23 results about "Knowledge use" patented technology

Systems, methods, and computer-readable media provide a context-specific prompt to answer a user query. The systems, methods, and computer-readable media determine a context based on content of a natural language request and / or determine a role of a user who submitted the natural language request. Additionally or alternatively, templates or RAG sources that will be used for prompt generation may include financials domain-specific knowledge or other domain-specific knowledge or insights. Inclusion of this additional information in the prompt enhances the context to promote more accurate results from a large language model. In one embodiment, the prompt templates are created from various RAG sources, such as payables, general ledger, receivables, and asset management, containing structured data and information specific to the financial domain, enterprise, or other domain, which helps craft accurate prompts. A prompt is generated that identifies a subset of available fields and other selected information based on the role or other context. The prompt template may contain domain-specific knowledge uses a relevant domain or enterprise information to drive relevant results, and an executable query is generated by a large language model based on the prompt. The executable query causes data to be retrieved from a database to generate a result, and information is displayed based at least in part on the result.

The invention discloses a federal remote sensing large model training method and system based on low-rank self-adaption. The method comprises the following steps: performing personalized heterogeneous data knowledge learning by using respective remote sensing data, a pre-trained large model and a local low-rank self-adaption model; using the trained local low-rank self-adaptive model parameters to initialize global shared low-rank self-adaptive model parameters, performing collaborative federal training based on the pre-trained large model and the global shared low-rank self-adaptive model, and learning global remote sensing task domain knowledge; after alignment of local heterogeneous data knowledge and global remote sensing task knowledge is realized in a different-rank alignment fusion mode, a federated remote sensing large model composed of parameters of a pre-trained large model and an aligned local low-rank adaptive model is output, so that the parameter quantity and communication overhead uploaded by federated training are greatly reduced, efficient training of the model is ensured, and the reliability of the model is improved. And self-adaptive alignment of local heterogeneous data and global remote sensing tasks is also realized, and the universality and robustness of the model are ensured.

The invention discloses a method and a system for automatically generating a video based on multi-agent unstructured knowledge, and relates to the technical field of video automatic generation based on knowledge processing, and through a data source weight evaluation mechanism and a content semantic similarity calculation method, repeated or contradictory knowledge fragments are automatically identified, a disputed knowledge unit list is established, and the video is automatically generated according to the disputed knowledge unit list. And analyzing the matching degree of the dispute content and the preset script by adopting a semantic vector similarity algorithm. And for the recognized dispute knowledge points, a standby knowledge replacement algorithm is applied to retrieve replacement contents with high confidence from a reliable knowledge base, a knowledge unit replacement operation is executed, and high-quality video output with dispute identification is generated, so that the content accuracy and credibility of the multi-source knowledge fusion video are effectively improved. According to the method, a whole-process quality control system from knowledge identification to video generation is established, intelligent matching of knowledge credibility evaluation and a video content presentation mode is realized, and practical deployment of the technology is restricted.

This invention discloses an AI-based intelligent question-answering method, device, electronic device, and product for games, relating to the field of intelligent question-answering technology. The AI-based intelligent question-answering method for games includes: identifying entity objects in game consultation text through part-of-speech tagging; performing similarity matching between the game consultation text and various question templates in a pre-built question template library to obtain a target question template with the highest matching degree; constructing a graph database query statement based on the question type corresponding to the target question template and the entity objects in the game consultation text; querying data from a pre-built graph database based on game knowledge using the graph database query statement; and generating a response text for the game consultation text based on the query results. The AI-based intelligent question-answering method, device, electronic device, and product disclosed in this invention ensure that the generated response content corresponds to the user's intended question, improving the accuracy of intelligent question-answering for games.

The application discloses an operation and maintenance knowledge intelligent question and answer interaction method, device and equipment based on an LLM large model. The method receives a question from an operation and maintenance personnel through a natural language interaction interface, analyzes semantics by using a pre-trained LLM to generate a candidate answer, verifies the accuracy of the answer in combination with a dynamic operation and maintenance knowledge graph (containing a device model and fault code mapping, a historical case library and a real-time updating knowledge base), and finally returns a verification result. The device contains an input module, a processing module, a verification module and an output module, wherein the processing module integrates field knowledge fine-tuning and real-time data set functions, and the verification module supports multi-source data fusion and confidence evaluation. The platform adopts a multi-tenant architecture, supports visual diagnosis, automatic device operation and knowledge usage report generation, and ensures that data cannot be tampered with through blockchain technology.

The application discloses a method and system for automatically generating a video based on unstructured knowledge of multiple agents, relates to the technical field of video automatic generation based on knowledge processing, and automatically identifies repeated or contradictory knowledge fragments through a data source weight evaluation mechanism and a content semantic similarity calculation method, establishes a controversial knowledge unit list, and adopts a semantic vector similarity algorithm to analyze the matching degree of controversial content and a preset script.For the identified controversial knowledge points, a backup knowledge replacement algorithm is used to retrieve high-confidence replacement content from a reliable knowledge base, a knowledge unit replacement operation is performed, a high-quality video output with a controversial identifier is generated, and the content accuracy and reliability of the multi-source knowledge fusion video are effectively improved.The application realizes a whole-process quality control system from knowledge identification to video generation, realizes intelligent matching of knowledge reliability evaluation and video content presentation mode, and restricts practical deployment of the technology.

Regarding the technology of automatically generating code using generation AI based on specifications, we want to provide technology that enables generation AI to generate code that better conforms to the specifications. [Solution] The code generation device 100 includes a prompt generation unit 110 that generates a second prompt indicating a second specification and second reference information when a first automatically generated code generated based on an inference model 50 and a first prompt indicating a first specification is committed to a version control tool 51, a first finalized code generated by modifying the first automatically generated code based on the first specification is committed to the version control tool 51, and a second reference information relating to the knowledge used by the inference model 50 when generating code is generated based on the first specification, a first conforming code, a first non-conforming code, and a first corrected code.

The invention discloses an automatic driving reinforcement teaching learning method, device and equipment, a medium and a product. The method comprises the following steps: in a simulation platform, constructing a reinforcement learning abstract environment containing a state, an action and a reward function based on an automatic driving scene; obtaining multi-source trajectory data in the reinforcement learning abstract environment, wherein the multi-source trajectory data comprises a preset teaching state transition trajectory, a different-orbit state transition trajectory and a same-orbit state transition trajectory; clustering the multi-source trajectory data to obtain a clustering result, performing probabilistic sampling according to the clustering result, and constructing a mixed experience playback pool; and carrying out automatic driving reinforcement teaching learning based on the mixed experience playback pool. According to the scheme, teaching knowledge used in imitation learning is integrated into a traditional reinforcement learning method, and the sampling and utilization mode of a probabilistic sampling optimization sample is combined, so that the learning ability of the traditional reinforcement learning method is kept, and meanwhile, the automatic driving learning process is more stable.

The invention belongs to the field of motor engineering, and particularly relates to an inference type prior knowledge construction method and device for motor parameter identification, and the method comprises the steps: obtaining basic physical constraint prior based on multi-source parallel data of a target motor; performing cross-task learning on the multi-source parallel data of the target motor between different motor types and different working conditions to obtain hot start prior; based on one or two of basic physical constraint priori and hot start priori, obtaining non-independent identically distributed adaptation priori; collaborative iterative optimization is carried out on the non-independent identically distributed adaptation priori, and enhanced non-independent identically distributed adaptation priori is obtained; the basic physical constraint priori, the hot start priori and the enhanced non-independent identically distributed adaptation priori are integrated, and inference type priori knowledge used for motor parameter identification is obtained; according to the method, reasoning type priori knowledge with physical reasonability, cross-domain adaptability and reasoning reliability can be obtained, and the generalization ability and efficiency of the AI model in a motor parameter identification scene are improved.

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

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 present disclosure discloses a knowledge extraction method, system, device and storage medium. The knowledge extraction method comprises: obtaining and processing text data describing an object to determine a plurality of sentences and an object category corresponding to each sentence; generating a syntax relationship template for each sentence, the syntax relationship template comprising the part of speech of each word and the dependency relationship between the words; generating a syntax tree corresponding to each sentence through syntax analysis; matching the syntax relationship template and the syntax tree, and if the match is consistent, generating a candidate knowledge using the matched syntax subtree; using a knowledge prediction model to predict whether the candidate knowledge is correct knowledge; and using a knowledge classification model to process the predicted correct knowledge to identify the knowledge category corresponding to each piece of knowledge. The knowledge extracted according to the present scheme has the characteristics of low redundancy and high accuracy.

This application provides a method, apparatus, device, and computer storage medium for constructing a tag tree. The tag tree construction method includes: obtaining a tag tree construction request, the construction request including an object to be analyzed; parsing the construction request to determine key request information, the key request information including at least one of the following: the modal type of the object to be analyzed, and the field type corresponding to the construction request; determining associated tag knowledge corresponding to the construction request based on the key request information; and processing the construction request and associated tag knowledge using a tag tree generation model to generate a target tag tree corresponding to the construction request. In this embodiment, the associated tag knowledge is determined based on the key request information. Since the key tag knowledge can fully understand and operate on the professional terms and implicit knowledge corresponding to the object to be analyzed, the quality and efficiency of tag tree construction are effectively guaranteed when generating the target tag tree based on the associated tag knowledge.

The invention discloses a kernel data multi-modal knowledge graph construction method, and relates to the technical field of knowledge graphs. The kernel data multi-modal knowledge graph construction method comprises the following steps: S1, acquiring multi-modal data in the field of kernel science and technology, wherein the multi-modal data comprises text modal data and image modal data; s2, constructing a multi-modal knowledge graph ontology of the kernel data; s3, integrating target detection, relation prediction and character recognition technologies for the image modal data to realize automatic extraction of entities, relations and attributes; s4, extracting structured knowledge from the text modal data by adopting a rule-based method; and S5, realizing fusion of the image knowledge and the text knowledge through entity alignment, and storing a fusion result into a graph database to complete construction of the kernel data multi-modal knowledge graph. By adopting the multi-modal knowledge graph construction method for the kernel data, deep fusion and knowledge mining of the kernel data can be realized, and data support is provided for multi-modal question answering, retrieval and recommendation application of the kernel data.

The invention provides a request processing method and device based on a fast and slow double inference system and electronic equipment, and relates to the technical field of artificial intelligence such as large models, multi-agent collaboration, inference systems and content indexing. The method comprises the steps that a preset fast reasoning system is used for processing an instant request initiated by a first user to obtain an instant response, and the fast reasoning system generates the instant response based on the content index technology; in response to the fact that the matching degree between associated knowledge used for generating the instant response and the instant request is lower than the preset matching degree, the instant request is processed through a preset slow reasoning system, a request reply is obtained, and the associated knowledge is determined and obtained based on information related to the instant request extracted from a shared information base through the content index technology; the slow reasoning system generates a request reply based on task disassembly and multi-intelligence cooperation; and supplementing the new knowledge contained in the request reply to the shared information base. According to the method, the balance between the request processing timeliness and the reply quality is realized through the collaborative architecture of the fast and slow double-inference system.

The system according to this embodiment aims to generate interfaces tailored to the engineer's level and knowledge using natural language. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a generated screen unit. The reception unit receives instructions in natural language. The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The execution unit executes the commands generated by the generation unit. The generated screen unit generates a user-specific settings screen based on information from the device.

The invention discloses an information processing method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: obtaining a target question in a target knowledge field; carrying out vector conversion processing on the target problem by adopting an embedded representation model to obtain an initial vector; vector updating processing is carried out on the initial vector through a target neural network model to obtain a target vector, and the target neural network model is obtained based on reference knowledge training of the target knowledge field; and performing knowledge search in the reference knowledge of the target knowledge field according to the target vector, and determining target reply information corresponding to the target question according to a search result. According to the method and the device, the technical problem of low domain knowledge question and answer accuracy caused by low accuracy of vectors obtained by processing domain knowledge by adopting a universal embedded representation model under the condition that the universal embedded representation model cannot be finely adjusted in a question and answer scene in related technologies is solved.

This invention discloses a method and system for controlling the surface quality of thin-walled parts milling based on a dynamic knowledge graph, belonging to the field of intelligent monitoring technology for thin-walled parts. The method includes: transforming the milling mechanism into structured knowledge, defining entity categories and semantic relationships, and constructing a dynamic knowledge graph representing the cutting mechanism; acquiring single-channel vibration signals, which, after adaptive filtering and feature extraction, are input into a pre-trained lightweight monitoring model to output real-time surface roughness estimation data; using the surface roughness estimation data as real-time state input and synchronously updating it to the dynamic knowledge graph; and establishing a fault cause confidence scoring model by combining real-time monitoring data and prior knowledge. This invention outputs surface roughness data in real-time based on an adaptive filtering strategy and a lightweight surface roughness perception model, and integrates real-time monitoring data with domain expert knowledge using a dynamic knowledge graph to form a closed-loop control scheme for precise surface quality control of thin-walled parts milling.

This invention discloses a multi-strategy retrieval intelligent question-answering system based on knowledge graphs. Through the collaboration of question parsing, knowledge retrieval, answer generation, and evaluation modules, intelligent question answering is achieved: The system receives user questions, parses the question intent using a large language model, and extracts entities, relation types, and query types; it then performs a multi-strategy progressive retrieval based on a graph database knowledge graph, sequentially performing batch exact matching of entities, batch querying of relations, full-text index search, fuzzy matching, and source file keyword search; it scores and filters the retrieval results based on relevance; it generates answers based on the filtered knowledge using a large language model and calculates the confidence level of the answers by integrating multiple factors; and it implements result caching. This invention, through the synergy of multi-strategy progressive retrieval and intelligent filtering, effectively solves the problems of inaccurate retrieval, poor answer quality, and slow response speed in traditional question-answering systems, significantly improving the accuracy, recall, and response speed of question answering.

The application provides a microwave filter debugging method based on knowledge-driven particle swarm optimization, mainly including data collection, data-driven knowledge model establishment, debugging knowledge extraction, knowledge-driven particle swarm optimization, debugging process model construction and debugging according to the optimization result. The microwave filter debugging method based on knowledge-driven particle swarm optimization has the beneficial effects that a series of evaluation functions are set for each performance index of the microwave filter, the filter performance is effectively comprehensively evaluated, the knowledge model is established through a large amount of data, the debugging knowledge is mined, the particle swarm optimization algorithm is driven by using the debugging knowledge, the optimization can be effectively avoided from falling into local optimization by setting the optimization range of each adjustable parameter, and the debugging efficiency is significantly improved.

Systems, methods, and computer-readable media provide a context-specific prompt to answer a user query. The systems, methods, and computer-readable media determine a context based on content of a natural language request and / or determine a role of a user who submitted the natural language request. Additionally or alternatively, templates or RAG sources that will be used for prompt generation may include financials domain-specific knowledge or other domain-specific knowledge or insights. Inclusion of this additional information in the prompt enhances the context to promote more accurate results from a large language model. In one embodiment, the prompt templates are created from various RAG sources, such as payables, general ledger, receivables, and asset management, containing structured data and information specific to the financial domain, enterprise, or other domain, which helps craft accurate prompts. A prompt is generated that identifies a subset of available fields and other selected information based on the role or other context. The prompt template may contain domain-specific knowledge uses a relevant domain or enterprise information to drive relevant results, and an executable query is generated by a large language model based on the prompt. The executable query causes data to be retrieved from a database to generate a result, and information is displayed based at least in part on the result.

The application provides a network attack traceability evidence method, system and storage medium, the method comprising: obtaining attack data with attack characteristics and attack attributes; searching the knowledge base for information collection knowledge matching the attack type of the data, including the information collection dimensions and tools of the attacked device in historical cases; determining the information collection dimensions and tools of the attacked device based on the attack data and the knowledge using a large model; pushing the information collection tools to the attacked device to collect device information according to the information collection dimensions; searching the knowledge base for traceability evidence knowledge matching the device information, including the device information, attack data and traceability evidence results in historical cases; outputting the traceability evidence results based on the device information, knowledge and attack data using a large model, including the results of verifying whether the attack characteristics exist, the results of determining whether the device is subject to potential attacks and the corresponding types, the traceability results of the attack path, and the evidence for each result.

This invention discloses a user group knowledge transfer recommendation method and system based on a large-scale model. The method includes: classifying users with rich interaction information in the training set as seed users, collecting their positive and negative interaction data, and extracting their structured semantic knowledge using a large language model; vectorizing and encoding the semantic knowledge and using a clustering algorithm to divide user groups, then constructing group prototype knowledge based on the group semantic knowledge; determining the group affiliation of remaining non-seed users based on the existing groups; transferring group knowledge to users through an adaptive feature fusion mechanism to generate enhanced user representations; and predicting user preferences and generating a recommendation list based on the enhanced user representations. This invention extracts structured semantic knowledge and constructs group prototypes through a large language model, and achieves effective transfer of group knowledge to individual users through an adaptive feature fusion mechanism. It solves the problems of insufficient group knowledge extraction and rigid knowledge transfer strategies in existing recommendation technologies, effectively improving the recommendation accuracy and diversity for new users and users with sparse interactions.