55 results about "Semantic framework" patented technology

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention belongs to the crossing field of artificial intelligence and material informatics, discloses a multi-layer material knowledge graph construction method and system based on a large model, and solves the problems of low utilization rate of multi-modal information, flat knowledge representation, uncontrollable quality and insufficient entity alignment precision in the prior art. The method comprises the following steps: analyzing material science and technology literatures, extracting multi-modal information of texts, tables and images, and preprocessing the multi-modal information into structured data; constructing a three-layer semantic framework of a concept term layer, a material entity layer and a material data layer; extracting knowledge by a large model layer by layer to generate a triple, and performing multi-dimensional quality evaluation and dynamic threshold filtering; entity fusion is realized by adopting hybrid alignment of semantic embedding and literal similarity and large model discrimination, and an atlas is maintained by combining incremental updating and version management. According to the method, knowledge integrity and accuracy are improved, entity heterogeneous scenes in the material field are adapted, whole-process automation is achieved, and high-quality knowledge support is provided for material research and development and data sharing.

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module for extracting minimum semantic fragments from multi-source data and performing cross-source alignment to generate a symbolized semantic framework; the data purification module converts multi-source data into symbolic predicates according to the framework, filters low-evidence data and outputs a purification set; the ternary generation module inputs the initial triad candidate set into a minimum rule grammar, and extracts entities, relationships and attributes to generate an initial triad candidate set; the disambiguation calibration module generates entity two-hop topological fingerprints based on the candidate set, completes disambiguation alignment and corrects conflicts, and outputs unambiguous structured knowledge; the mapping fusion module fuses the mapping information with the minimum connected ontology, and constructs and verifies an initial mapping knowledge domain; and the incremental updating module processes newly added data, performs incremental merging and maintains consistency, and forms a complete knowledge graph. According to the method, through full-link automatic construction, efficient fusion and high-quality atlas are realized.

The invention discloses a controllable text generation method based on framework semantic knowledge, and belongs to the technical field of natural language processing. Aiming at the problem of how to improve the semantic distinguishing ability of a model and the consistency of knowledge updating in context editing, firstly, a semantic framework network is introduced to carry out semantic analysis on input and associated contexts, and an adaptive threshold value is set through comparative analysis so as to enhance the semantic distinguishing ability and effectively isolate interference of irrelevant knowledge; and secondly, proposing a comparison perception distribution decoding mechanism, capturing offset generated in a knowledge updating process by comparing original and fused probability distribution, and designing a double filtering mechanism to ensure that the injected knowledge can be stably applied in different scenes. Experimental results show that the knowledge editing method is remarkably improved in the aspects of improving the accuracy, generalization and robustness of knowledge editing.

The invention relates to the technical field of data compliance. According to the AI-driven data compliance rule intelligent method, device and equipment and the medium, the method comprises the steps that natural language input of a user is obtained, and user demand description is obtained; performing multi-dimensional semantic analysis on the user demand description through a pre-trained large language model, and generating a structured semantic framework comprising an intention vector, an entity relation graph and a constraint condition set; performing compliance correction on the structured semantic framework to generate an optimized semantic framework; performing logic conflict detection and performance simulation test on the domain-specific language rule to generate an executable compliance rule; and deploying the executable compliance rule to the target data governance platform so as to achieve the technical effects of reducing the learning threshold and the manual error rate generated by the domain specific language rule, improving the semantic adaptation capability in a complex business scene and optimizing the operation stability of the rule on the data governance platform.

The invention relates to an evidence chain reasoning method and system based on rule conflict coordination and a storage medium, and relates to the technical field of trade management.The evidence chain reasoning method comprises the steps that a rule query text input by a user is analyzed and recognized, and related trade rule terms are obtained in combination with a rule knowledge base; verifying related trade rule terms one by one according to rule limiting conditions to obtain condition verification tags; according to the semantic framework, initial execution actions suitable for rule terms in all related trade rule terms are screened and extracted; identifying and solving conflicts among the initial execution actions by utilizing a conflict detection algorithm through a logic structure and a conflict solving strategy, and generating first screening execution actions; filtering the first screening execution action according to an exception condition, and performing simulation application on the rule query text to generate a simulation result; the simulation result is converted according to the elaboration template, a rule simulation application text is obtained, and an evidence chain is obtained; the accuracy of rule ambiguity resolution is improved, and the suitability of a conflict resolution strategy is improved.

The invention provides an urban water supply system anti-seismic analysis method based on a multi-fineness digital twinborn model, and belongs to the technical field of intelligent disaster prevention and digital twinborn of urban infrastructure systems. Constructing a topological network model and a hydraulic model of the city-level pipe network; constructing a multi-fineness parameterized numerical model library, and designing a dynamic scheduling engine based on event response; constructing a bidirectional data conversion interface and a unified semantic framework, and constructing a multi-fineness digital twin model; performing simulation by using the multi-fineness digital twin model to obtain a real-time simulation result; based on the real-time simulation result, the comprehensive state of the urban water supply system is visualized, an emergency decision is made, and anti-seismic analysis of the urban water supply system is completed; the problems of precision and efficiency imbalance, collaborative decision-making obstacle and serious lagging of disaster scene response existing in anti-seismic analysis of an existing urban water supply system are solved.

The invention discloses an MBSE model cross-version migration method based on an ontology, and the method comprises the steps: building a version self-adaptive ontology framework supporting MetaGraph2.0 and MetaGraph3.0 through employing an ontology modeling language OWL based on an MOF mechanism and a GOPPRR-E element model; establishing a semantic mapping rule between a MetaGraph2.0 tool model, a MetaGraph3.0 tool model and the ontology framework, and defining an inter-version increment migration rule; a non-intrusive integrated plug-in is developed, and automatic migration from a MetaGraph2.0 tool model to a MetaGraph3.0 tool is achieved through ontology conversion; according to the method, the standardized semantic framework based on the OWL is constructed, the model elements of the MetaGraph2.0 and the MetaGraph3.0 are uniformly mapped to the ontology structure to ensure accurate expression and logic consistency of model semantics, the accuracy and reliability of cross-version migration are remarkably improved in combination with an increment migration rule, and the problem of semantic fuzziness or data loss is avoided.

The invention discloses an intelligent switch fault diagnosis method based on multilayer data reasoning, which relates to the field of switches, collects and fuses multi-source data of a switch, converts entities, attributes and relationships in a multi-source fusion data set into an RDF triple based on a predefined network operation and maintenance ontology model, and performs fault diagnosis on the RDF triple. The method comprises the steps of constructing a switch fault diagnosis knowledge graph, dynamically generating an SPARQL query statement based on real-time monitoring data, performing graph traversal and logical reasoning on the switch fault diagnosis knowledge graph, and outputting a switch fault diagnosis conclusion. According to the method, comprehensive upgrading of switch fault diagnosis from passive response to active prevention is achieved, the network operation and maintenance efficiency and decision quality are remarkably improved, equipment monitoring, topological relations and service data are integrated through a unified semantic framework, a structured knowledge graph is constructed, and fault diagnosis has semantic interpretability.

The invention discloses a multi-modal content extraction method based on a knowledge graph and a large model, relates to the technical field of content digitization, and realizes efficient and accurate extraction of various modal contents such as texts, images, audios and the like by fusing the semantic understanding ability of the knowledge graph and the deep learning ability of the large model. The method comprises the following steps: firstly, constructing a semantic framework of multi-modal content by utilizing a knowledge graph, and providing unified representation and association for information of different modals; then, deep learning and feature extraction are carried out on multi-modal data through a large model, and key information and potential association in the data are captured; and finally, the extracted multi-modal content is effectively integrated and presented, and richer and more comprehensive information experience is provided for the user. The method not only improves the efficiency and accuracy of content extraction, but also expands the application scene of content digitization.

The invention provides a multi-mode interactive data real-time fusion planning execution system, and belongs to the technical field of computer data processing. According to the method, a dynamic scene calculation graph is constructed and updated in real time, split multi-modal sensing data is integrated into a structured semantic framework, conversion from passive data processing to active scene understanding is achieved, relevance and potential importance of all elements in a scene are calculated in advance before a user instruction is sent, and therefore the user experience is improved. According to the method, intention anchoring can be achieved when an instruction is issued, cognition and response delay of a system is greatly shortened, interaction instantaneity is improved, a traditional linear task planning normal form is subverted by providing a task generation and elastic execution mechanism based on graph reasoning, and a task sequence containing alternative nodes is generated, so that the task planning efficiency is improved. And the execution unit directly subscribes to the state change of the scene graph, so that rapid link switching of a bottom layer is realized without depending on complete re-planning of an upper layer.

The invention provides a multi-personality decision simulation and game method for military artificial intelligence. Simulation of an AI opponent model on complex human behaviors is enhanced through a DIKWP semantic framework. The system firstly configures personality parameters based on a psychological model (such as MBTI, big five personalities), and maps the personality parameters to an AI cognitive model to construct a behavior preference model; then, a noise injection algorithm is adopted to simulate emotion and cognitive deviation, and unique deviation of different personalities in decision making is reproduced; the multi-agent self-gaming engine enables AIs with different personality characteristics to repeatedly game in a red-blue confrontation environment, optimizes a strategy and evaluates winning rate distribution. And the decision fusion analysis module summarizes the data, generates personality performance comparison and behavior pedigree diagrams under each tactical situation, and predicts opponent behaviors. The DIKWP semantic integration interface ensures that a personality simulation result can be seamlessly connected with a knowledge graph and a strategic intention module of an existing AI system. According to the method, AI opponent modeling is enriched on the semantic level, the decision simulation degree of a real commander is improved, and as a supplement of an existing AI simulation system, the ability of military AI in the aspects of strategic pre-judgment and cognitive game is enhanced.

The invention relates to the technical field of tunnel water inrush accident safety evaluation, in particular to a knowledge extraction method and device for tunnel construction water inrush accident hidden dangers and a storage medium, and the method comprises the following steps: S1, collecting all data related to the tunnel construction water inrush accident hidden dangers, and analyzing the features of the water inrush accident hidden dangers, constructing a knowledge semantic framework, and extracting entities and entity relationship categories; s2, acquiring a corpus set to be processed, preprocessing data in the corpus set, constructing a data set, and grading the data in the data set according to sources; compared with the prior art, the method has the advantages that compared with a traditional knowledge extraction method, resistance sampling and type constraint optimization shielding processing are added, so that the problem that false negative samples can only be generated depending on high-quality input is avoided, the samples are further screened, the main beam of the input samples is improved, and the model training effect is guaranteed.

The invention relates to a trade rule reasoning method and system based on bidirectional mapping and a storage medium, and relates to the technical field of trade management.The trade rule reasoning method comprises the steps that a trade rule text is collected and split, and a plurality of rule terms are obtained; analyzing the rule terms according to preset key entity elements to obtain rule condition segments, and combining the rule feature weight values to obtain trade rule feature vectors; identifying and converting trade rule feature vectors according to the rule attributes and the dependency relationship between the rules, and constructing a rule dependency graph; converting the trade rule text into formalized rule elements according to the semantic framework and the logic structure in combination with the rule dependency graph; generating an optimal rule reasoning path according to the rule query request in combination with the formalized rule elements; aggregating all the optimal rule reasoning paths to form a trade rule corresponding to the rule query request; and establishing a mapping relationship between the semantic structure and the formalized representation of the rule text through a bidirectional mapping mechanism.

The embodiment of the invention relates to the technical field of natural language understanding, and discloses a semantic understanding method, device and equipment and a storage medium, the method comprises the following steps: when user input is received, transmitting the user input to a preset artificial intelligence model through a LangChain framework to perform semantic understanding and generate a semantic understanding result; if the semantic understanding result contains a function call instruction, selecting a target function call from a plurality of registered function calls through a LangChain framework according to the function call instruction so as to execute corresponding instruction operation; transmitting an execution result called by the target function to a preset artificial intelligence model through a LangChain framework so as to generate answer content input for the user; and the generated answer content is returned to the user through a LangChain framework. By applying the technical scheme provided by the invention, the problems of high development difficulty, obvious technical threshold and difficulty in non-professional personnel to participate in development due to the fact that a structured semantic framework is defined by a domain expert in the prior art can be solved.

The invention relates to the field of coal bed gas exploration, and particularly discloses a coal bed gas multi-source heterogeneous data fusion and management method based on a knowledge graph, which establishes a unified semantic framework by constructing a coal bed gas field ontology model and definitely defining entity types of geology, wells, engineering, data and the like as well as attributes and relationships thereof. Structured knowledge is extracted from multi-source heterogeneous data based on the framework, knowledge fusion is achieved through entity alignment and conflict resolution, and finally the processed knowledge is stored in a graph database to construct a coal bed gas knowledge graph. Based on the knowledge graph, intelligent query, visual correlation analysis, decision support and other services can be provided. According to the method, semantic-level deep fusion of coal bed gas multi-source heterogeneous data is realized through a knowledge graph technology, a data island is broken through, and a unified knowledge network is constructed; the analysis and mining capability of the complex incidence relation is enhanced through a graph structure expression mode, and multi-hop query and causal inference are supported.

This invention relates to the field of dynamic monitoring technology for foundation pits, specifically a dynamic monitoring method for deep foundation pit support construction methods that integrates digital twins. The method includes the following steps: obtaining construction stage numbers and binding them to support units; collecting excavation depth and component status data and writing them into a digital twin model; matching design elevations and verifying status consistency to determine stage completion; updating stage attributes and synchronously displaying the construction monitoring status. In this invention, support units and spatial ranges are associated with construction stage numbers as the main line. Component identification, excavation depth, installation status, and acceptance status are integrated to form a unified status expression. Stage completion is automatically determined through stage condition matching and consistency verification. The displayed content is updated synchronously with stage switching, enabling the support status, excavation status, and construction progress to be linked within the same semantic framework. This reduces human judgment bias, lowers the risk of stage misjudgment, and improves the consistency between monitoring results and on-site construction, as well as the efficiency of dynamic control.

The application discloses a traffic safety scene text generation method, comprising: obtaining an initial parameter set input by a user; generating prompt information based on the initial parameter set and a preset semantic constraint template; the semantic constraint template comprises a plurality of non-numeric mandatory items and their arrangement order; receiving scene description information generated by a generative artificial intelligence model in response to an inference request based on the input prompt information; and generating a traffic safety scene file based on the scene description information. The application generates highly structured input prompt information by receiving an initial parameter set input by a user and combining a preset semantic constraint template. Since the semantic constraint template clearly specifies a plurality of mandatory non-numeric items and their arrangement order, and rigidly stipulates that no numerical parameters are allowed in the prompt information, the generative artificial intelligence model can be strictly limited within the preset semantic framework when receiving instructions to perform inference and creation, thereby improving the semantic consistency of the generated text.

The invention relates to the technical field of intelligent text generation, and discloses an AI-based travel note automatic generation method and system, and the method comprises the steps: obtaining an original data set during a travel; clustering the original data to obtain a clustered data group; calculating a time sequence dependency relationship based on the clustering data group, and constructing a time sequence association graph; generating a time sequence paragraph sequence according to the association graph; logic fluency among semantic elements is calculated and sorted, and a semantic arrangement sequence is formed; performing connection optimization and framework construction on the semantic sequence to obtain a coherent semantic framework; a draft structure is arranged in combination with the clustering data set, and a complete note draft is generated; and finally, adjusting emotion elements according to the draft to generate travel note content. According to the method, the semantic coherence and the emotion presentation effect of the note content can be enhanced.

This invention discloses a method for monitoring and managing the operational environment at power infrastructure construction sites, belonging to the fields of intelligent decision-making technology and power safety management. It includes: constructing a semantic network framework based on the construction plan; collecting and calibrating multi-source environmental data to generate a reliable dataset; generating a real-time risk network diagram based on the semantic framework and the reliable dataset; calculating robust risk indicators and performing sensitivity deconstruction; generating closed-loop intervention instructions when risk indicators exceed safety thresholds; and finally, iteratively optimizing the entire system by collecting feedback and generating a reusable intelligent template library across projects. This invention employs a comprehensive technical approach combining semantic modeling, causal inference, and closed-loop adaptive optimization, enabling deep analysis of operational scenarios, proactive quantification and attribution of potential risks, and intelligent generation of optimal intervention strategies. This significantly improves the intelligence, precision, and forward-looking nature of on-site safety management in power infrastructure construction.

The invention discloses an insurance customer demand matching and business recommendation method and device based on a knowledge graph, and the method comprises the steps: carrying out the targeted preprocessing of structured data and unstructured data in an insurance business scene, and extracting standardized entity, relation and attribute data; s2, converting the standardized data extracted in the step S1 into an RDF / RDFS format of a unified semantic framework to obtain a multi-source independent initial knowledge graph; through an entity alignment method combining semantic standardization and semantic matching, redundancy and noise of a multi-source initial knowledge graph are eliminated, and a global customer knowledge network is formed through fusion; based on the fused global customer knowledge network, customer portraits are perfected through rule reasoning, and potential association relationships among entities are identified; and realizing accurate matching and personalized recommendation of customer demands and insurance businesses through risk level evaluation, hidden demand mining and claim settlement risk check in combination with the improved customer portrait and the inference result.

The invention belongs to the field of tourism recommendation, and particularly relates to a tourism destination recommendation method and system based on sensory impressions. The method comprises the following steps: acquiring a basic data set of tourist destinations and a user online evaluation set of each tourist destination, and performing text word segmentation and stop word removal on text data to obtain a text sequence in a standardized form; high-frequency sensory vocabularies are divided into five dimensions of vision, hearing, smell, taste and touch, and a semantic framework of a structured mode director is formed; inputting the standardized text sequence into an information extraction model based on a UIE framework, automatically extracting sensory description entities according to an SSI mode, and generating a tourist destination sensory impression knowledge table; and aiming at user demands, generating a Top-N recommendation list according to the sensory coding proportion, and completing tourist destination recommendation. According to the method, the problem of insufficient tourist demand matching precision caused by lack of structured sensory knowledge representation of a traditional recommendation system is solved.

The invention discloses an oral cavity multi-modal data fusion method based on a knowledge graph, and belongs to the technical field of oral medicine artificial intelligence and medical big data fusion. The method comprises the following steps: firstly, constructing a stomatology knowledge graph with a'top-layer multiplexing-specialized extension 'double-layer architecture, and forming a standardized semantic framework; then, multi-modal data such as structured medical records, unstructured texts and CBCT images of the patient are collected and subjected to quality control; then, analyzing and mapping each modal data into a standardized semantic representation associated with the knowledge graph by applying a natural language processing and computer vision technology; and finally, under a unified semantic framework, deep integration of multi-source information is realized through cross-modal semantic alignment, confidence weighted fusion and conflict resolution, and a patient dynamic comprehensive data view containing a three-dimensional panorama, a time axis and a semantic network is generated. According to the method, deep semantic understanding and collaborative utilization of oral multi-source heterogeneous data are realized, and a comprehensive and credible data basis is provided for precise diagnosis and treatment.

The invention provides a three-level environmental risk assessment linkage method and system for an industrial park, relates to the technical field of environmental risk management, and aims to realize a unified semantic framework of enterprise-level, special-level and park-level data by constructing an environmental risk assessment ontology. According to the method, an event-driven dynamic updating mechanism is adopted, data recalculation and risk map adjustment are automatically triggered, and the timeliness and accuracy of environmental risk response are improved. The whole system effectively solves the problems of data islands, updating lagging, verification missing and the like, and provides real-time and accurate decision support for environment management of industrial parks.

The invention discloses a natural semantic recognition method based on a large model. The method comprises the following steps: constructing a context cue P1 according to a to-be-recognized natural language sentence and a predicate and a first template; inputting the context cue P1 into the large language model to obtain a semantic item; extracting a core semantic role of the semantic item and a description thereof from the semantic framework library; combining the core semantic role and the description thereof with all fixed non-core semantic roles and role descriptions corresponding to the semantic framework library to obtain all possible semantic roles and descriptions thereof; constructing a context prompt P2 according to all possible semantic roles and descriptions thereof and a second template; inputting the context cue P2 into a large language model to obtain a structured annotation result; and according to the natural language sentence to be recognized and the predicate, post-processing and correcting the structured labeling result to obtain a corrected recognition result. According to the natural semantic recognition method, large-scale manual data annotation is not needed, the development and deployment cost is remarkably reduced, and the development efficiency is improved.

The invention discloses an evaluation double-layer directory construction method and system based on an electric power AI application scene. The method comprises the steps of obtaining a business specification document; the business specification document is analyzed to obtain core elements, and the core elements comprise business scenes, observation objects and attributes of the observation objects; constructing a multi-level nested structure based on the core elements to obtain a preliminary double-layer directory; reviewing and adjusting the initial double-layer catalogue to obtain an adjusted double-layer catalogue; verifying and perfecting the adjusted double-layer catalogue by using historical business data to obtain a corrected double-layer catalogue; and finally auditing the corrected double-layer catalogue to obtain an evaluated double-layer catalogue. By implementing the method provided by the invention, three major problems of fuzzy structure hierarchical relationship, lack of business and technical team collaborative semantic framework and high construction cost of the traditional knowledge graph in vertical business scene application can be solved, so that efficient and low-cost knowledge graph construction which is closely combined with business logic is realized.

The present disclosure provides a system for semantic analysis and automatic solution of a mathematical application problem. The system includes: a semantic framework mode matching module, configured to: perform mode matching on a clause vocabulary sequence with a semantic framework, to form local semantic information of the mathematical application problem; a scenario semantic analysis module, configured to: form, based on a local feature vocabulary string of a scenario, a global semantic feature vocabulary string of the scenario; a data meta-variable naming module, configured to: generate a global semantic name of a data meta-variable based on a local name of a variable; a module for explicitly expressing a computation relationship, configured to: explicitly express an explicit computation rule and an implicit computation rule between data meta-variables, to construct a dynamic semantic circle; and a module for implementing a machine thinking mechanism, configured to solve the questionable data meta-variable.

The invention relates to the field of large models, in particular to an information extraction and dynamic updating method, system and device and a medium. The method comprises the following steps: performing cross-modal alignment on multi-modal data in an enterprise webpage to obtain standard data; inputting the standard data into a preset fine tuning and enlarging model, and extracting key information; encoding the key information by using double encoders to obtain a semantic feature vector, and carrying out context comparison on the semantic feature vector through a sliding window to judge an update boundary; and judging updating information according to the updating boundary and a preset time attenuation weight, and mapping the updating information into a knowledge graph with a timestamp. Standard data are obtained through cross-modal alignment, data of different modals can be unified to the same semantic framework, and semantic gaps and format differences among the data are eliminated. Standard data are input into a preset fine tuning model to extract key information, irrelevant noise information can be filtered out, and the method focuses on content valuable to enterprises.

The embodiment of the invention provides a data processing method and device, and relates to the field of intelligent driving, and the method comprises the steps: obtaining the first track data of a vehicle based on the driving data of the vehicle; performing first interpolation on the first trajectory data, determining a first curve, and obtaining second trajectory data based on the first curve; constructing a Frenet coordinate system based on the second trajectory data; and semantic data of the target object at each moment are constructed based on a dynamic area formed by the front boundary and the rear boundary of any target object at each moment and the first position data of the target object under the Frenet coordinate system, and the target object comprises a vehicle and an obstacle. According to the embodiment of the invention, the semantic framework based on the Frenet coordinate system is constructed, the driving data is converted into the semantic data, and the motion intention and the interaction relationship of the target object are described through the semantic data, so that the method is closer to user cognition, and the reuse and generalization of the driving data in different high-level application requirements are facilitated.

The application relates to the technical field of intelligent text generation, and discloses a travel note automatic generation method and system based on AI, which comprises the following steps: obtaining an original data set during a travel; clustering the original data to obtain a clustered data group; calculating a time sequence dependency relationship based on the clustered data group, and constructing a time sequence correlation graph; generating a time sequence paragraph sequence according to the correlation graph; calculating the logical fluency among semantic elements and sorting to form a semantic arrangement sequence; performing connection optimization and framework construction on the semantic sequence to obtain a coherent semantic framework; combining the clustered data group to arrange a draft structure and generate a complete note draft; and finally adjusting emotional elements according to the draft to generate travel note content. The application can enhance the semantic coherence and emotional presentation effect of note content.