53 results about "Context knowledge" patented technology

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention discloses a multi-agent-based context knowledge internalization, sharing and multiplexing system and method. The system comprises a multi-source knowledge capture module, a situation information extraction and structuring module, a knowledge internalization and incremental learning module, a situation awareness knowledge reuse and recommendation module and a knowledge quality evaluation and maintenance module, wherein each module is composed of a special intelligent agent or a functional unit and works cooperatively; according to the method, interaction, cooperation and process data and expert input of a task execution full cycle of a multi-agent system are captured through a multi-source module, preprocessing is carried out through a situation extraction module, a situation is associated to generate structured knowledge, and then the structured knowledge is fused into a dynamic knowledge system through an internalization module to achieve incremental learning. In a new task, matching knowledge is retrieved and recommended through the context awareness module, and finally the knowledge quality is periodically maintained by the evaluation module to ensure accuracy and timeliness; according to the method, the autonomous learning ability, the knowledge reuse efficiency and the intelligent level of solving complex problems of the multi-agent system are remarkably improved.

The invention provides a hybrid AI adaptive optimization system based on agent context engineering. The hybrid AI adaptive optimization system comprises a context knowledge base, a generation module, a feedback analysis module and a knowledge integration module. According to the method, the problems that in an existing context optimization technology based on a large language model (LLM), context Bias is simplified, Context Collapse is caused by overall rewriting, and the learning process is high in delay, high in cost, unstable and the like are solved.

The invention relates to the technical field of artificial intelligence and emotion calculation, and discloses an emotion state evaluation method based on multi-modal data, and the method comprises the steps: obtaining and decoupling an implicit signal, an explicit signal and situation data of an evaluated object; and respectively generating implicit features, explicit features and context vectors through double-flow decoupling coding and context knowledge graph coding. And the core engine determines the internal emotional state and the extrinsic emotional representation in parallel, and dynamically regulates and controls a bridging conversion process from the internal state to the predicted extrinsic representation by utilizing the situation vector. Finally, the emotion regulation intensity is calculated by comparing actual and predicted extrinsic representations, and the emotion regulation intensity, the internal state and the extrinsic representations are jointly used as a multi-dimensional evaluation result to be output. According to the method, the dynamic relationship and the adjustment strategy between the internal feeling and the external expression can be disclosed, a deeper and more three-dimensional analysis view angle is provided for the fields of mental health, human-computer interaction and the like, and the method has remarkable application value.

The invention provides an attack link restoration method and system based on a large language model and a traditional AI model, and effectively solves the problem that attack link restoration is incomplete and inaccurate when a current attack link restoration scheme faces complex factors such as network address translation. The method comprises the following steps: acquiring an attack semantic knowledge base containing an attack context knowledge matrix, wherein the knowledge base is generated by processing an attack framework, a security log and a traceability report by a generative large model; obtaining an asset access relation graph generated by analyzing the metadata of the target network infrastructure by the large model and reasoning in combination with an attack semantic knowledge base; regularly acquiring security alarm logs in a first time window and scoring, and acquiring context logs if the security alarm logs exceed a threshold value; based on a time sequence diagram attention network model, determining the confidence degree that edges in an asset access relation graph corresponding to logs containing access pairs in the alarm logs and the context logs belong to attack links; and performing sub-graph extraction in the atlas to obtain candidate sub-graphs, and then determining a target attack link.

The invention relates to the field of knowledge enhancement, natural language processing and artificial intelligence, and provides a dialogue emotion recognition model determination method and device and a dialogue emotion recognition method and device. The dialogue emotion recognition model determination method comprises the steps of determining target contextual knowledge corresponding to a dialogue sequence sample based on the dialogue sequence sample and a contextual knowledge prompt template; inputting the dialogue sequence sample and a contextual knowledge prompt template into an initial model to obtain first predicted contextual knowledge corresponding to the dialogue sequence sample; and based on the target context knowledge and the first prediction context knowledge, performing fine tuning on the initial model to obtain a trained dialogue emotion recognition model. The dialogue emotion recognition model determination method provided by the invention can improve the recognition effect of the dialogue emotion recognition model.

The invention belongs to the technical field of natural language processing and power system intelligent information processing. Comprising the following steps: S1, preparing data; s2, performing named entity recognition and relation extraction on the text block by utilizing a pre-trained large language model; s3, constructing a knowledge graph in the urban power distribution field by using the triple summary; s4, generating a semantic abstract with a hierarchical structure for each theme community; s5, querying and retrieving: retrieving related information from the vector index and the knowledge graph in parallel by receiving a natural language query of a user, and fusing results into a context knowledge set; and S6, an answer generation step: inputting the query and the context knowledge set thereof into a large language model, and generating an answer which is coherent in semantics, accurate in facts and covers query requirements through a preset prompt strategy guide model. According to the method, the accuracy and practicability of the question-answering system are remarkably improved, and core technical support is provided for intelligent operation and maintenance of the power distribution network.

The invention discloses an entity attribute prediction method based on dynamic twinborn attention and a KAN network. The method comprises the following steps of entity digital twinborn construction and data mapping; constructing a space-time knowledge graph; carrying out dynamic feature fusion based on DTA-KAN; predicting a future state based on fusion features; according to the method, firstly, a digital twin and a space-time knowledge graph are constructed for a target entity, an attention query signal is dynamically generated by using a real-time state vector of the digital twin, and the signal is used for guiding a graph attention network; according to the method, context knowledge most relevant to the current real-time state is adaptively extracted from a space-time knowledge graph, intelligent prediction of dynamic knowledge extraction and deep fusion under the guidance of the real-time state is realized, and a complete closed loop from original data acquisition to fusion and prediction to comprehensive presentation and intelligent research and judgment is formed; and powerful technical support is provided for realizing prospective and initiative social governance.

The invention discloses a teaching content generation method and system based on large model instruction and context knowledge dual drive. The method comprises the following steps: firstly, analyzing, slicing and vectorizing an original knowledge document in the education field through knowledge preprocessing in the education field; then, defining teaching content to generate a prompt word template, and defining a large model instruction, a variable and a context knowledge content block; secondly, arranging teaching content to generate a workflow, and constructing large model instruction driving and context knowledge driving logic; and finally, implementing dual-drive teaching content generation and performing result post-processing. Based on the method, a system corresponding to the method is constructed, and the system comprises an education field knowledge preprocessing module, a teaching content generation prompt word template definition module, a teaching content generation workflow arrangement module, a teaching content generation implementation and result post-processing module and a user man-machine collaborative interaction operation module. According to the method, the teaching content can be generated according with the education rule, the specific teaching scene requirement is met, and the teaching content generation accuracy and applicability are improved.

The invention discloses a context-aware dialogue system and method based on AI assistant intelligent question answering, and belongs to the technical field of artificial intelligence and man-machine interaction, and the system comprises a user interaction interface module which is used for receiving a query request input by a user and returning a response result to the user; the dialogue context management module is used for capturing, storing and structurally processing historical information in the dialogue session in real time so as to construct and dynamically update a dialogue context knowledge base containing original dialogue records, refined dialogue states, user intentions and entity information; and the context perception understanding module is used for carrying out deep semantic analysis on the current query of the user and carrying out joint understanding in combination with the dialogue context knowledge base so as to analyze anaphora, omission and implicit intentions in the context and generate a complete semantic representation rich in context information. According to the method, the AI assistant can have strong context memorizing, understanding and reasoning capabilities, and human-like and coherent multi-round intelligent dialogues are realized.

The invention discloses a financial field knowledge base construction method, device and equipment. The method comprises the following steps: firstly, identifying a text region, a table region and an image region from a target image; then extracting text data of the text region by using an OCR algorithm, and extracting semantic features and visual features of the image region and a second entity set and an entity type corresponding to the table region by using a multi-modal AI model; carrying out illusion detection by utilizing the similarity of the two entity sets, and when judging that the illusion of the AI model does not trigger a fault-tolerant mechanism by utilizing a detection result, carrying out intelligent alignment processing on the two entity sets, and screening out an optimal entity matching pair; carrying out formatting verification on the entity type; and then context knowledge association processing is carried out on text data corresponding to the text region, semantic features and visual features corresponding to the image region and entity matching pairs passing verification, and a more stable and accurate knowledge base is constructed by utilizing the obtained graph structure document to represent corresponding structured knowledge.

The application discloses a kind of intelligent query control method and electronic equipment based on natural language interaction.The method includes the following steps: building context knowledge base, converting into context vector, and storing in vector database;Convert the input natural language question into query vector;Retrieve the k context vectors most similar to the query vector from the vector database, and determine the corresponding context information;Combine the natural language question and the retrieved context information into a prompt word sequence input into a large language model (LLM) to obtain an SQL query statement, and analyze whether the natural language question contains a control intent;Execute the SQL query statement;If the natural language question contains a control intent, analyze the query result intelligently and perform the corresponding device control operation.The application can analyze the control intent in the natural language question, and after the result is queried, the corresponding device can be controlled according to the control intent to work, improving the work efficiency.

The invention discloses a knowledge retrieval method, device and equipment, and the method comprises the steps: carrying out the complexity evaluation of a query statement inputted by a user, and obtaining a complexity evaluation result; based on the complexity evaluation result, selecting a retrieval strategy corresponding to the complexity evaluation result to perform entity extraction processing on the query statement to obtain target entity information; based on a retrieval strategy, performing selection and combination processing on the target entity information and the query statement to generate a retrieval instruction; based on the retrieval instruction, performing retrieval from a knowledge vector library to obtain a plurality of knowledge retrieval results; and performing score selection processing on the plurality of knowledge retrieval results, and constructing to obtain target context knowledge data.

The invention discloses a knowledge graph-based government affair retrieval enhancement generation framework and method, and relates to the technical field of natural language processing. The method specifically comprises the steps that fine adjustment is conducted on a basic model through government affair corpora, and a government affair field enhanced semantic model is generated; vectorizing a government affair knowledge graph entity by using a semantic model enhanced in the government affair field and storing the vectorized government affair knowledge graph entity into a graph entity vector database; key government affair entities are extracted from user questions, semantic matching is carried out on the extracted government affair entities and graph entities, and accurate context knowledge is retrieved from the knowledge graph through automatically generated graph query statements; and fusing the retrieved context knowledge with the original question to construct an enhanced prompt, and driving a large language model to generate a high-quality natural language government affair answer. According to the method, the accuracy of government affair questions and answers, the understanding depth of complex government affair logic and the retrieval efficiency are remarkably improved by deeply fusing the structural advantages of the knowledge graph and the semantic ability of the large language model.

The invention relates to a database diagnosis method and device based on a large model, electronic equipment and a storage medium. The method comprises the steps that index data, an internal library table and field annotations in a database are obtained, and a knowledge system of the database is set; classifying the index data according to the knowledge system to obtain fault data corresponding to each fault system; performing natural language mapping processing on the fault data to obtain a large-model context knowledge system; training a preset fault large model according to the large model context knowledge system, the internal library table and the field annotation, and then performing fault detection to obtain a diagnosis result; fault data classification is achieved by building a knowledge system, a context knowledge system capable of being recognized by a large model is generated in combination with a natural language processing technology, automatic fault diagnosis is achieved based on the trained fault large model, and the technical problems that a traditional method depends on manual analysis and is low in complex fault processing efficiency are solved. The method has the advantages of improving diagnosis accuracy and reducing operation and maintenance cost.

A question answering method based on a large model is performed by an electronic device. The method includes: determining a first result corresponding to a query statement based on a first large model, in which the first result includes a first response corresponding to the query statement, a first response logic corresponding to the first response, and first context knowledge corresponding to the first response; determining a retrieval result corresponding to the query statement by retrieving in a database based on the query statement; determining a type of a knowledge conflict between the first result and the retrieval result; and determining a target response corresponding to the query statement based on the first result, the retrieval result, and the type of the knowledge conflict.

The invention provides a diversified multi-hop question generation method and system based on knowledge combination sampling, and relates to the technical field of multi-hop questions.The generation method comprises the steps that context knowledge and target answers are obtained, and the target answers are obtained based on a hierarchical neural network architecture; obtaining conditional probability distribution data and an initial knowledge combination according to the context knowledge and the target answer; obtaining a plurality of diversified knowledge combinations according to the initial knowledge combination and the conditional probability distribution data through a preset sampling strategy; and generating a corresponding multi-hop question according to all the diversified knowledge combinations and the target answer through a preset multi-hop question generation model. The whole process does not need complex intermediate links and can be realized by relying on a mature neural network architecture, the manpower and hardware cost of technology landing is reduced, for example, when a multi-hop question bank is constructed in the education field, a teacher only needs to input a textbook text and knowledge points, multi-hop questions of different examination angles can be automatically generated, and the efficiency is improved. And the problem of data sparsity of the multi-hop question-answer data set is effectively relieved.

The application provides a risk transmission prediction method and system based on a time sequence diagram and a large model joint deduction, relates to the technical field of artificial intelligence and knowledge engineering, and comprises the following steps: extracting forward-looking time sequence facts based on hierarchical prompt words and a structured constraint mechanism; extracting conflict events and generating time sequence relationships; updating a historical time sequence knowledge graph based on a time sequence relationship and a confidence weighted update strategy; determining a central entity, initiating a structured query to the updated historical time sequence knowledge graph from the central entity, and generating a context knowledge subgraph; converting the context knowledge subgraph into a natural language description with logical relationships, injecting a risk hypothetical event, constructing a prompt word as a new input of a large model, and outputting a structured JSON object containing a complete reasoning chain; and based on a risk path refining and visualizing algorithm with influence weight attenuation, converting the structured JSON object to obtain risk warning information and a visualized transmission path diagram.

The invention discloses a multi-source heterogeneous data continuous learning evaluation method based on context knowledge enhancement, and relates to a continuous learning technology and a model performance evaluation method in machine learning. The method mainly aims at evaluating the ability of a depth model to continuously learn sequence tasks on multi-source heterogeneous data; the method is an evaluation method which is based on context knowledge enhancement and focuses on key new knowledge learning efficiency and precision. According to the method, a human forgetting curve is simulated, the defect that new knowledge and old knowledge are not distinguished in an existing evaluation mode is broken through, higher calculation weights are given to knowledge needed by the model for processing problems in the current context environment, and the continuous learning ability of the model on multi-source heterogeneous data is evaluated in a mode more fitting the reality.

According to the multi-mode emotion regulation subtitle generation method fused with cognitive intervention, classroom teaching audio is collected through an omnidirectional condenser microphone, multi-dimensional acoustic features are extracted after preprocessing such as mute segment removal, noise suppression and signal normalization, the audio is converted into a text through a DNN-ASR model and a pre-training BERT model, and a semantic embedding vector is generated. An improved CNN-LSTM model is adopted to fuse acoustic and semantic features for emotion recognition, and a primary cognitive tag is generated in combination with a classroom situation knowledge base. Based on an emotion cognition theory, secondary modeling is carried out on emotion signals through an explanatory subtitle generation sub-model and a background annotation adjustment sub-model, an emotion adjustment factor matrix is formed, and weights are dynamically controlled. And finally, according to the adjustment factor, generating a multi-mode subtitle, matching a color code, a dynamic icon and a font style, and presenting the information on a teaching terminal. According to the invention, classroom teaching information processing efficiency can be effectively improved, and deaf students and other groups can be assisted to better understand teaching contents.

The invention relates to the technical field of data processing, and provides a data retrieval method and device, electronic equipment and a readable storage medium. The method comprises the following steps: performing semantic similarity matching processing on query data to obtain context knowledge data corresponding to the query data; performing integrity verification on the context knowledge data to obtain a state identifier, valid data and missing information representation data corresponding to the context knowledge data; under the condition that the state identifier is in an incomplete state, performing closed-loop completion processing on the query data based on the missing information representation data, performing re-query based on the completed query data, and obtaining updated valid data in combination with the valid data; and performing integration generation processing on the updated effective data and query data to obtain a first target retrieval result, thereby improving the information integrity, improving the accuracy of the retrieval result, enhancing the credibility of the retrieval result and improving the robustness.

A machine learning and inference system for generating personalized activity plans includes: a trained world context knowledge base; a trained personal digital memory bank; the target engine is used for establishing a moving target; an activity decomposition engine to decompose the activity into connected logically consistent sub-activities; a weight allocation engine to allocate value weights to the potential sub-activities; a graphics generation engine to generate a multi-layer weighted graph to train a model of personalized results of the sub-activities according to value weights; a scene generation engine to analyze a network of logically consistent potential sub-activities connected by paths to determine a selected scene path to the activity target according to a model that at least personalizes the user results; a feedback engine to apply learning from the scene generation engine to a world context knowledge base and / or a personal digital memory bank; and an output channel for outputting a personalized activity plan comprising recommended actions for implementing the selected scene path.

The invention provides an intelligent interaction-oriented memory enhancement and reasoning method and system, and belongs to the technical field of intelligent interaction, and the method comprises the steps: firstly capturing an original interaction data stream of a user, segmenting and arranging into an interaction event time sequence, preliminarily determining an interaction intention, and merging into an interaction round unit sequence; extracting a core keyword, performing association expansion to obtain a background support knowledge unit, and generating an interaction situation knowledge set; and fusing the context knowledge coding vector with the interactive content semantics, and retrieving historical memory entries in a long-term interactive memory library. And finally, complementing the current interaction intention according to historical memory, generating response reasoning content and transmitting the response reasoning content to the intelligent interaction terminal for presentation. According to the invention, the accuracy of intelligent interaction and the user experience are improved.

The invention provides a question and answer generation method and device based on knowledge in the field of drug research and development, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a query request of a user; performing text optimization on the query request to obtain an enhanced query text; wherein the enhanced query text comprises target drug research and development process entry information so as to provide context knowledge support based on a drug research and development process basis; based on the enhanced query text, performing retrieval filtering in a pre-constructed drug knowledge base to obtain a target knowledge set; based on the cue word template, performing knowledge fusion on the enhanced query text and the target knowledge set to obtain a structured enhanced cue word; inputting the structured enhanced cue word into the large language model to obtain an output result; wherein the output result comprises the reference information. By establishing the accurate corresponding relation between the query request and the fine-grained process knowledge in the drug research and development field, the accuracy, specialty and reliability of the question and answer generation result are remarkably improved.

The invention provides a medical subject knowledge service-oriented data processing method and electronic equipment, and belongs to the technical field of medicine. The method comprises the following steps: in response to a medical subject knowledge problem, establishing a multi-agent cooperation team; a plurality of target information dimensions required for answering the medical subject knowledge problem and importance parameters corresponding to the target information dimensions are determined through the multi-agent cooperation team, so that a preliminary service result is output, and in the process of executing reasoning by the multi-agent cooperation team, positive and negative example reference libraries are retrieved, so that the preliminary service result is obtained. Acquiring historical cases associated with the medical subject knowledge problem, the target information dimension and / or the current reasoning stage as context knowledge; and performing correctness evaluation on the preliminary service result, and generating and outputting a final service result according to an evaluation result. According to the method, on the premise of avoiding re-calculation amount parameter training, experience can be continuously absorbed through the positive and negative example reference library by using the multi-agent cooperation team, and the accuracy of solving the medical subject knowledge problem is improved.

The application provides an intelligent Scala code generation method and system based on artificial intelligence, which comprises the following steps: obtaining a user natural language requirement description, performing semantic understanding and information extraction, extracting function, input and output type and constraint condition to form standardized technical elements, and mapping to a Scala syntax and programming paradigm to generate a requirement semantic vector; performing static analysis on a project code library, extracting code structure, dependency relationship and coding style, constructing a multi-level context knowledge graph, and obtaining a context vector through a graph neural network and an attention mechanism; fusing the two types of vectors to generate a joint vector, relying on a pre-trained Scala code generation model to generate a code framework and core code, generating and optimizing a candidate scheme through a beam search algorithm, and determining an optimal code scheme; performing formatting, type checking, performance optimization and test verification on the optimal code scheme, automatically generating document annotations, and obtaining a final perfect code. The application realizes perfect balance between performance and reliability.

The invention discloses a cross-modal generation algorithm-based inheritance and interactive learning method for art non-perpetual items, and the method comprises the steps: collecting the information data of art non-perpetual items, forming multi-source heterogeneous data, carrying out the cleaning, classification and labeling of the data, forming a semantic-associated data set, and carrying out the recognition of the multi-source heterogeneous data. The data are classified into a static pattern library, a dynamic process library and a context knowledge library, the three information libraries form a standard multi-source heterogeneous characteristic spectrum information library of non-perpetual items, and non-perpetual characteristics of different modalities are fused by using an improved cross-style generation algorithm so as to form a standard multi-source heterogeneous characteristic spectrum information library of the non-perpetual items; generating a plurality of scheme works with diversified styles, which not only can retain traditional culture genes, but also can adjust the innovation degree; and learning and design reconstruction are carried out by using virtual-real fusion learning design and a cross-style generation algorithm, and fusion learning and creation of artistic contents are carried out by combining historical creation data of the user. The method disclosed by the invention has important practical significance for diversified presentation and inheritance learning of non-perpetual culture.

The application discloses a context knowledge construction method and device based on industrial point position analysis, equipment and a storage medium, relates to the technical field of artificial intelligence, and comprises the following steps: performing alias normalization on the field names of an industrial field original point position table, obtaining standardized point position data, arranging semantic attributes, generating point position audit data after association, identifying the correlation between points, generating relationship audit data, and storing the relationship audit data in a point position knowledge base and a relationship knowledge table respectively; receiving a search request containing a factory identifier and a point position identifier to be analyzed, searching for corresponding point position knowledge based on the factory identifier, and obtaining an input point position set; searching for associated records in the relationship knowledge table, completing associated point position knowledge, and obtaining an associated point position set; and combining the input point position, the associated point position and the relationship record, generating structured context containing semantic information and analysis prompts, and using the structured context for industrial data analysis, so that the efficiency of constructing context knowledge is improved.

The invention discloses a system and a method for sensing vulnerability priority ranking and intelligently repairing suggestions. The method comprises the following steps: acquiring original security data and context information through a data acquisition module; cleaning, standardizing and associating the acquired data through a data fusion and knowledge graph construction module, and constructing a context knowledge graph; performing risk assessment and priority ranking on vulnerabilities by utilizing a machine learning model based on the multi-dimensional context information in the knowledge graph through a vulnerability priority ranking module; providing vulnerability repair suggestions for developers through an intelligent repair suggestion generation module; and a feedback and learning module is used for collecting the adoption condition, the repair result and the vulnerability verification result of the repair suggestion by the developer, and the feedback data is used for continuously optimizing the AI model and the knowledge graph. According to the method, the vulnerability repair efficiency and the code security level can be remarkably improved, the security risk is reduced, the automation level of DevSecOps is enhanced, and comprehensive security situation awareness is provided.