LLM under the conditions that reliable predefined associated entities are lacked and query semantics are complex are achieved, and robustness and fact consistency of the model in a complex knowledge graph question and answer task are improved.

The invention discloses a retrieval enhancement generation method and system based on long-term memory and multi-source knowledge iterative fusion. The method comprises the following steps: firstly, after user inquiry is received, triggering long-term memory database retrieval, external knowledge base retrieval and large language model internal knowledge generation in parallel to construct a multi-source candidate knowledge base, and improving result correlation by adopting a self-adaptive retrieval method based on a dynamic top-k value; secondly, inputting user query and multi-source knowledge into the large language model for iterative fusion, and generating a final answer through conflict detection, fusion processing and confidence evaluation; and finally updating the long-term memory database. The method can improve the accuracy and stability of the generated answers in a multi-round interaction and complex task scene, reduces the risk of answer one-sidedness, inference incompleteness or fact deviation, enhances the multi-source knowledge utilization ability and long-term learning ability of the system, and improves the user experience. Therefore, the comprehensive performance and reliability of the system in applications such as intelligent question answering, information retrieval assistance and text generation are obviously expanded.

The invention discloses a vertical domain intelligence analysis method based on a large language model. The method comprises the following steps: receiving a natural language analysis demand of a user; based on pre-constructed vertical domain specific task planning knowledge, intelligently decomposing the demand into a subtask list corresponding to a preset dimension by using a large language model, and semantically matching a proper information acquisition tool for each subtask to generate a structured task plan; the system schedules and executes tool sets of network search, webpage capture, internal query and the like according to a plan, and performs cross verification and cleaning on collected fragmented information; and inputting the verified data into a large language model for integration analysis, and optionally carrying out multi-round iteration self-evaluation optimization to automatically generate a high-quality vertical domain intelligence analysis report. According to the method, full-process and high-intelligence automatic processing of information analysis in the vertical field from demand understanding to report generation is realized, and the method has excellent planning capability, process interpretability, result reliability and excellent modular expansibility.

The invention provides an autonomous vulnerability analysis system and method based on knowledge graph guidance and hierarchical verification, and relates to the technical field of automatic software vulnerability analysis, and the method comprises the steps: constructing a structured vulnerability knowledge graph by using unstructured historical vulnerability cases; obtaining a to-be-analyzed project, inputting the to-be-analyzed project into the large language model, taking the structured vulnerability knowledge graph as priori knowledge, and performing information retrieval by using a macroscopic mode and statistical evidence in the structured vulnerability knowledge graph; guiding a large language model to identify a pollution source and a risk pool based on a retrieval condition, and driving a static analysis engine to generate a preliminary suspected path; and screening the suspected paths through a two-stage grading review mechanism, finally outputting to obtain a high-credibility vulnerability, and generating a final high-credibility vulnerability report. The method is based on statistical law inference, so that the false alarm rate can be greatly reduced, and the discovery precision of real vulnerabilities is remarkably improved.

The application discloses a pollution toxicity evaluation method and device based on RAG enhancement and knowledge graph reasoning, equipment and medium, relates to the technical field of pollution evaluation, and comprises the following steps: constructing a standardized toxicology knowledge graph through multi-source data acquisition and preprocessing. Combined with RAG enhanced retrieval and knowledge graph reasoning, the toxicity reasoning path and evaluation index are quickly generated, the explainability content is provided, the multi-source heterogeneous data fusion and the adaptive difference of different site spectrum characteristics are realized, the problems of low efficiency, high cost, insufficient knowledge utilization and the like of the traditional evaluation are effectively solved, the rapid and accurate evaluation is realized, and the credibility and explainability of the evaluation result are enhanced.

The application discloses a kind of based on measurement and knowledge water surface unmanned system operating state early warning method, belong to unmanned system safety monitoring technical field.To solve the problem of insufficient knowledge utilization, poor dynamic adaptability of existing early warning method, the method of the application includes: collecting position, attitude, ship body state and environment multi-source time series data, and obtaining text form expert knowledge;Adopt sliding window and standardization preprocessing data;Expert knowledge semantic vector is extracted using BERT model;Through Crossformer model coding time series features, knowledge vector is spliced and fused, and system state features are output;Risk membership degree is calculated based on fuzzy set theory, and early warning is triggered according to the maximum membership principle.The application realizes the deep integration of measurement data and expert knowledge, improves the early warning accuracy, robustness and generalization ability.

The invention discloses a knowledge management method and system based on document similarity detection and a multi-modal knowledge base. The method comprises the steps of obtaining a target document; when a target document is put in storage, a similarity detection method based on a resource self-adaption mechanism is adopted, and existing documents are screened out from the multi-modal knowledge base to serve as a candidate document set; a similarity detection method based on a precision and efficiency optimal mechanism is adopted, and the candidate document set is used for conducting duplicate removal on the target document or storing the target document which is judged to be a new document; obtaining a question asked by the user; based on the multi-mode knowledge base subjected to duplicate removal or updating, multi-path mixed retrieval is conducted on the problem, a multi-path mixed retrieval result is subjected to two-stage sorting to obtain a rearrangement result, reference data are selected from the rearrangement result, the problem, cue words, context and the reference data are assembled into a large model input instruction according to a preset cue word template, and the large model input instruction is input into the multi-mode knowledge base. An answer generated by the large model is obtained and returned to the user; and comprehensive improvement of the knowledge utilization rate, the retrieval precision and the efficiency is realized.

This invention provides a sepsis consultation method and system based on multi-turn dialogue, belonging to the field of intelligent consultation equipment technology. This invention achieves effective integration of standardized medical knowledge and personalized clinical experience through the construction of a two-dimensional knowledge graph and cross-graph interactive retrieval, improving the accuracy and reliability of sepsis diagnosis and treatment recommendations. Through structured sepsis diagnosis and treatment chain constraints and a confidence-driven dynamic strategy, it alleviates the illusion problem of large language models in complex clinical reasoning, ensuring that the generated content conforms to clinical diagnosis and treatment logic norms. Through semantic anchor guidance, hierarchical activation, and knowledge feedback mechanisms, it achieves multi-granular semantic association from macro-clinical scenarios to micro-medical concepts, balancing retrieval accuracy and knowledge coverage. The confidence evaluation system comprehensively considers knowledge sufficiency, redundancy, and consistency, adaptively balancing knowledge utilization and information exploration, improving the reasoning efficiency and diagnosis and treatment quality of multi-turn dialogue.

The disclosure provides a sequence retrieval and classification method based on domain rule knowledge and data. It includes a key text extraction module, a specification and chapter structure tree module, and a loss correction module. The key text extraction module inputs the external text information after artificial review and the text knowledge in the domain rule knowledge specification and chapter, and obtains the key text sequence of each document. The specification and chapter structure tree module inputs the text knowledge in the domain rule knowledge specification and chapter, and uses the inherent hierarchical structure of the specification and chapter to which the document belongs to guide the retrieval process. The loss correction module calculates the loss by comparing the difference between the predicted document identifier and the true labeled document identifier. This algorithm uniformly minimizes the difference between the predicted and labeled classification / retrieval document identifiers, improving the accuracy and consistency of the two tasks.

The invention discloses a mechanism knowledge base intelligent classification and indexing system and device based on multi-modal data fusion, and aims at solving the problems that when a traditional knowledge base processes multi-modal data, the efficiency is low, the classification precision is insufficient, the indexing quality is poor, and non-text resource processing is short. According to the system, through a feature extraction technology, features such as colors and textures are extracted for images, features such as keywords and themes are extracted for texts, and multi-type data such as texts, images and videos are converted into uniform feature vectors. A classification model is constructed by combining an SVM, a CNN and a Transform, and an attention mechanism is introduced to realize adaptive fusion. Based on a classification result, through automatic indexing such as keyword extraction and topic modeling, resources are associated through a knowledge graph. Experiments prove that the processing time of the device is shortened to 1 / 20 of that of an original method, the classification accuracy is improved from 30% to 90% or above, the knowledge base management efficiency and service quality are effectively improved, and support is provided for knowledge utilization and decision making.

The invention discloses an operation and maintenance management method, system and device based on artificial intelligence, and a medium, mainly relates to the technical field of operation and maintenance management, and is used for solving the problems of insufficient correlation analysis, low knowledge utilization efficiency and lack of self-healing ability in an existing scheme. Comprising the following steps: when a preset alarm self-healing processing program runs abnormally, progressively decreasing a quality evaluation value of a preset maintenance document set corresponding to the preset alarm self-healing processing program according to a first preset numerical value, adding the target alarm item, the equipment node corresponding to the target alarm item, the maintenance capability level, the quality evaluation value corresponding to the target maintenance document set and the maintenance explanation information to a preset manual maintenance management interface, and deleting the target alarm item, the equipment node corresponding to the target alarm item, the maintenance capability level, the quality evaluation value corresponding to the target maintenance document set and the maintenance explanation information from a preset automatic operation and maintenance management interface; and when the target maintenance document set does not exist, directly adding the target alarm item and the corresponding equipment node to a preset manual maintenance management interface.

A method and system for extracting and organizing knowledge from historically accumulated data using AI assistance includes the following steps: unified preprocessing of multi-source heterogeneous aviation maintenance data to output unified JSON structured data; inputting the structured data into a large language model fine-tuned from aviation maintenance corpus for knowledge triple extraction; constructing a multimodal interaction and query interface supporting structured queries, natural language question answering, historical case retrieval, and maintenance solution recommendations; and establishing an incremental update mechanism to update newly submitted repair reports and batch-uploaded documents to the knowledge graph after preprocessing, extraction, and manual review. This invention effectively solves the problems of difficult standardization of multi-source heterogeneous aviation maintenance data, low reliability of knowledge extraction from large language models, and limited query and interaction methods, achieving efficient knowledge utilization and intelligent knowledge services from historical maintenance data.

This invention relates to the field of engineering design technology, and in particular to a method and system for auxiliary design of nuclear power plant engineering modifications. A method for auxiliary design of nuclear power plant engineering modifications includes: acquiring multi-format design documents and structuring the content of these documents to obtain structured data with consistent format; establishing a hybrid engine using a first language model and preset rules; extracting nuclear power plant engineering documents using the hybrid engine to construct a tag library; segmenting the structured data and saving it in association with the tag library to construct a nuclear power knowledge base; training a second language model based on the nuclear power knowledge base to establish a domain-specific vertical model; acquiring design instructions and generating auxiliary information using the domain-specific vertical model. This invention significantly improves knowledge utilization by leveraging structured data and a nuclear power knowledge base; ensures professionalism and accuracy through the combination of a vertical domain model and the nuclear power knowledge base; and creates an intelligent auxiliary design process using design instructions, improving design efficiency and document quality.

The invention relates to the technical field of knowledge management and artificial intelligence, and discloses a knowledge management system based on artificial intelligence big data. The system comprises knowledge flow capture, symbol reasoning, neural embedding, strategy synthesis and strategy deployment components. The system generates structured knowledge through multi-modal data acquisition and cleaning; performing symbolic reasoning by utilizing predicate logic and a rule base, and constructing a symbolic knowledge graph; a deep learning model is adopted to generate low-dimensional vector representation; fusing the symbolized knowledge graph and the vector representation, and generating a knowledge management strategy through a strategy gradient algorithm; and asynchronously executing the strategy in the cloud environment. According to the scheme, through combination of symbol reasoning and a neural network technology, interpretability and logic preciseness of a knowledge processing process are ensured, and meanwhile, the dynamic adaptive capacity and decision accuracy of system strategy generation are improved.

The application discloses a kind of multi-modal knowledge enhanced cross-document event co-reference resolution method, system, computer and storage medium, through big language model based on abstract-type-entity three-layer prompt template accurate extraction event information in document, structured multi-modal event graph including text and visual node is constructed, for original image, semantic alignment is carried out using multi-modal big model, and no image event is generated using stable diffusion model Synthesis image and easy-to-confuse negative sample;Text encoder and visual encoder are used to vectorize the representation of event text content and image respectively, and multidimensional co-reference relationship between cross-document events is established;Based on semantic distance calculation and contrast learning optimization, cross-document event co-reference resolution is realized, and the same event dispersed in different documents is uniformly associated.The application breaks through the limitations of traditional pure text event co-reference resolution in the aspects of insufficient multi-modal knowledge utilization and rough semantic association modeling.

The invention discloses an advertisement putting method and system based on user portrait big data, and the method comprises the following steps: converting the original behavior data of a user into structured information assets through integrating the data of a user side and generating a space-time semantic tag; prospective modeling is carried out on the user behavior sequence through deep learning, and future potential consumption demands of the user behavior sequence are predicted; through natural language processing and behavior pattern analysis, the emotional state and decision cognitive style of the user are dynamically identified; a passive shielding behavior is upgraded to active accurate avoidance through a knowledge graph; a bidding strategy is dynamically optimized through reinforcement learning, and global maximization of a single exposure value is realized; and a closed-loop feedback mechanism is established, and the system is driven to carry out continuous self-iteration. Through the method, the user intention can be predicated, negative feedback is utilized in a knowledge-based manner, and the social fission value is quantified, so that the return on investment and the user satisfaction are synchronously improved.

The application discloses a patent matching method based on large model and knowledge graph fusion, comprising the following steps: obtaining a semantic vector by a pre-trained large model according to collected patent text data, obtaining a graph embedding vector by a pre-trained knowledge graph model, and fusing the semantic vector and the graph embedding vector into a joint representation vector; further comprising: obtaining a semantic vector by a pre-trained large model according to collected user demand data, obtaining a graph embedding vector by a pre-trained knowledge graph model, and obtaining a joint representation vector; performing patent preliminary screening according to the vector similarity between the joint representation vector of the user demand data and the patent text data, obtaining a correlation score by comparing the preliminary screened patent text data and the user demand data, fusing the vector similarity through the correlation score, and obtaining a patent recommendation sequence. The core problem of poor matching precision caused by shallow semantic understanding and insufficient knowledge utilization is solved.

Embodiments of the present application disclose a BIM-based knowledge graph system. A specific implementation of the method includes: a data acquisition module configured to acquire and parse data of a coal preparation plant information system; a data governance module configured to govern the data acquired by the data acquisition module, and to obtain an entity topic of a knowledge graph by correlating and aggregating entities under a BIM business scenario of the coal preparation plant; a knowledge graph construction module configured to construct the knowledge graph by extracting entity attributes and combining relationships between entities according to the entity topic; and a knowledge graph intelligent application module configured to provide data intelligent application based on the knowledge graph obtained by the knowledge graph construction module. The implementation provides an automated process from data acquisition, parsing, and governance to knowledge graph construction and application, thereby improving the knowledge utilization efficiency of the coal preparation plant.

This invention provides a method and system for cross-project knowledge transfer of engineering equipment, belonging to the field of engineering project technology. The method includes: using an improved temporal block algorithm to divide source project data into multiple subdomains with the most diverse distributions; using a hybrid deep neural network model to extract hidden knowledge for each subdomain, thereby extracting the knowledge to be transferred from the source project data; and using a multi-knowledge feature fusion method to fuse the extracted hidden knowledge from each subdomain to obtain target project data, thus completing the cross-project transfer of engineering equipment knowledge. This invention solves the problems of existing knowledge transfer methods that lack consideration for removing noise from source domain data due to differences in data distribution, and do not simultaneously utilize multiple types of knowledge to make the model more adaptable to the target domain data when fusion results are obtained.

The invention provides a software vulnerability severity prediction method based on retrieval enhancement generation and chain thinking prompt. Belongs to the technical field of software engineering, and solves the technical problems of insufficient knowledge utilization, limited reasoning ability and poor interpretability of evaluation results of the existing software vulnerability severity prediction technology. According to the technical scheme, the method comprises the following steps: (1) knowledge base construction and vectorization: collecting codes and descriptions of an authoritative vulnerability source, normalizing the codes and descriptions, and combining the codes and descriptions with an initial data set to form a local knowledge base; and (2) similarity calculation and information fusion retrieval. And (3) using a few-sample chain thinking prompt template. According to the method, the severity of software vulnerabilities can be predicted.

The application discloses a kind of agent collaborative processing method and system for flood forecasting service, including receiving user initiated flood forecasting service request data and session identification;Parsing flood forecasting service request data, determine target service task;When the size of service document is less than the preset threshold, the content of service document is directly injected into the current session context;When the size of service document is not less than the preset threshold, the service document is blocked and vectorized, and the relevant document block is returned based on the similarity between the user request and the document block;Based on the injected session context, the corresponding executable object is called to execute the target service task, and the execution result is obtained;The execution result is fed back to the session corresponding to the session identification.The application can realize the cooperation of flood forecasting service document knowledge utilization, professional task execution, achievement automatic reference and continuous processing.

The invention discloses an incremental implicit refinement image classification method based on hyperbolic hierarchical modeling and two-way knowledge transmission, and the method comprises the steps: 1, carrying out the incremental task division of a data set: reconstructing an original image data set into a data set with a two-stage hierarchical structure, and carrying out the division of each task data set; step 2, network training: inputting a training set of a current task, training the hyperbolic hierarchical modeling and knowledge two-way transmission network, and updating model parameters; step 3, testing and outputting classification accuracy: testing on a test set of all the seen tasks and outputting an average increment classification accuracy; and repeating the steps 2 and 3 until no new task appears. According to the method, the dynamic hierarchical relationship is continuously constructed in the hyperbolic space, and the dynamic hierarchical relationship is used as a transmission cost to guide bidirectional optimal transmission between new and old knowledge, so that the problems of poor effect and insufficient knowledge utilization of a static hard hierarchical relationship adopted by a current method are solved, and the accuracy of incremental implicit refined image classification is improved.

The invention discloses a personalized federal learning method based on uncertainty feature distillation, and relates to the technical field of edge computing, comprising: sending a global model to edge devices participating in this round of training, the global model being used for updating local model parameters of each edge device participating in this round of training, the updated local model parameter is used for the edge device to update a local model according to a training model, and the training model is a training model based on an uncertainty estimation algorithm; acquiring updated local models and model parameters of all edge devices participating in the current round of training; the server aggregates all the updated local models of the edge devices participating in the current round of training, and updates the parameters of the global model; and repeating the training process until the maximum value of the communication rounds is reached. The method solves the problem of insufficient utilization of global knowledge in a traditional method; the negative influence of wrong global knowledge on local training is effectively reduced; and the distillation precision and the personalized effect are improved.

The application provides a kind of complex product design knowledge service method and system based on knowledge graph, comprising: through text tree structure and improved TFIDF algorithm combination N-gram strategy, reference document is semantically annotated;Knowledge carding template and rule extraction method are used to extract design process knowledge, generate knowledge triple and build knowledge graph in Neo4j graph database;On this basis, product design knowledge super network is built, and the semantic matching degree between tasks and knowledge is calculated by combining super edge correlation degree and information entropy modified vector space model;Finally, the query push of process knowledge is realized based on Cypher language, and the sorting recommendation of reference knowledge is completed by multidimensional comprehensive evaluation method.The application realizes the task-oriented knowledge structured expression, semantic correlation calculation and intelligent push in complex product design, and improves the enterprise knowledge utilization efficiency and design decision quality.

17. Abstract of description Provided in the embodiments of the present application are a knowledge base processing method and apparatus, and a computer device, a medium and a program product. The knowledge base processing method comprises: receiving a sharing credential of a shared knowledge base; in response to an access operation on the sharing credential, displaying the shared knowledge base, which is shared, wherein the shared knowledge base comprises at least one knowledge file; receiving question information for the shared knowledge base; and displaying answer information for the question information, wherein the answer information is obtained by means of performing answer generation on the question information on the basis of the knowledge file in the shared knowledge base. By means of the embodiments of the present application, the knowledge utilization rate of a knowledge base can be increased.

The application relates to a knowledge graph enhanced multi-modal archive retrieval method and belongs to the field of artificial intelligence and multi-modal information retrieval. In order to solve the problems of multi-modal information semantic splitting, weak semantic reasoning capability and low semantic matching precision in the existing multi-modal archive resource retrieval process, through four stages of archive multi-modal data preprocessing and feature extraction, knowledge graph construction and enhancement, semantic retrieval request analysis and intention modeling, multi-modal semantic matching and sorting, the semantic relationship is enhanced by using the knowledge graph, the semantic correlation and context consistency of the retrieval result are significantly improved, the user can input the query in the form of text, image, voice and the like, and semantic extension retrieval is supported. The application can realize more efficient and accurate archive resource retrieval, and improve the user retrieval experience and archive knowledge utilization.