193 results about "Semantic network" patented technology

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention provides a control method and system of a double-arm clothes folding robot, and relates to the technical field of robot control, and the control method constructs a perceived control closed loop by fusing visual, force and tactile multi-modal data. The touch sense and the force sense make up the blind area of the vision in the self-shielding scene, and grabbing, sliding and pleating perception is improved. Based on clothing joint characterization of a visual semantic network, geometric morphology, semantic components and confidence coefficient are structured and coded, a robust state fusing prior and real-time observation is provided for planning, and decision errors caused by deformation and noise are reduced. Long-time-history folding is decomposed into sub-target sequences through a hierarchical strategy, action closed-loop correction is conducted through multi-modal data, and cooperative control over grabbing, folding and pleating is achieved. And finally, a torque instruction is generated based on a control algorithm, interaction compliance and safety are ensured, and the folding success rate and operation stability of the double-arm clothes folding robot under different materials, sizes and initial forms are integrally improved.

The invention provides a complex scene-oriented end-to-end semantic extraction system, belongs to the technical field of artificial intelligence and natural language processing, and realizes cross-modal information association through a multi-source heterogeneous data fusion module to construct a dynamic semantic network model. A hierarchical attention mechanism is adopted to carry out context-aware coding on unstructured input, and unsupervised pre-training and a weak supervised fine tuning strategy are combined to optimize a feature representation space. And designing an adaptive inference engine, automatically switching semantic analysis paths based on scene complexity, and generating a structured output result. According to the method, the dependency on specific knowledge in the field is reduced, the semantic understanding generalization ability in a complex scene is remarkably improved, high-precision analysis performance can still be kept in a low-resource environment, meanwhile, calculation resource consumption is reduced, and the method is suitable for practical application scenes with multi-language mixing, serious noise interference and high real-time performance requirements.

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

Knowledge-based audio-text modeling via automatic multimodal graph construction is performed. An audio dataset is received, the audio dataset including clips of audio data, wherein each of the clips of the audio data is paired with corresponding metadata descriptive of the audio contents of the respective clip of the audio data. Graph nodes of interest are identified from a sematic network, the graph nodes being descriptive of semantics of the knowledge domain of the contents of the audio dataset. A large language model (LLM) is utilized for categorizing the metadata into the graph nodes and for inferring supplemental data for the graph nodes for which there is no metadata, producing an extracted knowledge graph. The extracted knowledge graph is validated utilizing the LLM to perform relation verification of edges between the graph nodes of the extracted knowledge graph, thereby mitigating hallucination effects in the categorizing and inferring of the supplemental data.

The invention discloses an automatic compliance examination method and system based on graph retrieval enhancement. The method comprises the following steps of: obtaining a billing specification and a historical inquiry report as input documents; the input document is subjected to dual-mode document segmentation based on a large language model, logic blocks and physical blocks are generated, and each logic block is a continuous page unit and is attached with a content abstract generated by the model; constructing a precedent graph, extracting key legal elements in the historical inquiry report through multi-stage recursion, generating semantic network nodes carrying traceability identifiers, and establishing cross-document semantic association; and constructing a state graph, automatically identifying chapters and logic and semantic relationships among the chapters based on a document hierarchical structure, and generating a machine-readable structured index and the like. According to the method, the long text semantic understanding depth, the cross-section consistency and the legal reasoning accuracy are remarkably improved, the manual review cost is reduced, and the method is suitable for a listing compliance review scene under a registration system.

The invention belongs to the technical field of computer aided process design (CAPP), and particularly relates to a body-based shaft part fixture scheme automatic generation method. The method specifically comprises the following steps: (1) constructing a shaft part clamp scheme generation body; (2) establishing a shaft part clamp scheme to generate an SWRL inference rule; (3) extracting relevant feature constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, and carrying out inference selection and adjustment on a shaft part fixture scheme. Constructing an ontology reasoning knowledge framework by utilizing ontology pair shaft part clamp scheme domain knowledge, and reasoning implicit knowledge according to dominant domain knowledge; and reasoning an optimal clamp scheme according to the feature constraint condition of the geometric product in combination with a semantic network rule language rule base. According to the method, a consistent knowledge description framework generated by the shaft part clamp scheme can be provided, so that a computer can automatically select a proper clamp scheme and the like according to various constraints of geometric product parts, and a quick and effective method is provided for intelligent selection of the shaft part clamp scheme.

The invention relates to the technical field of data management, in particular to a data weaving semantic integration method based on a semantic network and a knowledge graph, which comprises the following steps: acquiring multi-source heterogeneous data and preprocessing to obtain standardized data, the multi-source heterogeneous data comprises structured data, semi-structured data and unstructured data from different business scenes; performing rule injection on the standardized data to generate semantic web ontology data; extracting entities and attribute values thereof in the standardized data to generate structured knowledge graph data of the instance layer; and establishing a semantic association mapping network of the semantic network ontology data and the structured knowledge graph data, and generating a semantic integration result of data weaving. According to the method, the basic differences of the multi-source data in the aspects of formats, codes and the like are eliminated, a unified semantic standard is provided, and conflicts caused by non-unified semantics are reduced.

The invention discloses a natural language processing technology-based cultural and creative element data analysis method, which comprises the following steps of: based on AI intelligent plush doll interaction data, generating a cultural and creative structure embedding degree index through named entity recognition and semantic network topology analysis; an impulse response function is constructed based on the cultural creative structure embedding degree, and a cultural creative memory cycle index is calculated in combination with the path length and information backflow; fusing the multi-modal features and the ideal innovation region, and outputting a perception novelty score; screening high-potential-energy elements to construct a semantic sub-graph, simulating a thermodynamic process, and identifying a text-creative fusion hotspot chain; and decoding the hotspot chain into a design semantic tag, matching a material database to generate an appearance scheme, and converting the appearance scheme into an interaction template, thereby realizing a customized design closed loop from a user dialogue to a doll form and behavior. Through data driving and cross-modal analysis, implicit culture preferences in user dialogues are dominated, and creativity of the appearance of the AI intelligent plush doll and humanization of interaction are improved.

The invention discloses an SLAM dynamic point semantic filtering method based on DDMA-SAM, and belongs to the technical field of synchronous positioning and mapping. A decoupling distillation mechanism is introduced, an image encoder in an original SAM model is subjected to lightweight optimization, a DDMA-SAM semantic network integrating a multi-scale aggregation detection module and an efficient mask decoding module is constructed, and the SLAM dynamic point semantic filtering method based on DDMA-SAM is obtained. And the segmentation performance is improved while the model parameters are greatly compressed. Based on the semantic network, providing a semantic prior and geometric consistency combined-driven double filtering strategy; based on a semantic mask and a confidence threshold, carrying out preliminary dynamic point identification; in combination with the epipolar geometric constraint and the triangulation reprojection error of random sampling consistency estimation, fine elimination of dynamic feature points is realized, and only static points are reserved to participate in camera pose estimation. According to the method, the real-time performance of the system is kept, and meanwhile, the mapping quality and the track stability in a dynamic scene are effectively improved.

The invention provides a multi-application scene-oriented window layout cross-screen collaborative nonlinear restoration system, which belongs to the technical field of computers and comprises a system data acquisition module, an application scene generation module, an intelligent layout strategy module and a scene layout restoration module. The system data acquisition module is responsible for acquiring application-related data; the application scene generation module is in communication connection with the system data acquisition module and the intelligent layout strategy module to generate a high-relevance application combination scene; the intelligent layout strategy module is in communication connection with the system data acquisition module, the application scene generation module and the scene layout restoration module to generate multiple sets of window layout snapshots; and the scene layout restoration module is in communication connection with the system data acquisition module and the intelligent layout strategy module and is responsible for scene layout restoration. According to the method, a software combination semantic network is established, and multi-screen application automatic layout based on application data flow and user interaction is realized.

The invention discloses a social engineering self-adaptive dynamic protection method based on post risk assessment, relates to the technical field of network security, and solves the problem that in the prior art, the aspects of employee security awareness modeling, multi-modal data fusion and personalized security policy recommendation still have obvious deficiencies. The method comprises the following steps: firstly, constructing an electric power post knowledge graph, and carrying out structured modeling on four entities of posts, permissions, assets and vulnerabilities and association relationships thereof to form a global semantic network; secondly, a post risk baseline model is established, a normal behavior mode is defined by quantifying inherent risks of posts and analyzing historical operation logs, and a static and dynamic combined risk reference is formed; and finally, designing a post exclusive feature extractor, fusing personal behavior data of the employees with semantic constraints and risk baselines of the knowledge graph, and generating highly personalized feature vectors, thereby laying a foundation for subsequent real-time monitoring of abnormal behaviors of the employees, quantification of safety consciousness and providing of differentiated protection.

The invention discloses a multi-source data management evaluation method based on soil and agricultural products, and relates to the technical field of agricultural data management, and the method comprises the steps: collecting preprocessing data, constructing semantic network association, calibrating a sensor, extracting features, constructing a causal network, and carrying out the comprehensive evaluation and updating of a model, and achieves the management evaluation. The method has the advantages that the self-adaptive semantic network containing the soil, agricultural product and environment domain term ontology library is constructed, the semantic mapping relation is updated in real time based on the ontology evolution algorithm, the dynamic semantic matching degree is calculated in combination with the context sensing analysis engine so as to achieve multi-source data dynamic association alignment, edge end calibration nodes are deployed, and the data dynamic association alignment accuracy is improved. Real-time dynamic calibration of sensor data is completed through an environmental factor compensation model and a drift correction formula, the problem of inaccurate evaluation caused by insufficient dynamic association of multi-source data is systematically solved, and finally accurate management and scientific evaluation of soil and agricultural product multi-source data are realized.

A tailored interactive language learning system that teaches an individualized set of vocabulary words to users through interactive avatars and stories. The interaction is modeled through probabilistic rules in a semantic network and neural network having objects and relations. Dialog and narration is generated dynamically based on the state of the interactive story model using phrasal rewrite rules and neural network implementiung a four-valued logic system in which truth values of the objects and relations are encoded as true, false, defined, and undefined in a single memory array.

The invention relates to the technical field of legal consultation services, in particular to an intelligent question and answer matching method based on machine learning, which comprises the following steps of: acquiring legal consultation text recognition subject behavior objects and articles, analyzing and carding statement logic in sections, generating a semantic hierarchical table, extracting key phrases and integrating semantic units, and constructing a semantic network to generate a mapping table. Comparing question answer semantic logic to generate a matching corresponding set, reviewing offset revised text to generate a revised set, and rechecking consistency arrangement to generate an intelligent matching result set. According to the method, legal text logic relations are analyzed and processed through semantic layering, question and answer matching precision is optimized, keyword extraction and semantic network construction are combined, semantic consistency recognition and matching flexibility is enhanced, semantic offset and logic inconsistency are corrected, result connection coherence is ensured, mismatching is reduced compared with the prior art, question and answer accuracy is improved, and the method is suitable for popularization and application. Legal consultation is more intelligent and accurate, and a reliable matching result is provided.

The invention provides an intention-driven semantic evolution mechanism based on a tensor semantic field and an application system thereof, and relates to the technical field of artificial intelligence, semantic networks and cognitive computing. According to the method, five types of semantic primitives including data, information, knowledge, intelligence and intention are expressed as high-order tensor nodes, a semantic tensor field network is constructed, and dynamic evolution and intention driving of semantics are achieved. The system comprises a multi-scale semantic aggregation mechanism, an intention weight diffusion algorithm, a semantic tensor evolution operator and a white box interpretation interface, supports full-link semantic processing from original data to high-level wisdom to intention constraint, and overcomes the defects in semantic representation and evolution, intention fusion and system interpretability in the prior art. And the generative AI system has stronger intention perception, semantic self-optimization and process transparency capabilities, and is suitable for applications such as a semantic perception large model platform, an AI cognitive map system and an interpretable language generator.

The invention relates to the technical field of knowledge graph construction, and provides a knowledge graph construction method and system based on a traditional Chinese medicine classical prescription, and the method comprises the steps: obtaining multi-source heterogeneous data composed of a digital text of a classical ancient book, a teaching material, a traditional Chinese medicine authority dictionary, an academic journal literature and structured clinical data; the standard text data is subjected to mixed strategy knowledge extraction, discrete knowledge elements such as entities, relations and attributes are separated out, and the discrete knowledge elements form an original fact unit of a knowledge graph; the extracted discrete knowledge elements are subjected to ontology modeling and formalized representation, and a multi-dimensional semantic network with the prescription-syndrome-disease-machine as the core is constructed; and performing semantic similarity comprehensive judgment by calculating the character string similarity of the entity names and combining context features of the entity names. The system comprises a heterogeneous data processing module, an entity recognition module, a semantic network construction module and a similarity judgment module. Structured organization, deep association and intelligent utilization of classical prescription knowledge are realized.

The invention belongs to the technical field of user portrait analysis, and mainly relates to a cross-border e-commerce user portrait analysis method based on big data, and the method comprises the steps: extracting a user demand tag through multi-source data collection and standardization processing in combination with a semantic understanding model, and constructing a compliance knowledge graph of a target market; in the construction of user portraits, a framework based on cultural cognition is adopted, and preferences of users on types and components of health care products under different cultural backgrounds are deeply mined; a semantic network between a culture background and health care product attributes is constructed through a knowledge graph technology, and the recognition ability of the model to culture sensitive features is further improved; by dynamically updating the user portraits and combining marketing feedback data, commodity recommendation and marketing strategies are continuously optimized, so that the accurate marketing effect of cross-border e-commerce is improved on the premise of guaranteeing compliance; the user experience and the conversion rate of the cross-border e-commerce platform can be effectively improved, and the risk of violation is reduced.

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

The invention relates to the technical field of scenic spot intelligent interaction, and discloses a scenic spot intelligent question answering and recommendation method based on knowledge graph and large model fusion. The method comprises the following steps: receiving multi-modal original data to generate an entity relationship pair set, and structurally recombining the entity relationship pair set into a multi-layer abstract scenic spot semantic network; constructing an initial large language model containing a memory network and a reasoning module, and injecting full semantic association of a semantic network into the memory network; and collecting the query sample fine tuning model marked with the multiple intention labels, and generating a special interaction model. And in response to user query, the model executes intention classification and knowledge retrieval in parallel, calls memory network structured knowledge and reasoning module generation capability, and fuses output answers and recommendations. According to the method, the semantic understanding depth and interaction fusion of the scenic spot are improved, and the intelligent service experience is optimized.

The application discloses a mathematical expression diagnosis method and device based on an abstract syntax tree and a semantic network, and belongs to the technical field of mathematical expression processing. The method comprises the following steps: establishing formal grammar rules of a mathematical expression based on an extended Backus-Naur form; performing lexical analysis on an input mathematical expression to generate a token stream; performing syntax analysis on the token stream based on the formal grammar rules to construct an abstract syntax tree; in the process of constructing the abstract syntax tree, running a structural diagnosis process, wherein the structural diagnosis process comprises constructing a semantic network based on a mathematical knowledge base, and performing graph matching on nodes in the abstract syntax tree and the semantic network to identify structural errors in the mathematical expression; and generating error correction suggestions based on the identified structural errors. The application can realize deep analysis from syntax correctness checking to mathematical rationality, and improve the accuracy of mathematical expression processing.

The invention relates to the technical field of knowledge management, in particular to a knowledge graph-based creative IP derivation design method and system, in a graph attention network stage, by performing weighted integration on hierarchical difference and connection depth of semantic paths, nodes with high weights are automatically highlighted, an interpretable semantic trunk structure is formed, and the semantic trunk structure and the semantic path are integrated; in a complex semantic space, a semantic main line is enhanced, semantic drift caused by path mixing is reduced, conflict nodes are replaced and reserved based on a matching calculation result of semantic tags and relation attributes in a semantic fusion comparison stage, so that semantic hierarchies maintain logic closure and consistency during multi-source information fusion, and therefore, multi-source information fusion is realized. The greedy algorithm is dynamically adjusted according to the node access degree and the direction income in the knowledge reorganization process, low-income connection is eliminated, and direction error connection is redirected, so that the connectivity and the information transmission efficiency of the semantic network are optimized, and the reliability of the semantic network is improved. And it is ensured that the design input structure has the characteristics of clear hierarchy, balanced weight distribution and consistent cultural semantics.

The application discloses an intelligent matching method and system for an intelligent supply chain platform, and the method comprises the following steps: receiving user demand data and performing multi-modal semantic analysis to generate a semantic network comprising a technical field label, a technical problem vector and a technical means correlation graph; generating a first matching list and a second matching list through collaborative operation of a knowledge graph matching model and a natural language processing model; performing adaptive cross-validation and generating a candidate set; uniformly sorting the candidate set according to a sorting rule and outputting a matching result. Through the semantic network of the technical field label, the problem vector and the technical correlation graph, the limitation of traditional keyword matching is broken through, and multi-dimensional accurate mapping of demand and supply is realized; the double-model cooperation of the knowledge graph model and the natural language processing model solves the problem of semantic deviation of a traditional single model; and the adaptive cross-validation mechanism dynamically switches a fusion strategy through a deviation rate, thereby effectively improving the robustness in a complex scene.

The invention relates to the technical field of artificial intelligence, and discloses a conference summary automatic generation method and device, equipment and a medium, and the method comprises the steps: obtaining a conference image, a conference text and conference voice of a target conference, carrying out the text conversion of the conference voice, obtaining a converted text, and storing the converted text; performing cross-modal semantic alignment on the conference image, the conference text and the converted text sequence to obtain a semantic network, extracting structured information of the semantic network, and constructing a conference discussion process map based on the converted text and a timestamp of the structured information, generating a discussion content summary according to the conference discussion process graph, constructing a conference knowledge graph according to the semantic network, generating a conference hierarchical abstract according to the conference knowledge graph, and summarizing the discussion content summary and the conference hierarchical abstract to obtain the conference content summary, thereby improving the accuracy of the conference summary.

The application discloses a multi-task semantic communication system resource allocation method based on deep reinforcement learning, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a multi-task semantic communication network model assisted by semantic relays; based on the multi-task semantic communication network model, a multi-task resource allocation optimization model is established with the target of maximizing multi-task user experience quality; wherein the multi-task resource allocation optimization model is used for adjusting power allocation, sub-channel allocation and transmission semantic symbol number allocation of each task to maximize multi-task user experience quality; a hybrid deep reinforcement learning model is constructed and trained to obtain a strategy network capable of realizing optimal resource allocation, and wireless resource allocation optimization is performed on the basis of the multi-task resource allocation optimization model. The application can meet user equipment restrictions while reducing semantic network deployment overhead and realizing efficient utilization of spectrum bandwidth resources.

This invention discloses a method for aligning fields based on structured parsing of overseas open-source information, relating to the field of data fusion and parsing. The method includes: acquiring raw data packets from overseas open-source platforms; performing structured parsing and field extraction on the raw data packets to generate a candidate field set; performing semantic recognition and field mapping on the fields in the candidate field set based on a dynamic cognitive network to generate a field mapping decision result; resolving and correcting the consistency of conflicting field values ​​mapped to the same standard field based on the field mapping decision result to obtain an aligned field set; and organizing the aligned field set into a unified structured record and outputting it according to the standard field model. This method achieves intelligent mapping and conflict resolution of cross-platform fields through an evolvable semantic network, possessing the ability to adaptively learn unknown fields, achieve deep semantic understanding, and output interpretable decisions, significantly improving the intelligence level and accuracy of multi-source information fusion.

The application discloses a hydrological remote sensing image target recognition method based on a deep semantic model, relates to the technical field of remote sensing image processing, and comprises the following steps: obtaining a primary semantic code of a hydrological remote sensing image through a deep semantic network, calculating a semantic attribution probability distribution according to the primary semantic code, generating an initial attention guide signal, dynamically adjusting the weight of a specific perception path in the network, realizing first feature re-extraction to obtain refined features, calculating a feature compensation vector based on the confidence deviation generated by matching the features with a standard feature library, correcting the refined features to obtain enhanced features, and inputting the enhanced features into the network again for analysis to obtain a final recognition result. Through the attention adjustment of the semantic guide and the feature compensation based on the confidence feedback, the method effectively improves the recognition accuracy of complex hydrological ground object targets and the model robustness.

The invention discloses a distribution transformer small-size defect identification method and device based on a large electric power model and a storage medium, and belongs to the field of distribution transformer defect identification. The method comprises the steps that electric field data, voltage data and current data of a distribution transformer are collected, and the electric field distortion degree is calculated based on the electric field data; calculating a dielectric loss angle increment based on the voltage data and the current data; constructing a power operation semantic network through the distribution transformer image, and performing electric field distortion detection in combination with the electric field distortion degree to obtain an electric field distortion detection result; performing fault type identification based on the dielectric loss angle increment and dielectric loss angle parameters in the power operation semantic network to obtain an abnormal fault type; and matching in a preset small-size defect identification library based on the electric field distortion detection result and the abnormal fault type to obtain the small-size defect type of the distribution transformer. Therefore, by implementing the method, accurate identification of the small-size defect type of the distribution transformer can be realized.

The invention discloses a non-invasive hemodynamic detection and decision-making assistance method and device. The method comprises the steps of obtaining a first data set; preprocessing the first data set to obtain a second data set; an intracardiac prediction model based on a Transform architecture is constructed and trained; inputting the second data set to the trained intracardiac prediction model, and outputting the second data set as a third data set; collecting expert experience knowledge; constructing a relational expert knowledge base by adopting a knowledge representation method of a semantic network; performing trend analysis and correlation analysis on the third data set, the real-time electrocardiogram monitor physiological parameters and the electronic medical record to obtain an analysis result; performing matching reasoning on the analysis result and a treatment scheme of a corresponding case in an expert knowledge base to obtain a reasoning result; and generating personalized decision suggestions based on the reasoning result. According to the method, the treatment scheme decision is generated only based on the electrocardiograph monitor, and an efficient, intelligent and non-invasive diagnosis and treatment assistant is provided for clinical medical staff.