1201 results about "Question answering" patented technology

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

A method of question answering using enhanced retrieval-augmented generation according to an embodiment includes receiving, by a computing system, a user query, pre-processing, by the computing system, the user query to determine whether the user query is associated with malicious intent, retrieving, by the computing system, relevant data from a knowledge base by using a keyword index and a semantic index in response to determining that the user query is not associated with malicious intent, prompting, by the computing system, a large language model to generate an answer to the user query based on only the relevant data retrieved from the knowledge base, and receiving, by the computing system, the answer to the user query from the large language model in response to the prompt.

The invention provides a complex question and answer method and system based on adaptive task deconstruction and multi-modal evidence aggregation, belongs to the technical field of natural language processing, and designs a dynamic Few-shot prompt construction method based on dependency syntax fingerprints to ensure that a prompt template is matched with a question structure; the invention discloses a dynamic problem deconstruction method based on confidence evaluation and auto-reflection. The method comprises the following steps: recursively decomposing a problem tree by using a large language model; converting the problem tree into a standardized linear task execution sequence by a problem tree context dependence specification and task sequence generation method; obtaining a high-correlation evidence set of each task based on an evidence generation method of two-way recall and cross encoder rearrangement; and the task sequence is reasoned and dynamically optimized by a question answer extraction method based on double-strategy aggregation reasoning. According to the method, the accurate complex question and answer result can be provided on the premise of ensuring the question disassembling quality, restraining error propagation and comprehensively recalling evidences.

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention belongs to the related technical field of product detection, provides a multi-view zero sample anomaly detection method and system based on cross-modal prompt reasoning, and aims at solving the problem of multi-view zero sample anomaly detection by constructing core technologies such as multi-view pose estimation and alignment, static-dynamic prompt collaboration, vision-language progressive fusion, feature space semantic enhancement and the like. And a set of end-to-end anomaly detection and reasoning system is formed. Particularly, a collaborative mechanism of a dynamic learnable prompt pool and a static attribute prompt library is designed, deep fusion of prompts is realized through cross attention, and multi-view feature compression and semantic decoding are performed by adopting a visual angle self-adaptive hybrid expert model. Zero sample anomaly detection and visual question and answer performance is further improved on multiple industrial public data sets, and the method can be widely applied to industrial precision part quality inspection, intelligent manufacturing and other complex scenes needing high-precision and multi-view perception and semantic reasoning.

The invention discloses a product data question and answer method based on a large model and mixed retrieval and related equipment. The method comprises the following steps: constructing a double-track knowledge base based on multi-source heterogeneous product data; performing multi-dimensional analysis on the current query statement in combination with the large model to obtain a multi-dimensional analysis result; the multi-dimensional analysis result comprises a query vector, a structured query instruction and a keyword; inputting the query vector into a vector database for retrieval to obtain a vector retrieval result; inputting the structured query instruction into a graph database for retrieval to obtain an entity relationship retrieval result; inputting the keyword into the text knowledge fragment for keyword matching to obtain a keyword retrieval result; and combining the vector retrieval result, the entity relationship retrieval result and the keyword retrieval result, and sorting and screening the combined mixed retrieval result through a large language model to obtain target answer data. The method can significantly improve the accuracy and reliability of the retrieval result, and can be widely applied to the technical field of artificial intelligence.

The invention belongs to the technical field of artificial intelligence and educational informatization, and relates to a multi-mode knowledge graph enhanced intelligent teaching-assistant question-answering system. According to the method, the knowledge graph construction technology, the multi-modal content analysis technology and the large language model reasoning enhancement technology are comprehensively applied, and the semantic understanding, knowledge integration and reasoning generation capabilities of the intelligent teaching assisting system in an education and teaching scene are improved. The related technology comprises layout analysis of textbook documents, semantic description generation of image content, entity and relation extraction of text content, multi-modal knowledge graph construction and question and answer reasoning and natural language generation combined with the knowledge graph. Through cooperative application of the technologies, semantic interconnection can be carried out on various modal information such as texts, images and tables in the textbook, and a searchable and traceable textbook-level knowledge network is formed.

The invention discloses an elevator intelligent operation and maintenance question-answering system based on edge computing and computing network integration. The system obtains running state data in real time through an edge data acquisition module, and performs preprocessing and dynamic sampling optimization on an edge side; the computing network fusion analysis module performs multi-dimensional comprehensive analysis on the operation state data by utilizing computing power cooperation of the edge node and the cloud node to generate an elevator health state result and a fault risk result; the knowledge graph reasoning module executes semantic association and logical reasoning in the elevator knowledge graph according to the analysis result to obtain a fault diagnosis result and a fault reason speculation result; the intelligent question and answer interaction module analyzes questions input by a user based on a natural language understanding technology, and generates question and answer responses containing running state instructions, potential risk prompts and maintenance suggestions. According to the system, a complete closed loop from data acquisition, intelligent analysis and knowledge reasoning to semantic interaction is realized, and the intelligence, real-time performance and interpretability of elevator operation and maintenance diagnosis can be remarkably improved.

The invention relates to the technical field of data analysis, and discloses a feedback generation method and device based on a business knowledge graph, equipment and a medium, and the method comprises the steps: collecting multi-source business data, cleaning and segmenting the multi-source business data to form standardized text fragments, and carrying out the entity recognition and relation extraction of the text fragments to construct the business knowledge graph; receiving a natural language query instruction, extracting query intention features and key entity references, mapping the key entity references into the business knowledge graph, and performing association traversal under the guidance of the query intention features to obtain associated knowledge sub-graphs; and serializing the associated knowledge sub-graph into a knowledge background context, and jointly inputting the knowledge background context and a natural language query instruction into a pre-training language generation model to generate natural language feedback information. The method can be applied to business scenes such as financial science and technology, medical health and the like, semantic structuring is carried out on text knowledge, reasoning is carried out in knowledge association in combination with query semantics, accurate question answering is achieved, and knowledge retrieval efficiency is improved.

The invention discloses a large language model multi-source inference knowledge graph question answering method and system, and the method comprises the steps: extracting entities in a question, linking the entities with knowledge graph entities, and determining a subject entity set; searching paths in the knowledge graph by adopting a graph constraint reasoning method, and obtaining candidate answers and reasoning paths; detecting incomplete path coverage or answer dimension deficiency by using a large language model, triggering question decomposition to generate logically complementary sub-questions, and obtaining answers; establishing an inference source coordination layer, a coordination graph constraint inference source, a planning-retrieval inference source and a sub-problem inference source for multi-source evidence; and standardizing the output of each inference source into a structured evidence triple, sorting and selecting the first K evidences according to relevance, and generating a final answer by utilizing a large language model to carry out inductive inference. According to the method, reasoning can be dynamically adjusted, multi-source evidences can be effectively aggregated, logic consistency and semantic integrity are guaranteed, and the integrity and accuracy of knowledge graph multi-hop questions and answers are improved.

The invention provides an intelligent number asking method suitable for a sales data query scene, which belongs to the technical field of data processing and comprises the following steps of: (1) inputting a natural language; (2) dialogue type number asking; (3) accessing a plug-in model; (4) carrying out multi-modal analysis; and (5) carrying out platform service. According to the intelligent data asking system architecture, an industry knowledge graph is used as a cognitive footstone, a large language model is used as an interaction engine, and dynamic data association is used as execution blood vessel three-in-one. The core pain point of'water and soil disability 'in vertical industry application of a general AI model is solved, the method is a key for really changing the AI from'chatting' to'drying ', and unprecedented agility and accuracy are provided for data-driven decision making.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention relates to the technical field of building information, and provides a knowledge base construction and retrieval method and system based on a multi-source text in the building field, and the method comprises the following steps: a knowledge base construction stage: constructing a multi-dimensional metadata feature vector for a multivariate text based on a standard classification index table; the method comprises the following steps of: converting and segmenting a document, splicing an end clause with all superior title texts by utilizing a context inheritance algorithm to form a text unit with complete semantics, and performing dynamic filtering based on an analyzed query intention and a metadata vector at a user retrieval stage; then, in the screening set, performing fusion calculation on semantic vector similarity, keyword matching degree and authority offset weight based on effectiveness attribute and implementation time, and performing mixed retrieval and reordering on the text units; and finally selecting a text unit according to a sorting result and inputting the text unit into the large language model to generate answers. According to the method, high-precision and high-compliance intelligent retrieval and question answering of building domain knowledge are realized.

The invention provides a video question-answering system and method based on iterative multi-mode, and the method comprises the steps: carrying out the preprocessing of an input original video file and natural language query, extracting a key frame sequence, and generating an initial subtitle sequence; performing multi-granularity retrieval based on the preprocessed natural language query and the current subtitle sequence, and determining a candidate region; carrying out fine-grained frame selection in the candidate region by using a large language model, and identifying a key frame; based on the candidate area and natural language query, determining the type of the visual information to be supplemented and generating a multi-modal cue word corresponding to the type, and extracting the visual information by the visual language model according to the multi-modal cue word to update the subtitle sequence of the candidate area; generating a prediction answer by adopting a large language model and a visual language model; and judging the confidence of the generated predicted answer, and outputting a final answer. According to the method, the processing mode of video understanding can be optimized, and an accurate cross-modal coordination solution is provided through dynamic reasoning-sensing coordination.

The invention provides a medical insurance knowledge base automatic construction method based on multi-agent collaboration, and relates to the technical field of medical insurance knowledge base construction.The method comprises the steps that firstly, multiple agents with differentiated medical insurance knowledge backgrounds and functions are defined and configured, and standard behaviors are configured for all the agents; driving multiple rounds of dialogues of the agent by the initial query, collecting and fusing dialogue data to extract a core site and a semantic relation graph, constructing a collaborative decision model to reach a consensus, and compiling the collaborative decision model into structured medical insurance knowledge information; a third-party verification agent is introduced, a knowledge pedigree diagram is constructed based on knowledge entries and traceability data, credibility is calculated through an evidence theory fusion decision algorithm, conflicts are resolved, and suggested adoption entries are output; a knowledge base is stored and generated, a retrieval enhancement generation question and answer system is built, user feedback is recorded, regular new topic discussion and artificial expert intervention are combined, continuous learning and dynamic iteration of the knowledge base are achieved, and a structured medical insurance knowledge base can be automatically built and dynamically updated to support intelligent question and answer.

The invention relates to the technical field of natural language data processing, in particular to a question and answer method based on a large language model and an improved knowledge graph. The method comprises the following steps: 1, acquiring a natural language question input by a user, and preprocessing the natural language question to obtain a standardized question; 2, processing the standardized question based on an entity recognition module of an improved knowledge graph, positioning a core entity and a field to which the core entity belongs, and generating an entity-field associated pair; according to the question and answer method based on the large language model and the improved knowledge graph, structured fact support is provided by improving the knowledge graph, dual verification of an initial question and answer result is achieved in combination with a fact verification module, authoritative data in the knowledge graph is matched firstly, then an external authoritative data source is linked for supplementary verification, and fact consistency of output answers is ensured; compared with a pure large language model question-answering scheme, the method has the advantages that the fact error rate is greatly reduced, and the method is particularly suitable for the fields such as medical treatment and law which have strict requirements on fact accuracy.

The invention relates to the technical field of knowledge management systems, and discloses a knowledge full-life-cycle management system and a construction method, and the system comprises a technical platform and multi-modal perception layer, a calculation and arrangement engine layer, a unified API gateway layer, a multi-modal processing module layer, a knowledge graph layer, a cognitive reasoning layer and an application layer. Real-time collection and batch processing of multi-source heterogeneous data are achieved through a standardized SDK / API; spark / Flink is combined with Kubernetes to complete the ETL (Extract Transform Load) and resource scheduling of the multi-modal data; constructing an entity-relationship-attribute knowledge graph through cross-modal feature fusion; intelligent decision support is realized based on rule reasoning, graph calculation and GNN; the application layer provides intelligent questioning and answering, decision support and personalized recommendation services; the technical problems of knowledge islands, sharing barriers, knowledge statics and the like in the prior art are solved.

The invention provides a personal question and answer method based on an observation-recording-decision mechanism, which comprises the following steps: an intelligent agent captures RGB images and depth images from all directions through multi-view perception, is used for constructing a 3D scene graph and mapping the 3D scene graph to a 2D semantic map, and meanwhile, the intelligent agent marks each passing position on the 2D semantic map, so that the 3D scene graph is mapped to the 2D semantic map; and dynamically updating the weight of a non-visited position, reducing the selection probability of a boundary point in a marked passing region, based on a 2D semantic map in an observation stage, distinguishing whether a question can be answered or not by an intelligent agent according to an observed RGB picture, if so, directly generating a response, otherwise, navigating to a new region, and repeating the steps until an available or maximum step number is reached, and completing the question answering. According to the method, through construction of a semantic map, weight regulation and control navigation, and fusion design of special VLM analysis and double-criterion decision making, decision making of agent non-redundancy exploration and accurate question and answer is achieved.

This disclosure describes techniques and architecture provide automated contribution analysis for “why question” style NLQ answering, e.g., “why is revenue down in North America Q1 2022.” In particular, the techniques described herein combine multiple signals together including, for example, frequency of use of combinations of dimensions in previous NLQs (warm-start), statistical information about columns (e.g., entropy), correlation / co-occurrence between pairs of dimension columns, and correlation between dimensions and dates. This information is used with a set of heuristics and rules to pick the best set of dimensions as contributing factors for a particular metric over a particular time period and present an automatic contribution analysis to the users to give them insights into their data.

The invention discloses a medical visual question and answer model construction method based on a feedback loop. The method comprises the following steps: step 1, generating initial candidates; step 2, performing multi-dimensional candidate evaluation; 3, iterative optimization of feedback driving is carried out; step 4; and 5, efficient fine adjustment of model parameters: performing fine adjustment on a pre-trained multi-modal large language model by using the enhanced training data set constructed in the step 4 to obtain a final medical visual question and answer model. According to the method, a feedback loop mechanism is introduced, and cooperative and iterative self-correction and optimization are carried out on generated text interpretation (Ratio) and a visual localization region of interest (RoI), so that the fact reliability, the logic consistency and the visual strong relevance of a final output result are systematically ensured.

The invention relates to an ultra-long-range medical record diagnosis and treatment question answering system and method based on a dynamic time sequence semantic map, belongs to the technical field of medical artificial intelligence and information processing, and solves the problems that in the prior art, the inference ability is insufficient when ultra-long-range medical records are processed. The system comprises a pre-processing module for pre-processing overlong-range medical record information of a patient to generate standardized data; the medical entity recognition module is used for recognizing event entities and descriptive entities in the standardized data; associating a time attribute for the event type entity to generate a time sequence entity tuple; outputting a medical entity recognition result; the dynamic semantic map construction module is used for constructing a specific dynamic semantic map of the corresponding patient; the text-map dual-mode fusion query engine is used for analyzing the medical problem proposed by the user and carrying out text-map dual-mode fusion query based on the specific dynamic semantic map; and the diagnosis and treatment reply module is used for generating a diagnosis and treatment reply result according to the text-map dual-mode fusion query result.

An adaptive classification retrieval-augmented generation model system and a method, relating to the technical field of artificial intelligence. The system comprises: a query splitting module, a classifier module, a processing module, a user interaction module, and an evaluation and optimization module. The user interaction module is used to receive an original query of a user, and display a generated answer. The query splitting module is used to split the original query received by the user interaction module into multiple equivalent queries. By means of integrating a classifier module, the complexities of different tasks are effectively identified and, corresponding processing methods therefor are determined, so that the understanding and application efficiency of the augmented generation technology is optimized. Advanced RAG technologies and specially adjusted classification algorithms are combined, so that not only is the accuracy of question answering improved, but processing speed is also improved, and consumption of running resources is significantly reduced.

The invention relates to the crossing field of an artificial intelligence technology and an automotive electronic technology, and discloses a voice interaction question answering method and system based on a vehicle-mounted large model knowledge base, and the method comprises the following steps: receiving a user query text, and executing intention recognition and structured analysis; based on an analysis result, constructing and executing a mixed retrieval request including structured filtering, full-text keyword retrieval and semantic vector retrieval; fusing and reordering the plurality of retrieval result sets obtained respectively to obtain a high-quality final retrieval result set; and finally, constructing a context based on the final retrieval result set and a user query text, and calling a large language model to generate and output a final answer. By introducing a hybrid retrieval and fusion reordering mechanism, comprehensive context information is provided for a large language model, the accuracy and reliability of questions and answers are improved, and the ability of a system to understand and process complex fuzzy queries is enhanced.

The invention discloses a question answering method and device for credit review knowledge based on a large model and a medium, and the method comprises the steps: obtaining a to-be-processed credit review question, extracting a core keyword from the question through a natural language processing algorithm, and converting the question into a deep semantic vector through a semantic vector model; inputting the questions into an intention classification model, generating filtering labels, inputting the questions into a distributed full-text search engine, screening a target knowledge base according to the filtering labels, and performing retrieval in the target knowledge base based on the core keywords and the deep semantic vectors to obtain a preliminary retrieval result set; calculating the score of each preliminary retrieval result in the result set according to the core keyword, the deep semantic vector and the weight factor, and screening out final reference content in a descending order; and on the basis of credit and loan business compliance requirements, risk control rules and question and answer output specifications, the credit and loan prompt words are constructed, and the final reference content is input into the large model to obtain structured credit and loan review answers, so that the question and answer efficiency and accuracy are improved.

The invention provides an intelligent question and answer semantic understanding and enhanced rewriting optimization method based on multiple rounds of dialogues, and relates to the technical field of artificial intelligence semantic understanding, and the method comprises the steps: obtaining a current query statement and a historical dialogue context, carrying out the time sequence analysis of the historical dialogue context, and constructing a dialogue context coding representation; and performing multi-level semantic disassembly on the query statement to identify dominant and implicit intentions, generating a semantic enhancement rewriting statement based on an intention hierarchical structure, and finally performing semantic matching and retrieval to output answers. The understanding accuracy of the real intention of the user in multiple rounds of dialogues can be effectively improved, and the interaction experience and the answering quality of the intelligent question answering system are improved.

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph fused question answering system which comprises a knowledge graph construction module, a natural language understanding module, a knowledge graph query module and an answer output module which are connected in sequence. The knowledge graph construction module performs knowledge extraction on the unstructured text to obtain triple data to construct a knowledge graph; the natural language understanding module converts a question input by a user in a natural language form into a structured query statement; the knowledge spectrogram query module performs retrieval in a knowledge graph according to the structured query statement and returns knowledge sub-graphs related to the query statement; and the answer output module inputs the knowledge sub-graph into an end-to-end generative model based on a copy network mechanism, generates a natural and smooth text answer and presents the text answer to the user. Through cooperative work of all the modules, efficient knowledge extraction, accurate intention understanding, efficient knowledge retrieval and high-quality answer generation are achieved, and the question and answer requirements of users can be met more efficiently and accurately.

The invention provides an intelligent retrieval and question-answering system for knowledge base context awareness and traceability enhancement, and the system obtains an analysis result, an optical character recognition result and a semantic analysis result through the analysis of PDF physical layout, Word / Excel paragraphs, titles, tables and style attributes thereof, and the optical character recognition. Structured knowledge blocks, positions and levels of path images and vector representation are generated and stored in a vector database, and related results of user query requests are extracted by executing a mixed retrieval algorithm with keyword retrieval and vector semantic retrieval. Performing intelligent reordering by considering semantic similarity, keyword matching, knowledge block type weight, source knowledge base weight and page position weight to obtain a candidate knowledge block list, constructing cue words according to an intelligent reordering result, and calling an external large language model to generate answers; source labels in answers are managed to be associated with metadata of corresponding numbers in a candidate knowledge block list, and the problem that text blocks and traceability are not accurate is solved.

The invention discloses a knowledge intelligent analysis and question-answering method and system based on time sequence knowledge graph retrieval enhancement, relates to the technical field of natural language processing, and aims to solve the problem that a wrong answer is generated due to the fact that a model possibly retrieves semantic-related but time-inconsistent information due to a time-insensitive retrieval mechanism in an existing method. According to the method, a dynamic time sequence knowledge graph modeling and hierarchical interactive retrieval mechanism is introduced. According to the framework, entities and relationships are bound with specific timestamps through time annotation in a knowledge construction stage, and a dynamic graph capable of evolving along with time is formed; in a retrieval stage, through time query decomposition (decomposing a multi-time constraint complex query into single-time sub-queries, and gradually screening related knowledge by a three-layer interactive retriever (time sequence sub-graph retrieval, node-level retrieval and knowledge-level retrieval), time conflicts and redundant interference are reduced, and finally answers with consistent contexts and accurate time are generated.

The invention discloses a video anomaly detection method based on a traffic scene, and relates to the related field of computer vision, and the method comprises the steps: obtaining an input video clip which comprises a plurality of frames of images, traversing the plurality of frames of images, carrying out the selection through combining with a text prompt, determining a plurality of key frames, and enabling the text prompt to be input by a user; performing context generation based on the plurality of key frames to obtain position and time context information; the key frame, the position, the time context information and the text prompt are synchronized to a large visual language model for visual questions and answers of diversified traffic scenes, abnormal events are extracted, and the abnormal events comprise abnormal scores; and carrying out abnormal event detection analysis according to the abnormal score, and carrying out abnormal detection on the diversified traffic scenes. The technical problem that abnormal events in diversified traffic scenes are difficult to comprehensively and accurately detect in existing traffic scene-based video anomaly detection is solved, and the technical effect of improving the accuracy and generalization of traffic scene video anomaly detection is achieved.