190 results about "Knowledge acquisition" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a content generation and optimization method based on a knowledge graph, which comprises the following steps: constructing a multi-source knowledge database, extracting core concepts and knowledge contents, and constructing the knowledge graph. Performing semantic analysis to generate semantic vector representation and a keyword list; retrieving the associated text fragment based on the semantic vector and the keyword list, and inputting the associated text fragment into a generation model to generate initial answer content; and utilizing the knowledge graph to match the domain entity and the knowledge graph node, generating a logical reasoning path, optimizing the initial answer content, and generating the final answer content. According to the method, content generation of accurate retrieval, deep knowledge association and logical reasoning enhancement is realized by fusing a multi-source knowledge database, knowledge graph reasoning and generation optimization; semantic vector matching and keyword retrieval are combined, so that the accuracy of knowledge acquisition is improved; and through knowledge graph reasoning path construction, the answer logic is coherent.

The invention discloses a multi-modal knowledge graph construction method and device based on a large model and a program product, belongs to the technical field of artificial intelligence and knowledge engineering crossing, and particularly relates to a knowledge graph dynamic construction and evolution method based on a large language model technology and multi-modal data processing. The problems of symbol grounding and semantic understanding obstacle in the prior art are solved. According to the method, innovation and breakthrough are realized in three dimensions of knowledge acquisition, representation and reasoning by fusing deep learning and knowledge engineering technologies. The multi-modal knowledge graph construction method and device based on the large model and the program product are applied to the field of multi-modal knowledge graph construction and are suitable for specific task scenes such as intelligent question and answer, decision support and semantic search.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

The invention discloses a database operation and maintenance method, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a user intention matched with an operation and maintenance demand problem of a user; if the intention is a general operation and maintenance knowledge acquisition demand, performing retrieval in a knowledge base according to an operation and maintenance demand problem, and generating a first response result for feedback; if the intention is an actual operation and maintenance scene knowledge acquisition demand, real-time operation data of a database is acquired through a database monitoring module, retrieval is performed in a knowledge base according to the real-time operation data and an operation and maintenance demand problem, and a second response result is generated for feedback; and if the intention is to execute the target operation and maintenance task, retrieving an operation and maintenance instruction set matched with the target operation and maintenance task in the knowledge base, and issuing the operation and maintenance instruction set to an operation and maintenance task execution module, so that the operation and maintenance task execution module arranges and executes the target operation and maintenance task according to the task priority. According to the embodiment of the invention, the workload of operation and maintenance personnel is reduced while the operation and maintenance efficiency is improved.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

The invention relates to the technical field of term libraries in the field of constructional engineering, and discloses a construction application method and system for a term library in the field of constructional engineering, and the method comprises the steps: preprocessing unstructured data to obtain structured constructional engineering text data, and extracting the structured constructional engineering text data based on a joint extraction architecture to obtain an extraction result; the method comprises the steps of obtaining structured data of terms and building engineering triple information, establishing a standard library and a term library according to the structured data, constructing a term-standard double-layer knowledge graph based on internal association of the standard library and the term library and the building engineering triple information, and responding to query of a user. And calling the standard library, the term library and the term-standard double-layer knowledge graph by a mixed decision-making mechanism based on rule reasoning and graph neural network learning to obtain a query result. According to the invention, the full-life-cycle intelligent closed loop of construction engineering terms from extraction, management to application is realized, and the problems of difficult acquisition of industry term knowledge, complex management and low application efficiency are solved.

The invention discloses a group chat questioning method and device, equipment, a storage medium and a computer program product, and relates to the technical field of intelligent AI, the group chat questioning method comprises the steps that user questions in a discussion group are acquired, the discussion group comprises a user and a target model group, the target model group comprises at least one large model group member, and the user questions in the discussion group are acquired; the large model group members correspond to at least one large model; inputting the user question into a target model group corresponding to the discussion group to obtain large model answer information of at least one large model group member to the user question; and generating and displaying a model reply result according to the large model reply information. Through the mode, the user can obtain collaborative answers and help of a plurality of large models without additional operation, the interaction efficiency with the AI large model is improved, and the comprehensiveness of knowledge acquisition is expanded.

The invention relates to an AD-RAG-based vehicle after-sales maintenance document analysis and maintenance knowledge acquisition system and method, and the system comprises a document analysis module which carries out the layout analysis and document recognition of after-sales maintenance document information, and obtains structural data; the knowledge organization module is used for establishing an association relationship between the text data and the structural relationship in the structured data to obtain a maintenance knowledge graph; the knowledge retrieval enhancement module is used for analyzing and expanding the vehicle fault query request based on an AD-RAG model to obtain an expanded query request, and querying in a maintenance knowledge graph based on the expanded query request to obtain document fragment information; and the task decomposition and reasoning optimization module analyzes and decomposes the extended query request to obtain at least one fault sub-problem sequence, and performs fault reasoning through a preset fault reasoning model based on the fault sub-problem sequence and the document fragment information to obtain a fault maintenance suggestion result. The retrieval requirement can be better met, and the obtaining efficiency of the maintenance knowledge can be improved.

The invention discloses a knowledge network construction and recommendation method for artificial intelligence teaching, and relates to the technical field of education informatization, and the method comprises the steps: collecting structured course database data, semi-structured teaching material data and unstructured audio and video resources; executing cross-modal knowledge analysis by adopting a bidirectional sequence labeling model, and extracting an artificial intelligence teaching knowledge subject object and a multi-dimensional feature description thereof; constructing a knowledge unit, and adopting a model of'knowledge subject-feature dimension-feature value 'and'knowledge subject-association relationship-association knowledge subject'; vectorized semantic calibration is realized through an embedded space mapping technology, and heterogeneous semantic unit fusion is completed in combination with a dynamic similarity threshold mechanism; and screening high-quality knowledge units based on a credibility verification engine to construct an artificial intelligence teaching knowledge network. According to the invention, the organization efficiency and personalized service accuracy of artificial intelligence education resources are effectively improved, and the problem of knowledge acquisition efficiency bottleneck caused by discretization of multi-source teaching resources is solved.

The invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, and relates to the technical field of intelligent manufacturing and artificial intelligence. Natural language output of a large language model is converted into rules, facts and ontology fragments which can be directly consumed by an inference engine through knowledge obtaining and compiling, continuous increment updating of cross-domain knowledge is achieved in cooperation with metadata with sources and timestamps, and the limitation that a traditional static knowledge base is difficult to cover dynamic faults is overcome; secondly, a blackboard and agenda mechanism is used as a cooperative carrier, intermediate assertions of intelligent agents such as vibration, circuits and logs are published and subscribed in a structured mode, and a conflict resolution and consistency verification module carries out unified judgment according to specificity, time freshness and source credibility; therefore, delay and uncertain accumulation caused by long-chain natural language dialogues are avoided in a strong real-time scene.

The invention relates to the technical field of computers, and discloses a fragmentation block chain federal learning method based on a large language model multi-agent, and the method comprises the steps: S1, initializing a client; s2, dynamic fragmentation scheduling and distribution; s3, generating and uploading local knowledge; s4, intelligent agent collaborative routing and knowledge acquisition; s5, knowledge fusion and model updating; and S6, repeatedly executing the steps S3 to S5 until the model converges or reaches a preset number of iterations. According to the invention, under a decentralized and fragmented federated learning architecture, the complex reasoning ability of a large language model and the autonomous cooperation mechanism of three multi-agent systems, namely a fragmented scheduling agent, a fragmented knowledge state agent and a global knowledge routing agent, are deeply fused; according to the mechanism, dynamic optimization of a bottom layer fragment structure and intelligent routing of high-value knowledge are achieved in an intelligent mode, and therefore the overall efficiency and model performance of a system under the condition of heterogeneous data and heterogeneous equipment are remarkably improved.

The invention discloses a generative artificial intelligence-based banquet creative design system, which comprises: a demand determination module, which is used for interacting with a user terminal, obtaining a user demand and generating a demand theme; the banquet creativity generation engine is used for determining and operating a proxy artificial intelligence assembly line and generating a banquet creativity design project; comprising a main control unit, a domain knowledge acquisition unit, a creativity generation unit and a integrating degree evaluation unit, each unit is embedded with a multi-level intelligent agent as an execution main body, and prompt information is defined as input of the execution main body; the brand domain knowledge vector library is used for providing domain knowledge; the banquet design domain knowledge vector library is used for providing banquet design knowledge; and the design output module is used for obtaining the banquet creative design project and forming a banquet creative design scheme. According to the technical scheme, multi-modal brand domain knowledge and banquet creative design can be deeply fused, and automatic generation of multi-modal information of a high-end banquet catering art scheme is achieved.

The invention specifically relates to the technical field of multi-modal large models, and discloses an automobile production teaching evaluation optimization system based on a large model, and the system comprises an industry knowledge obtaining module, a knowledge graph construction module, a teaching resource generation module, a capability data collection module, an intelligent capability evaluation module, and a resource matching cooperation module. An industry knowledge acquisition module acquires an automobile production and teaching fusion dynamic knowledge data set, a knowledge graph construction module constructs an automobile production and teaching knowledge graph, a teaching resource generation module generates teaching resources, and a capability data acquisition module acquires learning capability associated data of students. Student ability comprehensive scores are calculated through the intelligent ability evaluation module, personalized learning paths and learning materials are recommended for students through the resource matching cooperation module, talent lists are recommended for enterprises, automatic and high-frequency updating of teaching content is achieved, and intelligent scheduling of the whole process and intelligent upgrading of automobile production teaching evaluation are achieved. And the production and education docking efficiency is greatly improved.

The present invention relates to a platform management method and system based on low-code development, the method comprising: obtaining multiple heterogeneous data sources, extracting first data from the multiple heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph. A plurality of different entities and entity relationships between different entities are obtained from the standard data, and a knowledge graph is constructed based on the plurality of different entities and entity relationships between different entities. The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures. A predefined business process and a plurality of decision points corresponding to the execution of the business process are obtained, and the execution results of the business process are evaluated by executing global knowledge and current business process variables to obtain corresponding guiding decisions.

The invention discloses an intelligent auxiliary decision-making method and system for multi-modal data fusion of multiple myeloma, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-source clinical data and authoritative literature of a patient, and building a literature database after preprocessing; configuring an interface to automatically capture new literatures, and generating a dynamic knowledge base in combination with semantic conflict detection, knowledge extraction and expert auditing; according to the knowledge base, fusing the multi-modal clinical data by adopting federal learning to generate a fusion feature matrix; constructing a causal relationship network, combining matrix prediction and generating a causal evidence network, and grading the causal relationship; and associating the knowledge base with the evidence network, generating an interpretable report containing a prediction result and the like, and realizing full-process traceability. Through three designs such as automatic knowledge acquisition and updating, the static lag problem of the knowledge base is broken through, and the medical frontier is synchronized; a causal reasoning system is constructed, defects of a traditional model are overcome, transparent reasoning is achieved, and the credibility of doctors is improved.

The invention relates to the technical field of knowledge graph construction, in particular to a knowledge graph construction and enhanced generation method and equipment for health and care, and a medium, which can solve the problem of suitability optimization of a large model in the field of health and care through reinforcement learning and semantic information technologies, and provide large model application services with professional adaptation and high interpretability. For knowledge graph construction, the extraction precision and the fine adjustment efficiency of a base large model for knowledge in the health and care field are improved through reinforcement learning, semantic association is reinforced by means of evidence chain fusion to improve graph information density, and accurate classification and summarization of knowledge are achieved based on multilevel clustering; aiming at enhanced generation, retrieval pertinence is improved through field adaptability optimization of a problem analysis model, comprehensiveness and accuracy of knowledge acquisition are guaranteed through multi-path recall and reordering, and the problem of interpretability of large model generation content is solved through traceability connection.

The invention relates to an electric power intelligent knowledge retrieval method and device based on an RAG model, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of computers. The method comprises the steps of obtaining a query request; performing semantic analysis processing on the query request to obtain a query semantic feature of the query request; according to the query semantic features, candidate knowledge content is retrieved from a pre-constructed electric power knowledge base; screening the candidate knowledge content to obtain a target retrieval result of the query request; inputting the target retrieval result into a pre-trained power field content answer model for content answer processing to obtain preliminary answer content of the query request; and performing security filtering processing on the preliminary answer content to obtain a target answer result of the query request. By adopting the method, the power knowledge acquisition efficiency can be improved.

According to the electric power data prediction method based on large language model auxiliary knowledge graph construction provided by the invention, a high-coverage electric power knowledge graph is automatically constructed through a large language model, so that the problems of low knowledge acquisition efficiency and insufficient semantic relationship recognition accuracy of a traditional method are solved; through a multi-path causal attribution mechanism, a multi-hop and multi-relation path in the knowledge graph is effectively converted into a prior adjacency matrix which can be utilized by a neural network, and structured fusion of domain knowledge is realized; through the synergistic effect of the time sparse attention and the mixed adjacency matrix, the modeling precision of the power data space-time dependency relationship is remarkably improved, and an important technical support is provided for precise scheduling and optimized operation of a smart power grid.

The invention relates to the technical field of data retrieval, in particular to a FastGPT-based intelligent question-answering system for construction project quality inspection and detection standards. According to the technical scheme, the method comprises the steps of external knowledge acquisition, document processing, vectorization, vector database, retrieval and context generation and intelligent question and answer, data can be collected by a system, a semantic segmentation technology, a QA pair generation technology and a high-dimensional vectorization technology are utilized, in combination with M3E and OpenAIEmbedding models, Top-K similarity retrieval and context dynamic recombination are achieved, and structured and hierarchical professional answers are generated. According to the method, the intelligent management and real-time application capability of the construction project quality inspection and detection standard can be remarkably improved, the manual retrieval and interpretation cost is reduced, and the digital and intelligent requirements of an engineering project are met.

The invention discloses an intelligent fault diagnosis method and system for welding equipment. The method comprises the steps that a large language model analyzes according to input fault description to obtain a core retrieval dimension; based on a pre-constructed welding equipment fault knowledge graph, performing primary screening and link completion on the core retrieval dimension to obtain one or more fault link strengths and case success rates; according to the fault link strength and the case success rate, diagnosing the credibility of the link, and comparing the credibility of each link to obtain an optimal diagnosis result; wherein when a new fault case is added to the welding equipment fault knowledge graph, the new fault case is automatically updated through a large language model; according to the method, the problems of knowledge acquisition and semantic understanding are solved through the large language model, the problems of knowledge storage and link tracing are solved through the knowledge graph, full-process automatic diagnosis from fuzzy input to landing reporting is achieved, the technical pain point of a traditional scheme is solved, and the practical requirement of an industrial welding scene is met.

The invention provides an information processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps that an information processing interface is displayed, and the information processing interface comprises an interaction control based on artificial intelligence; in response to an input operation in the interaction control, the input first information is displayed, and at least one first knowledge base is displayed, and the at least one first knowledge base comprises knowledge related to the first information. According to the method and the device, the flow and propagation of knowledge can be promoted, so that the knowledge acquisition efficiency is effectively improved.

The invention provides a policy knowledge questioning and answering method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent questioning and answering. Performing word segmentation processing on the question information to obtain each initial segmented word; removing preset stop words and obtaining a text quantity according to the quantity of the target segmented words; dividing the content of each piece of policy information into at least one information block based on the current block granularity; wherein the current block granularity and the text quantity are in a negative correlation relationship; determining a matching degree to obtain an information block sequence; adjusting the sorting of the information block sequence based on the target intention, and extracting a preset number of information blocks to obtain each target information block; and inputting the question information, the target information block and a preset prompt template into a preset question and answer model to generate a question and answer result. The content of the policy information is dynamically divided according to the question information, the matching degree and intention are combined to recognize and adjust the sorting of the target information blocks to generate the question and answer result, and the policy knowledge acquisition efficiency and accuracy are improved.

The invention discloses an intelligent question and answer method and system for a large model ship field based on retrieval enhancement generation, and the system comprises a fine tuning data integration module, a model fine tuning module, a model reasoning module and a module integrating various technologies, and aims at achieving the efficient management, intelligent retrieval and precise question and answer of complex documents in the field. According to the system, through organic cooperation of three core modules of document preprocessing, high-precision retrieval and large-model fine-tuning reasoning, the system adopts multiple index structures, and rapid positioning and semantic matching of documents are achieved. Through a large model fine tuning technology, the system adapts a general pre-training model to a ship domain task and endows the ship domain task with a domain specifying ability, and the system can quickly respond to multiple rounds of question and answer requirements of a user and dynamically adjust generated answers to meet the requirements of different contexts. According to the method, the threshold of knowledge acquisition and utilization is reduced for the ship industry, resource optimization configuration and technical progress are promoted, and a wider space is created for future development of the industry.

The invention discloses an electromagnetic interference risk assessment method based on expert knowledge and a neural network, and the method comprises the steps: building an electromagnetic interference risk assessment index system of a to-be-assessed object, bottom layer indexes of the index system comprise a power grid ripple coefficient, power grid peak signal conduction emission, power grid harmonic characteristics, electromagnetic emission characteristics, electromagnetic sensitivity characteristics, filtering characteristics and conduction and radiation safety margin. Establishing a potential interference source and potential sensitive equipment electromagnetic compatibility evaluation data set by using an expert knowledge method; and using the neural network to carry out electromagnetic interference risk assessment training and assessment of the to-be-assessed object. The method can give full play to the advantages of strong pertinence and high flexibility of expert knowledge and the advantages of strong ability of automatic knowledge acquisition and nonlinear mapping of a neural network, and realizes adaptive assessment of electromagnetic interference risks according to real-time acquisition and analysis of an electromagnetic environment; according to the method, manual parameter setting is not needed any more, the evaluation process is easier to implement, and the reliability of the evaluation result is higher.

The invention belongs to the technical field of computer network security, and discloses a power network security knowledge ontology modeling method, which comprises the following steps of: firstly, collecting multi-mode heterogeneous data such as power monitoring, security equipment and threat intelligence in parallel; then, adopting a hybrid strategy, on one hand, constructing a top-layer seed ontology based on a domain standard, and on the other hand, automatically extracting an entity relationship from the text by utilizing a deep learning model; secondly, associating multi-source knowledge and solving conflicts through entity alignment and sequential logic fusion, and generating a consistent and credible global knowledge view; and finally, establishing an ontology self-evolution mechanism based on reinforcement learning, dynamically adjusting knowledge weight and automatically expanding a mode, realizing deep understanding, accurate prediction and self-adaptive evolution of network threats, and improving the active defense level of a power system. According to the method, the core problems of one-sided knowledge acquisition, poor fusion capability, static model evolution incapability and the like in the prior art are solved.

The invention discloses a large model content generation acceleration method and system based on error optimization cache, and the method comprises the steps: extracting priori knowledge, and obtaining the output of an attention layer and a multi-layer perceptron layer of each module in a sampling process of a large model; a cache module needing to be optimized is judged based on the error size and the relative position of the module in the sampling process; and for the cache module which is judged to be required to be optimized, introducing trend information in the priori knowledge to perform cache optimization. According to the method, the trend of priori knowledge is mapped to the sampling data, so that the stability and the quality of a generated result are kept while the calculation efficiency brought by cache is enjoyed.