718 results about "Logical reasoning" patented technology

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.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

The invention discloses a customer service data quality inspection method and device based on dynamic reasoning, equipment and a medium, and the method comprises the steps: analyzing customer service dialogue data, and generating structured dialogue data containing an intention label and a key problem node; according to an intention label in the structured dialogue data, a framework is generated through retrieval enhancement, and matched domain knowledge is recalled in real time from a standard knowledge base so as to construct a dynamic context knowledge graph; taking the dynamic context knowledge graph as input, utilizing a thinking chain prompt template to guide a large model to carry out step-by-step logical reasoning, and outputting a preliminary quality inspection conclusion chain; performing retrieval verification on assertion nodes in the initial quality inspection conclusion chain to generate a traceable final quality inspection conclusion; and based on the final quality inspection conclusion, calculating a quantitative score of the customer service dialogue data and generating a visual thinking chain report. And through dynamic retrieval of the knowledge base and logical reasoning, the accuracy, interpretability and adaptability of customer service data quality inspection are improved.

The embodiment of the invention relates to the technical field of video processing, and particularly discloses a video timestamp event identification and reasoning method based on a multi-modal large model. According to the embodiment of the invention, an input video is processed, and visual information and text information are extracted; performing interaction and fusion on the visual information and the text information to generate a video event semantic representation; and performing event identification on the semantic representation of the video event to obtain an event timestamp mark and event logical reasoning. According to the method, deep fusion and semantic association understanding of multi-modal information such as vision and texts can be realized, the problem that a traditional method is insufficient in semantic consistency and feature expression ability among modals is solved, and deep fusion of multi-modal features and accurate understanding of semantic association are realized; and the frame-level time information can be used as a visual code and a text feature to perform visual text feature alignment, and then video-level modeling is performed, so that the problem that a traditional model cannot directly capture an accurate time frame when a visual event occurs is solved.

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

The invention discloses a multi-modal bill processing method based on dynamic knowledge enhancement. The multi-modal bill processing method comprises the following steps: S1, constructing a dynamic knowledge base containing an aging weight; s2, synchronously processing text, image and format features of the bill by adopting a multi-modal feature fusion network to generate a composite feature vector; s3, semantic-level, format-level and timeliness three-level fusion retrieval is carried out based on the composite feature vector, and a three-level fusion retrieval engine comprises dynamic weighted sorting with timeliness attenuation, a difference degree triggered artificial review mechanism and a policy sensitive slope adjustment algorithm; s4, setting a multi-expert cooperative verification system, wherein the multi-expert cooperative verification system comprises cooperative work of a rule engine, a large language model and a logical reasoning module; s5, implementing a dynamic knowledge updating mechanism, and automatically triggering incremental learning of the knowledge base when policy change or format update is detected; and S6, outputting structured data, and synchronously generating an auditing traceability chain containing a decision path. According to the method, the key field identification accuracy can be improved, and auditing traceability and non-perceptual increment updating in the whole process are realized.

The invention provides a digital human interaction control method and device fusing emotion semantics and logical reasoning and a storage medium. The method comprises the following steps: analyzing multi-modal input data of a user, constructing emotion-semantics joint representation, and generating a logic decision path; and through a cognitive fusion module, emotion-semantic representation and a logic decision path are fused, and an interaction response adapting to emotion and logic consistency is generated. The system optimizes an emotion semantic model and a logical reasoning rule on line according to user feedback and interaction history, and real-time interaction of emotion dynamic and logical rules is achieved. The system can dynamically adjust the logic decision path based on the multi-mode emotional state of the user, and improves the naturalness and situation adaptability of interaction. A dynamic time warping algorithm and a factorization machine are introduced to process a multi-modal feature fusion problem, and the accuracy and robustness of emotional state recognition are improved. The online optimization mechanism enables the model and the rule to be evolved continuously, and reasoning errors are corrected automatically through user feedback, so that error circulation is avoided.

The invention discloses an industrial system automatic fault diagnosis method based on a large language model. According to the method, a three-layer mapping system of industrial data, natural language description and knowledge reasoning is constructed, field multi-source sensor data are subjected to semantic conversion, and a quantitative calculation model based on a large language model is constructed based on historical data and logs. And a fault case is matched in real time with the help of a retrieval-enhancement generation technology to serve as a reference, the fault case and abnormal information are input into a knowledge reasoning model based on a large language model together, a structured logical reasoning chain is generated, and a diagnosis conclusion containing candidate faults, cause analysis and disposal suggestions is further output. Meanwhile, through user feedback and a reinforcement learning mechanism, the model and the knowledge base are adaptively updated, the defects of traditional static rules and expert experience are effectively overcome, the accuracy, interpretability and robustness of fault detection are remarkably improved, and the method adapts to complex and changeable working condition requirements.

The invention relates to the technical field of multi-mode interpretable decision scheme design, in particular to a multi-mode interpretable decision method and system and electronic equipment. According to the method, the intelligent decision-making level in the industrial manufacturing environment can be effectively improved through dynamic knowledge path optimization, multi-modal data fusion, symbol reasoning and self-adaptive feedback. A reinforcement learning driven dynamic knowledge retrieval technology is introduced, so that efficient organization and retrieval of multi-source heterogeneous data are realized; reasoning is enhanced based on the knowledge graph, the relevance between cross-modal data is improved, and the reasoning logic between the data is clearer and more reliable; and in combination with an ontological reasoning mechanism, the interpretability and transparency of the system are enhanced, so that the system conforms to causal derivation rules in industrial production. According to the technical scheme, dynamic knowledge path optimization, ontology reasoning and multi-modal data fusion are combined, and an efficient, accurate and explainable industrial manufacturing decision-making scheme is provided.

The invention relates to the technical field of data optimization, and discloses a financial data warehouse intelligent SQL (Structured Query Language) optimization method based on a large model, which comprises the following steps: S1, SQL statement analysis and preprocessing: carrying out word segmentation, grammatical analysis and logic structure analysis on an SQL statement submitted by a user, and identifying keywords, table structures and query conditions in query; s2, large model semantic understanding and optimization suggestion: performing semantic understanding and logical reasoning on the analyzed SQL statements by using a large model, and automatically generating optimization suggestions, the optimization suggestions including index recommendation, query rewriting and execution plan optimization; and S3, generating and selecting a dynamic execution plan. According to the method, the advantages of a large model in the aspects of natural language processing and intelligent decision making are fully utilized and combined with the specific requirements of the financial data warehouse, and the execution efficiency of SQL query and the overall performance of the system are remarkably improved; the query response time of the financial data warehouse can be effectively shortened, and the resource utilization rate is improved.

The invention discloses a software test case generation method and system based on a large language model, and relates to the technical field of software testing, and the system comprises a to-be-tested module detection subsystem which is used for constructing a to-be-tested module set; the test case generation subsystem is used for constructing a prompt template according to the to-be-tested module and generating a test case by utilizing a program generation model based on an adaptive focus context generation mechanism and the prompt template; the correction verification subsystem is used for performing multi-stage correction processing on the test case, eliminating errors and generating an initial test suite; and the test case supplement subsystem is used for carrying out coverage rate analysis and variation test on the initial test suite, constructing a model input text according to a result, and generating a supplement test case by utilizing a program generation model so as to update the initial test suite and obtain a final test suite. By combining the functional annotation of the to-be-tested module with the logical reasoning ability of the LLM, misjudgment caused by assertion errors can be reduced, and the accuracy of test feedback is ensured.

The invention discloses a junior high-school English reading understanding test question intelligent analysis method based on a large model, which comprises the following steps: S1, obtaining answer texts of junior high-school English reading understanding test questions completed by students and corresponding test question texts, and answers are from published standard answers; s2, preprocessing the input test question text and answer text; s3, performing semantic level deep analysis on the test question text by utilizing a deep context understanding large model; s4, according to the test question text and the standard answer, in combination with understanding of the logical reasoning large model on text context and logical relationship, analyzing the semantic matching degree of the student answer, and based on the standard answer and the logical reasoning large model, generating a semantic and logic comprehensive evaluation result of the student answer; s5, dynamically generating learning suggestions based on the semantic and logic comprehensive evaluation result of the student answers; and S6, outputting a test question analysis report. According to the invention, the answers of the students are analyzed more accurately, and targeted learning guidance is provided, so that the teaching effect and the learning experience of the students are remarkably improved.

The invention provides a mobile robot operation efficiency evaluation method and system based on the industrial internet, and relates to the technical field of operation efficiency evaluation, and the method comprises the steps: collecting multi-mode operation data, selecting a transmission channel, uploading the data to an edge calculation node, carrying out the preprocessing, completing the information, and carrying out the filtering operation, generating a first semantic feature and an operation feature descriptor; selecting an optimal transmission path and a service function chain, uploading the operation characteristic descriptor to a cloud server, carrying out real-time analysis on the operation characteristic descriptor, generating an initial operation efficiency evaluation result, carrying out analysis and comparison, determining an efficiency bottleneck, and generating an initial optimization improvement suggestion; sending an initial operation efficiency evaluation result to an edge computing node, performing semantic enhancement and logical reasoning, generating a refined efficiency evaluation report, analyzing an incidence relation and an influence mechanism among different evaluation indexes, determining key efficiency bottleneck factors, generating a comprehensive optimization suggestion, collecting operation data in real time, and generating a comprehensive efficiency evaluation report.

The invention relates to the technical field of intelligent assistant systems, and discloses an intelligent assistant system and method based on an AI technology, and the system comprises a user interaction module, a knowledge graph module, a natural language processing module, and a reasoning and decision module. The user interaction module realizes input and output interaction with a user; the knowledge graph module constructs a multi-domain knowledge system; the natural language processing module uses a BERT model to understand user intentions; and the reasoning and decision-making module fuses rule reasoning and machine learning prediction to realize accurate response. Particularly, an index construction unit is arranged in the reasoning and decision-making module to improve the retrieval efficiency, a reasoning engine adopts a Markov logic network to carry out logical reasoning, and a decision-making optimization unit continuously optimizes a decision-making strategy through reinforcement learning. The AI technology is comprehensively applied, so that the knowledge coverage, the response accuracy, the logical reasoning ability and the decision intelligent level of the intelligent assistant system are remarkably improved, and more efficient and convenient intelligent assistant services are provided for users.

The invention relates to the technical field of data processing, and discloses an intelligent question answering method and device fusing a knowledge graph and a large model, and a storage medium, and the method comprises the steps: obtaining a to-be-answered original question; performing entity extraction on the original problem to generate an entity information set, and performing problem rewriting to generate similar problems of the original problem; traversing each piece of entity information in the entity information set, and searching and screening in a preset knowledge graph based on a search strategy fusing similar problems to obtain a knowledge path; obtaining knowledge according to the knowledge path, and according to the task instruction and the original question, performing logical reasoning by combining an answer generation large model with the obtained knowledge to generate a prediction answer of the original question; and carrying out authenticity detection on the predicted answers, filtering the predicted answers, and generating target answers. According to the method, the possibility that a large model generates unreal or irrelevant information is reduced, the calculation cost is reduced, the illusion problem of generating answers by the large model is relieved, and the credibility and the accuracy of the answers are improved.

The invention discloses an enhanced large language model processing method, system and platform based on Monte Carlo tree search and a storage medium. The method comprises the following steps: creating a first model corresponding to a big language, generating and obtaining a data set corresponding to a logical reasoning process, and performing fine tuning processing on the first model according to the data set; dividing the search tree into at least two initial paths based on task requirements, allocating at least two nodes to each path for independent sub-tree expansion, generating at least two candidate outputs based on a first model in each sub-tree, and dynamically adjusting and processing a corresponding cluster width in combination with confidence data; independently simulating and processing the expansion path of each sub-tree, generating corresponding first data, backtracking, updating and processing confidence data of each node according to the first data, and generating corresponding second data at the same time; wherein the second data is the reasoning response data after enhancement optimization processing, and the corresponding system, platform and storage medium can improve the reasoning processing efficiency and accuracy.

The invention provides a large model patent atlas construction method with constraints. The method comprises the steps that a TRIZ rule table is constructed, TRIZ rules of each process are matched by inputting atlas states and atlas construction processes, the TRIZ rules of the processes form a process TRIZ rule table, TRIZ rule matching is carried out according to each process, and TRIZ rule matching results are obtained; converting the updated constraint condition cluster into a logical reasoning rule, performing constraint condition cluster classification on knowledge in the graph, judging whether the graph knowledge violates constraint conditions or a dependency set according to the logical reasoning rule, and if the graph knowledge violates the constraint conditions or the dependency set, triggering early warning; and carrying out correlation analysis on the updated constraint condition cluster by adopting an expectation maximization algorithm, iteratively optimizing the constraint condition cluster until the correlation of the constraint condition cluster tends to be stable, and updating a logical reasoning rule and an early warning mode according to the optimized constraint condition cluster.

The invention relates to an electric power data security compliance management method and system based on intelligent grading, and the method comprises the following steps: S1, obtaining electric power multi-source data, and carrying out the preprocessing of the data; s2, constructing an electric power security knowledge graph according to the preprocessed electric power multi-source data, and automatically extracting entities and relationships thereof from text data by using a Transform-based model; s3, analyzing the cross-regional data through a federated learning model, predicting the sensitivity change of the data, and performing logical reasoning by utilizing rules in the electric power safety knowledge graph and combining real-time data to generate a dynamic grading result; s4, a user credit evaluation model based on MPNN is adopted to analyze user access behaviors, user credit values are evaluated, and a dynamic access strategy is generated; and S5, based on the generated dynamic access strategy, centrally collecting logs through an SIEM system, analyzing log contents by using an NLP technology, extracting abnormal behaviors, and automatically isolating related accounts. According to the invention, the accuracy and adaptability of data compliance management are remarkably improved.

The invention discloses an academic paper retrieval method and device based on large language model driving, and a medium, and the method comprises the steps: obtaining a preliminary academic paper retrieval result through retrieval of a plurality of academic paper databases according to an input retrieval request; combining the preliminary academic paper retrieval results, and performing weighted sorting processing according to correlation, popularity and time; inputting the sorted preliminary academic paper retrieval results into a pre-trained first large language model to obtain article abstracts of academic papers and comparative analysis results of similar academic papers; wherein the training process of the first large language model comprises the steps of constructing diversified task samples including abstract generation, question and answer, keyword extraction, logical reasoning, information extraction and literature review generation, and constructing a total loss function so as to train the first large language model; the total loss function is the weighted sum of abstract generation, question and answer, keyword extraction, logical reasoning, information extraction and literature review loss functions.

The invention provides a satellite group task planning method and system based on a fine tuning large language model, and relates to the technical field of satellite group task planning. The task planning method comprises the following steps: processing a task planning request through a constraint analysis-tagging large language model to obtain a structured sample containing question definition, a reasoning process and a final answer; processing the structured sample through a pre-trained large language model oriented to satellite task planning domain knowledge alignment to obtain a preliminary satellite task planning scheme; and finally, carrying out iterative optimization on the preliminary satellite task planning scheme by utilizing a heuristic dynamic tuning framework to obtain a large-scale satellite group task planning scheme. According to the method, the problem of large-scale satellite group task planning is solved by utilizing the logical reasoning ability of a large language model, satellite task planning domain knowledge fine tuning is performed through satellite task planning fine tuning data based on a thinking chain, and a large-scale satellite group task planning scheme is efficiently generated with high quality in combination with a heuristic dynamic tuning framework.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses a big language model logical reasoning method, device and equipment, a storage medium and a product, and relates to the technical field of big language models.The method comprises the steps that a logical reasoning tree is constructed according to a preset problem, an intermediate step generated by a logic big language model and a reasoning path; alternately training the logic big language model and the logic tree scoring model to obtain a trained logic big language model and a trained logic tree scoring model; and according to the trained logic tree scoring model, evaluating a plurality of next-step reasoning paths generated by the trained logic big language model according to the logic reasoning tree to obtain an optimal reasoning path. According to the method, the training logic tree scoring model is used for replacing an existing prompt large model scoring strategy, a prompt or a high-level large language large model does not need to be preset in the scoring process, and the logic tree scoring model and the logic large model are alternately trained, so that the accuracy of model reasoning is improved in the face of complex problems.

A method, apparatus, and system for training a language model for enhanced consistency include selecting at least a portion of the content data of the language model, generating reasoning statements in the form of natural language relevant to the selected portion of the content data, and training the language model using the generated reasoning statements such that a logical inference of the trained language model in response to a prompt directed to the selected portion of the content data is increased as compared with the logical inference of the language model in response to the same or similar prompt before the training of the language model to enhance the consistency of the language model with respect to the selected portion of the content data. The trained language model can be used to generate a logical inference having enhanced consistency for at least a portion of content data.

Disclosed in the present application are a question answering method and apparatus based on a temporal knowledge graph. The method comprises: acquiring a target question; determining a plurality of first entities in the target question, and determining first semantic role information in the target question, wherein the first semantic role information at least comprises a subject, a predicate, an object and time information; determining from a target temporal knowledge graph a plurality of pieces of first knowledge associated with the plurality of first entities, and determining a first entity relationship between second entities in each piece of first knowledge; determining a piece of first knowledge among the plurality of pieces of first knowledge as target knowledge, which piece of first knowledge corresponds to the first entity relationship matching the first semantic role information; and performing temporal logic reasoning on the basis of the time information and the target knowledge, so as to obtain an answer to the target question.

The invention discloses a semantic analysis and generation method and system of an operation order, a medium and equipment, and belongs to the field of power system automation. The method comprises the steps that a task text of power grid operation is acquired, semantic analysis is conducted on the task text according to a generative model, and an analysis result is obtained; performing logical reasoning on the analysis result according to a preset knowledge graph to obtain a corresponding operation sequence; wherein the knowledge graph comprises nodes and edges between the nodes, each node comprises power grid equipment and an operation state, a running state and an operation rule of the power grid equipment, and each edge comprises an operation relation and a regulation dependency constraint between the nodes; processing the operation sequence according to a preset formatting rule to obtain a corresponding first operation ticket; and based on a preset operation rule set, performing logic verification on the first operation ticket to obtain a final operation ticket. Therefore, by implementing the method and the device, the problem that logical reasoning and semantic understanding capabilities are insufficient when the operation ticket is generated in a dynamic scene in the prior art can be solved.

The invention discloses a method and device for evaluating the logic conflict detection capability of a large language model, and belongs to the technical field of large language models.The method comprises the steps that a logic conflict mode is summarized according to an ontology containing logic conflicts; constructing a positive example data set and a negative example data set based on the logic conflict mode and the corresponding ontology; respectively combining the positive example data set and the negative example data set with multiple different types of pre-designed logic conflict detection cues, and asking questions for the large language model; according to the test result of the large language model, the logic conflict judgment and interpretation capability of the large language model is evaluated; according to the method, the logic conflict mode and the logic reasoning rule are considered, the complex context can be better understood, and the accuracy of logic conflict detection is improved.

The invention relates to an automatic assembly system and method for multi-scale digital twin components, and the system comprises a semantic relationship construction module which is used for carrying out explicit definition on the structural features, functional attributes, spatial layout requirements and logic dependency relationships of the multi-scale components, and constructing semantic relationships among the three types of components; the semantic reasoning and constraint engine module is used for carrying out logical reasoning through the semantic relationship constructed by the semantic relationship construction module and judging whether the component combination meets the assembly constraint or not; the assembly generation and configuration module is used for generating an assembly topological structure and a connection sequence of a system shelf according to the component candidate set output by the semantic reasoning and constraint engine module; and the man-machine interaction and visualization module is used for supporting a feedback closed loop between the engineer and the system and providing a visual display interface. The problems that an assembly method depends on artificial experience and is difficult to support high-frequency and multi-scene production line reconstruction are solved, and the method has higher semantic interpretation capacity, automatic combination capacity and context adaptive capacity.

The invention relates to the technical field of structured retrieval, and discloses a retrieval enhancement generation method and system based on LLM structured index and vector hybrid retrieval, and the retrieval enhancement generation method based on LLM structured index and vector hybrid retrieval comprises the steps of processing and analyzing a non-structural document, generating a hierarchical tree data extraction with document logic, and generating a hierarchical tree data extraction with document logic. The method comprises the following steps: obtaining a query demand, analyzing and identifying the query demand, self-matching any one or a combined retrieval strategy to quickly and accurately obtain a retrieval result in the complete logic positioning information, optimizing hierarchical tree data by adopting context perception, and establishing the complete logic positioning information in the hierarchical tree data through a reasoning path generation method. According to the method, through structured indexing, accurate positioning of specific chapters of the document, improvement of retrieval accuracy and support of multi-step logical reasoning, the long tail problem which cannot be processed by traditional RAG is solved, a clear and transparent retrieval path can be provided, and the system credibility is enhanced.