419 results about "Logical reasoning" patented technology

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 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 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.

The invention discloses a question answering method and device based on retrieval enhancement generation, a medium and equipment, and relates to the technical field of computers. According to the method, related candidate sub-graphs are matched in an existing structured knowledge graph according to the query problem of a user, and the candidate sub-graphs are further judged to be insufficient to deal with the query problem through logical reasoning; according to the method, sparse keyword vectors of query questions based on surface vocabularies and dense question vectors based on context deep dependency are further extracted; matching the query question with a sparse semantic vector of each text block of the unstructured text based on surface vocabularies and a dense semantic vector of each text block based on context deep dependency, which are acquired in advance, so as to determine the text block related to the query question from the unstructured text; the candidate sub-graphs are further converted into graph structures to supplement the candidate sub-graphs, answers corresponding to the query questions are generated based on the graph structures, and the performance of questions and answers in multi-hop reasoning and information integration retrieval recall is improved.

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 a land space planning and surveying and mapping engineering collaborative measurement method, which comprises the following steps: collecting multi-source surveying and mapping data of a target area, and constructing a live-action three-dimensional semantic model bearing ground feature semantics through fusion processing; key planning elements are extracted based on the model, the spatial relation of the key planning elements is quantified, and a spatial relation graph with geometric entities as nodes and the spatial relation as edges is generated; the planning management and control terms are formalized into computable logic assertions, and a machine executable planning rule base is constructed; performing collaborative traversal and logical reasoning on the spatial relation graph based on the rule base, automatically comparing and identifying violation situations, and generating a planning conflict report; and carrying out fusion mapping on the report and the three-dimensional model to generate a three-dimensional visual review layer for intuitively indicating the position, type and degree of the violation. According to the method, deep fusion of surveying and mapping data and planning rules is realized, planning conformity review can be automatically and intelligently completed, and the problems that the prior art depends on manpower, and is low in efficiency and different in standard are effectively solved.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

The invention provides an AgenticRAG-based customer service multi-agent collaborative question-answering method and system in the technical field of artificial intelligence and natural language processing. The method comprises the following steps: S1, inputting a query statement into a Prompt template to obtain an initial cue word; s2, optimizing the initial cue word through a query and analysis Agent to obtain an enhanced cue word, performing query intention recognition and key entity extraction based on the enhanced cue word, and performing query complexity recognition based on the query intention and the key entity; s3, based on the query complexity, inputting the query intention and the key entity into a retrieval module to obtain retrieval data; s4, an answer generation module performs logical reasoning on the retrieval data, the enhanced cue words and the business rules based on the user portrait to generate reply answers; and S5, the answer evaluation module performs quality evaluation and safety evaluation on the reply answers and then outputs the reply answers. The method has the advantages that the adaptability of questions and answers, the semantic understanding depth and the logic continuity of multiple rounds of dialogues are greatly improved.

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 field of intelligent report generation, in particular to an intelligent insight report generation method and system based on an AI large model, and the method comprises the steps: inputting an insight demand, and generating an insight data package comprising insight contents, associated data and industry labels; extracting a basic keyword set of the insight data packet to form a mixed feature code; after mixed feature coding preprocessing, weight distribution is carried out; and after weight distribution of the AI large model, injecting a high-weight feature vector into a semantic understanding core layer, injecting a low-weight feature vector into a logical reasoning layer, and outputting analysis data to form an intelligent insight report. According to the method, word embedding parameters are optimized according to field semantic characteristics, a parameter verification mechanism is introduced, field text characteristics are adapted by adjusting vector dimensions, context windows and low-frequency vocabulary filtering threshold values, window parameter validity is verified through cosine similarity, word frequency threshold value reasonability is verified through standard deviation, and field text characteristic matching is achieved. And it is ensured that the feature vectors can accurately capture domain-specific semantics.

The invention discloses an automatic causal structure generation method based on semantic representation and logical reasoning of a large language model. The method comprises the following steps: acquiring an input text; performing semantic coding and clustering on the obtained input text by utilizing a large language model, and establishing a candidate causal variable set; causal relationship detection is carried out on the established candidate causal variable set based on anti-fact intervention and do-calculation; performing causal direction judgment, and generating a directed acyclic causal graph meeting logic consistency; and on the basis of the generated directed acyclic causal graph, natural language interpretation is generated by using a large language model, and logic consistency closed-loop verification is carried out. According to the method, automatic generation from the natural language to the causal structure is realized, the causal variable set is automatically extracted and constructed from the unstructured natural language text, the defects that variables need to be manually defined and modeling depends on field experts in the existing causal modeling process are avoided, and the labor cost and professional threshold of causal structure construction are remarkably reduced.

The invention discloses a multi-modal content generation method and system based on a large model and knowledge graph driving, and relates to the technical field of multi-modal content generation, a neural engine based on a multi-modal large model extracts bottom layer features of a content generation demand of a user through a cross-modal semantic encoder, and maps the bottom layer features to a unified demand semantic space; the neural engine generates a corresponding cross-modal original content prototype meeting the content generation requirement through the creative generator; through the architecture of neural engine creative generation, symbol engine knowledge constraint and double-engine dynamic collaboration, the cross-modal understanding and creative ability of a large model is inherited, the advantages of fact verification and logical reasoning of a knowledge graph are exerted, and finally multi-modal content generation of sky and sky as well as fact anchoring is achieved; and an efficient and reliable intelligent creation tool is provided for the fields of education, media, art and the like.

The invention relates to a query optimization method and device based on agent planning, electronic equipment and a medium. The method comprises the following steps: pre-retrieving a target tree structure index based on a user query request to obtain a pre-retrieval result set; generating a retrieval-reasoning double-stage directed acyclic graph task chain comprising a task type, a retrieval / reasoning target and a dependency relationship; executing each task according to a topological sequence, and obtaining a target text fragment through query rewriting and expansion; and performing multi-hop logical reasoning in combination with the task chain and the target text fragment, and outputting a final answer. Therefore, dynamic task planning of environment perception is carried out by constructing a retrieval-reasoning dual-stage directed acyclic graph task chain and combining a knowledge base tree structure index; the problems that when an existing retrieval enhancement generation system processes a multi-hop question and answer task, logic association understanding ability among retrieval fragments is insufficient, semantic matching precision is low, and a reasoning path is prone to deviation are solved, and the retrieval recall rate and the answer accuracy under a multi-hop question and answer scene are improved.

The invention belongs to the technical field of large models and data agents, and particularly relates to a question answering method based on a large model and a data agent, and the method comprises the steps: receiving and preprocessing a user question, and generating a question data set; judging whether external data support is needed or not by utilizing a large model, and if so, dynamically generating an agent calling strategy; real-time data acquisition, domain knowledge analysis, multi-source data fusion and logical reasoning verification intelligent agent cooperative execution tasks are scheduled according to a strategy; fusing the traceability information returned by the intelligent agent and the knowledge of the large model to generate a preliminary answer, and outputting the preliminary answer after interpretable optimization; and finally, optimizing the case library according to user feedback and iterating an agent calling strategy. All data agent processing results are attached with full-link traceability information, data sources and reasoning processes are presented in an explanatory optimization mode, and a user can verify answer basis.

The invention discloses a multi-modal large model training method based on a position reflection thinking chain, and the method comprises the following steps: S1, constructing position reflection type thinking chain data, and associating the position reflection type thinking chain data with the coordinates of a chart visual region through distinguishing the data extraction and logical reasoning steps in the thinking chain; automatically generating position annotation data through drawing code editing, re-rendering verification and image analysis technologies; and S2, training a structured reasoning model, constructing a multi-type instruction data set containing visual positioning and logical reasoning, jointly optimizing generation of answer prediction, position positioning and reasoning steps by adopting a multi-task loss function, and enhancing the perception ability of the model to chart elements through a bounding box reflection mechanism. According to the method, the problems of numerical illusion and lack of visual interaction of the thinking chain due to the fact that an existing model depends on OCR are effectively solved, the chart understanding accuracy and the thinking chain interpretation are improved, and the performance is remarkably superior to that of an existing method on the mainstream basis.

The embodiment of the invention discloses a standard text generation method and device based on a rule engine and a large model, electronic equipment and a computer readable storage medium, and provides a complete technical path from knowledge driving to structure output through the standard text generation method based on the rule engine and the large model. The defects in the aspects of grass drawing efficiency, intelligence and knowledge reuse in the prior art are overcome. According to the system, through collaborative design of the knowledge base, the rule engine and the large model, full-process intelligent drafting and detection of standard documents from classification to output are achieved, content logical reasoning and automatic term generation are achieved by building the standardized knowledge base and combining the rule engine, dependence on artificial experience can be effectively reduced, and the system is high in practicability and high in practicability. According to the method, the draft cycle is remarkably shortened, the consistency and normalization of standard texts are improved, the standard writing efficiency is improved, and the method has remarkable practical value and popularization prospects.

The invention provides a natural language-driven scientific research naming and ranking patient matching method and system, belongs to the technical field of artificial intelligence and medical information processing, and solves the problems of high manual workload, difficulty in semantic analysis, insufficient logical reasoning ability and the like in an existing cancer patient screening mode. The method comprises the following steps: analyzing a containing and arranging condition in a natural language text form in the tumor field to obtain a structured containing and arranging condition logic rule set; performing entity recognition, relation extraction and portrait construction on the multi-modal medical record of the cancer patient to obtain a comprehensive cancer portrait of the patient; and performing logic matching on the comprehensive cancer portrait of the patient and the structured accommodating and discharging condition rule set, determining whether the patient meets accommodating and discharging conditions, and generating a traceable matching interpretation path. The method can effectively solve the problems of large manual workload, difficulty in semantic analysis, insufficient logical reasoning ability and the like in the existing cancer patient screening mode.

The invention discloses a multi-mode general reasoning method, device and equipment and a medium. The method comprises the following steps: constructing a pseudo image reasoning thinking chain data set according to a preset first data set and a data set construction strategy; performing supervised fine-tuning training on a preset reference visual language model by using the pseudo-image reasoning thinking chain data set and a preset large model fine-tuning framework to obtain an initial multi-modal reasoning model; performing reinforcement learning training on the initial multi-modal reasoning model by utilizing a preset second data set, a reward rule and a group relative strategy optimization algorithm to obtain a target multi-modal reasoning model; and if multi-modal problem information sent by the client is received, inputting the multi-modal problem information into the target multi-modal reasoning model to obtain a corresponding reasoning thinking chain. According to the method, the deep reasoning ability of the benchmark visual language model is enabled, the complex logical reasoning and generalization ability of the model in the multi-modal reasoning task is improved, and the interpretability and accuracy of the complex reasoning task are met.

The invention discloses a space chain reasoning-based space intelligent multi-modal space understanding and planning method, relates to the field of space intelligence, solves the problem that the existing space intelligent multi-modal reliability and interpretability are insufficient, and comprises the following steps: S1, obtaining multi-modal space data; s2, constructing a spatial semantic state diagram; s3, task nodes are extracted, the spatial semantic state diagram is traversed, a logical reasoning list is constructed, and a reasoning track is obtained; s4, performing simulation operation on the motion subject model in the space world model based on the reasoning track, recording a simulation position and a simulation operation state, and optimizing the reasoning track to obtain an implementation track; s5, constructing a space cost function, and dynamically optimizing the implementation trajectory according to the space cost function; s6, the reasoning track is corrected; according to the method, the reliability and interpretability of space intelligence are effectively improved in a space chain reasoning mode.

The invention discloses a medical insurance field document intelligent question-answering system and method based on an AI large model, and belongs to the technical field of artificial intelligence. The technical problems that an existing medical insurance question-answering system depends on manual configuration and is poor in flexibility and low in intelligent level are solved. According to the technical scheme, the system comprises a document processing module for analyzing and segmenting original documents such as medical insurance policy documents, vectorizing the original documents and storing the original documents into a vector database; and the knowledge question and answer module is used for analyzing user questions by using an AI large model, driving AIAgent to retrieve related knowledge in a vector database, performing reasoning in combination with injected professional knowledge such as business dictionaries and analysis logic, and generating accurate and traceable answers. Through context learning, logical reasoning and automatic generation ability of a large model, tedious intention model training and manual QA configuration are not needed, the intelligent level, adaptability and user experience of a medical insurance question-answering system are remarkably improved, and the operation and maintenance cost is reduced.

The invention belongs to the technical field of electric power intelligent operation and maintenance, and relates to an electric power equipment fault prediction knowledge graph updating method which comprises the following steps: continuously receiving multivariate heterogeneous information from an electric power system operation environment, and applying at least one technology of natural language processing, time sequence analysis and statistical pattern recognition. Extracting new knowledge elements to form a to-be-verified new knowledge set, performing semantic consistency verification on the to-be-verified new knowledge set and an existing knowledge graph, detecting logic conflicts between the to-be-verified new knowledge set and existing knowledge triples and rules through logical reasoning, and obtaining a to-be-verified new knowledge graph based on a context environment where the logic conflicts occur. A conflict resolution strategy is automatically selected and executed by evaluating evidence reliability of new and old knowledge, and an optimized knowledge rule is generated, so that an existing knowledge graph is dynamically updated, the problems that an updating mechanism of the existing knowledge graph is lagged and evolution of context-related knowledge is difficult to process are effectively solved, and the reliability of the knowledge graph is improved. And the knowledge graph is pushed to be converted from static storage to dynamic evolution intelligent agents.

The invention provides a multilevel modular personal memory large model and a construction method thereof. The model comprises a receiving module for receiving information input by a user, converting the information into a unified text and language vector, storing a personal historical language vector and context information, extracting key features, analyzing intention, retrieving by using Elasticsearch and returning related context information, and generating a reply according to the related context information. According to the method, the multi-modal data processing capability is integrated, the traditional limitation is broken through, and image, voice and text associated storage is supported; constructing a dynamic memory enhancement mechanism to prevent important information from being covered; then an intention analysis module is innovated to accurately capture deep demands; the correlation and the adaptability of memory fragments are improved by adopting a multi-modal retrieval and graph structure correlation technology; and finally, through a language habit adaptation and logical reasoning module, the output conforms to the style of the user, potential requirements are deduced, a whole-process closed loop is formed, and breakthrough is achieved in the aspects of memory management, intention understanding and interaction naturalness.

The invention provides a large model logical reasoning optimization method and system combined with a knowledge graph, and relates to the technical field of artificial intelligence, first, a logical reasoning task to be processed of a large model and a corresponding structured knowledge graph are obtained, and the logical reasoning task comprises knowledge units, association relationships and association strength; the logical reasoning task comprises a reasoning target, a premise and a constraint condition, then obtaining a large model reasoning guide rule set based on suitability analysis, injecting the large model reasoning guide rule set into a reasoning process, capturing and adjusting intermediate reasoning nodes in stages, correcting reasoning branches deviating from an incidence relation, and obtaining a large model reasoning guide rule set; then conflict resolution and propagation prediction are performed on the reasoning process after stage guidance, an inconsistent conclusion is identified, conflict rationality is verified, a propagation link is predicted, a multi-round correction scheme is generated, and finally reasoning path iterative optimization, integrating degree and efficiency evaluation and jump sequence and reference priority adjustment are performed based on the reasoning process after conflict resolution. Information is integrated to obtain an optimization result, and the logical reasoning quality of the large model is effectively improved.

The invention relates to a virtual costume matching recommendation method and system based on a knowledge graph, and the method comprises the steps: obtaining virtual image data of a user, extracting image feature data of the user from the virtual image data, and constructing a dynamic user portrait in combination with the historical purchase record, social behavior data and preference setting of the user; constructing a costume knowledge graph, wherein the costume knowledge graph comprises costume attribute nodes and costume relation edges; based on a semantic matching algorithm of graph embedding, performing embedded representation on the dynamic user portrait and the clothing knowledge graph, calculating semantic similarity between the dynamic user portrait and the clothing knowledge graph, and generating a semantic matching result; based on a logical reasoning technology, according to scene requirements set by the user, reasoning to generate a clothing collocation combination of the user, and generating a logical reasoning result; and according to a semantic matching result and a logical reasoning result, generating a costume matching suggestion, and supporting a user to carry out interactive adjustment and feedback. The method and the device have the effect of improving the clothing recommendation accuracy.

The invention discloses a legal affair information analysis method and system, a computer device, a medium and a product. The method comprises the following steps: analyzing legal affair information to be analyzed to generate an initial structured text; analyzing the initial reference law article based on the standard law article database, and updating when a law article analysis result does not meet requirements; logical reasoning verification is carried out on the initial logic information, and when a logic chain analysis result does not meet requirements, updating is carried out; comparing the initial analysis conclusion with a preset legal knowledge graph, determining text segments with potential errors, and performing confidence analysis to obtain a corresponding confidence analysis result; and according to the optimized initial reference law article, the optimized initial logic information and all confidence analysis results, generating a comprehensive, accurate and reliable analysis scheme. According to the method, data dependence can be reduced, the problems existing when a general large model is applied in the legal field are effectively solved, model output illusion is greatly relieved, and legal information analysis quality is improved.

The invention relates to a power grid multi-modal data compliance monitoring method based on a knowledge graph, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data of a power grid, carrying out the preprocessing, and generating structured and semantic data representation; s2, constructing an initial knowledge graph according to the produced structured and semantic data representation; s3, according to the initial knowledge graph, combining a time perception mechanism TGN to identify compliance standards and laws of a power grid operation environment changing with time, combining rule-based logical reasoning with a deep neural model through neural symbol fusion, realizing compliance judgment under a fuzzy condition, and constructing a knowledge agent; s4, based on the knowledge agent, performing multi-modal compliance analysis in combination with deep learning and symbolic reasoning to obtain an analysis result; and S5, based on an analysis result, in combination with a natural language generation technology, automatically generating an interpretation report for the violation event. According to the invention, dynamic intelligent compliance analysis is realized, and the intelligent and automatic level of power grid management is significantly improved.

The invention discloses a Token conversion method and system for converting multi-modal information to a unified control instruction, and the method comprises the steps: obtaining a multi-modal perception information flow and instruction reference data, carrying out the feature deconstruction of the multi-modal perception information flow, recognizing a modal identification code, carrying out the cross-domain mapping calculation based on the modal identification code and the instruction reference data, generating an offset vector, and carrying out the Token conversion of the multi-modal information to the unified control instruction. Performing tensor synthesis on the offset vectors to construct a Token coding library; performing intention flow direction analysis according to the Token coding library to generate an intention propagation domain, performing semantic potential energy optimization on the propagation domain to form an optimal intention link, and generating an adaptive Token flow; the input analysis layer, the logical reasoning layer and the output mapping layer are positioned and recognized through semantic backtracking, semantic nodes are implanted, node balance adjustment is executed, and a cooperative processing chain is formed; feature reverse extraction, reasoning complementation processing and deep reasoning processing are executed through a cooperative processing chain, a three-layer control network is formed, a control Token sequence is generated, and efficient conversion from information to instructions is achieved.

A work ticket intelligent checking method based on a multi-modal large-model knowledge graph and large-model reasoning belongs to the technical field of power system application, and comprises the following steps: firstly, extracting a text feature vector and an image feature vector through an OCR module and an image encoder, and performing multi-modal fusion to form comprehensive semantic representation; entities and relations are extracted from the electric power safety regulations and historical operation data, and an electric power safety knowledge graph is generated. And carrying out format and integrity verification on the face value basic field, and carrying out semantic reasoning by utilizing LLM in combination with context and map nodes to generate a semantic judgment result. And integrating three-layer results to calculate a comprehensive check score, and outputting an intelligent judgment result. According to the invention, by introducing the multi-modal input analysis, knowledge graph construction and large model semantic reasoning technology, a structured, interpretable and iteratively optimized intelligent work ticket checking system is constructed, and the system realizes full-process intelligence from ticket image recognition to semantic understanding and from explicit rule inspection to implicit logical reasoning.