546 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 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 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 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 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 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 case factor intelligent analysis and processing method based on intelligent semantic understanding, and belongs to the field of artificial intelligence, natural language processing and public security judicial informationization, and the method comprises the following steps: 1, preprocessing public security judicial document information, and supporting text analysis, format standardization and structured data conversion; 2, carrying out refined extraction on case elements based on a large language model, and identifying case constituent elements, hazard degrees and processing results; 3, fusing logical reasoning of a public security judicial knowledge graph, and enhancing cross-cause semantic understanding and reasoning accuracy; and 4, performing intelligent auxiliary processing, generating class case recommendation and penalty interval prediction, and performing interactive regulation question and answer. According to the method, efficient, accurate and automatic case factor analysis is successfully realized, and the public security judicial document processing efficiency and decision accuracy are remarkably improved.

The invention provides a dialogue processing method and system based on a large model, and the method comprises the steps: obtaining a dialogue text length value, employing a pre-established semantic boundary identification set to detect a topic turning frequency value according to the dialogue text length value, and combining a historical statement sequence sorted according to time in a context window, generating an initial semantic segmentation vector containing feature extraction dimensions; according to the initial semantic segmentation vector, calculating a topic coherence score by adopting a dependency weight distribution table, and separating sub-vectors of which the orthogonality degree is higher than a preset threshold value through a vector decomposition precision value to generate a preliminary structured representation matrix; extracting emotional intensity fluctuation features and knowledge density distribution features from the preliminary structured representation matrix, and generating a refined structured representation matrix after adjusting the rank number of the matrix; and calculating the Euclidean distance between the topic coherence sub-vector and the logical reasoning sub-vector in the refined structured representation matrix.

The invention relates to the technical field of natural language processing, and discloses a cross-domain vocabulary logical reasoning and extension method. According to the method, a multi-source cross-domain text data stream is received, cross-language alignment and semantic fusion are performed through a pre-trained semantic decoupling model, an adaptive semantic extension threshold model and a causal reasoning framework are constructed, logic interruption nodes are identified, a compensation strategy is configured, and collaborative parameters are iteratively optimized by using a distributed semantic collaborative learning framework. Multi-source data can be effectively integrated, semantic barriers can be broken, the vocabulary expansion range and mode can be accurately controlled, logic transmission coherence and accuracy are ensured, efficient collaboration of multi-language agents is achieved, algorithm efficiency and semantic accuracy are improved, the problem of semantic instability is monitored in real time, the problem of semantic instability is solved, and powerful support is provided for cross-domain natural language processing.

The invention discloses a hybrid intelligent system architecture combining rule driving and probabilistic reasoning and an operation method thereof. The system comprises a rule processing and debugging module, a probabilistic reasoning and candidate generation module, a hierarchical decision and fusion module and a semantic data packet management and circulation module. The system adopts a processing mechanism of'rule first and probability second ', logical reasoning and error elimination are carried out through a rule module, and then candidate results are generated and sorted in a limited space through a probability module. The semantic data packet is used for bearing information of each module. Model behaviors are constrained by using a digital natural normal form, so that the system improves the long text understanding ability, reduces the language model illusion occurrence rate, and optimizes the resource use efficiency.

The invention belongs to the field of intelligent man-machine interaction, and relates to an intelligent writing and correcting method and system.The method comprises the steps that a flexible pressure sensor matrix and a coordinate positioning system are used for sensing multi-dimensional data generated when an input pen writes, and a four-dimensional writing feature space is constructed; a three-level correction system is adopted, and logical reasoning verification is conducted on the answers through a deep semantic understanding model; the three-stage correction system sequentially comprises OCR font matching, semantic correction based on a large language model and intelligent correction based on a process reward model; a cognitive diagnosis model is introduced, and the answer hesitation degree is judged by analyzing answer indexes; fusing question answering information, utilizing a large model to identify knowledge points to which questions belong, and inferring knowledge mastering weak points according to question answering conditions; writing information is accurately collected through an intelligent writing capture technology based on multi-modal sensing fusion, an intelligent correction integrated method is built in combination with a cognitive diagnosis model, and efficient recognition and accurate correction of written content are achieved.

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.

The invention discloses an AI-based online homework correction method and system, and the method comprises the steps: carrying out the feature extraction of text content, logic structure and language expression through a multi-dimensional feature extraction model according to the homework data submitted by a student; according to the multi-dimensional feature vectors, a subject knowledge base and logical reasoning rules are combined, and error detection and correction are conducted through a logical reasoning network; according to the homework feature vector, in combination with a teacher scoring standard and a student ability evaluation index, performing scoring and ability analysis by using a dynamic scoring and ability evaluation model; and generating a personalized feedback report by using a generative adversarial network according to a scoring result and a capability evaluation result. According to the embodiment of the invention, various errors in the homework can be efficiently and accurately identified and corrected, personalized feedback is provided, and the quality and effect of online education are 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 discloses an automatic labeling method and system based on a neural symbol combination framework, and belongs to the field of information processing. The method comprises the following steps: S1, inputting multi-modal data, and preprocessing the multi-modal data; s2, respectively extracting features of each mode, and generating a joint embedded vector by adopting a multi-head attention mechanism; s3, logical reasoning is executed based on the domain knowledge base, and interpretable labeling rules are generated; s4, mapping the joint embedded vector to a predicate space of symbol logic for rule matching; s5, detecting whether a matching conflict exists or not based on a logic constraint solving algorithm; if the conflicts exist, manual auditing is triggered, the rule weight is updated based on the Bayesian network, and the steps S3-S5 are executed again; and if no conflict exists, outputting a labeling result. According to the method, collaborative optimization of data driving and knowledge driving is realized by fusing the perception ability of the neural network and the logical reasoning ability of the symbol system.

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.