257 results about "Knowledge acquisition" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

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.

The invention relates to the technical field of data processing, and discloses an AI knowledge base construction method and system for intelligent water conservancy. The method comprises the following steps: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literatures; knowledge sprouts are identified, and a water conservancy native knowledge unit is generated; key features are extracted, knowledge element decomposition is carried out, and a knowledge evolution chain network is constructed through correlation analysis; screening in combination with a historical application effect and expert evaluation to obtain a preferred knowledge body; performing intelligent reconstruction on a scheduling rule, risk prevention experience and an operation and maintenance method to form an innovative knowledge base; and finally, intelligently matching with real-time engineering requirements to generate an intelligent water conservancy AI knowledge base. According to the application, by establishing a self-adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to the environment change, the knowledge structure is optimized, and the intelligent operation management level of the water conservancy project is improved.

The invention discloses a multi-modal knowledge graph construction method and device based on a large model and a program product, belongs to the technical field of artificial intelligence and knowledge engineering crossing, and particularly relates to a knowledge graph dynamic construction and evolution method based on a large language model technology and multi-modal data processing. The problems of symbol grounding and semantic understanding obstacle in the prior art are solved. According to the method, innovation and breakthrough are realized in three dimensions of knowledge acquisition, representation and reasoning by fusing deep learning and knowledge engineering technologies. The multi-modal knowledge graph construction method and device based on the large model and the program product are applied to the field of multi-modal knowledge graph construction and are suitable for specific task scenes such as intelligent question and answer, decision support and semantic search.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

The invention relates to the technical field of knowledge base construction, in particular to a knowledge service system construction method, which comprises a knowledge acquisition step for collecting knowledge data from knowledge sources of multiple fields; the preprocessing step is used for preprocessing the knowledge data; the classification step is used for classifying the obtained knowledge data; the similarity calculation step is used for calculating the similarity between different categories of knowledge bases; the first judgment step is used for judging whether the similarity is greater than a first threshold value or not; the association step is used for establishing association between the knowledge according to the similarity; the second judgment step is used for judging whether the similarity is greater than a second threshold value or not; the labeling step is used for constructing a new upper label; a label directory generation step for establishing a label directory; a query demand acquisition step for acquiring a knowledge retrieval demand of a user and generating a query request; the knowledge retrieval step is used for conducting knowledge recommendation according to the query request. The method has the effect of improving the searching efficiency.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

The invention relates to a platform management method and system based on low code development, and the method comprises the steps: obtaining a plurality of heterogeneous data sources, extracting first data from the plurality of heterogeneous data sources, and carrying out the preprocessing of the first data, and obtaining standard data used for constructing a knowledge graph; and acquiring a plurality of different entities and entity relationships among the different entities from the standard data, and constructing the knowledge graph according to the plurality of different entities and the entity relationships among the different entities. And mapping entity nodes in the knowledge graph and entity relationships among different entity nodes to a low-dimensional space through a graph embedding model to perform vector representation so as to extract global knowledge including the entity relationships among different entity nodes and a network topology structure. And acquiring a predefined business process and a plurality of corresponding decision points when the business process is executed, and evaluating an execution result of the business process by executing global knowledge and a current business process variable to obtain a corresponding guidance decision.

The invention discloses a database operation and maintenance method, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a user intention matched with an operation and maintenance demand problem of a user; if the intention is a general operation and maintenance knowledge acquisition demand, performing retrieval in a knowledge base according to an operation and maintenance demand problem, and generating a first response result for feedback; if the intention is an actual operation and maintenance scene knowledge acquisition demand, real-time operation data of a database is acquired through a database monitoring module, retrieval is performed in a knowledge base according to the real-time operation data and an operation and maintenance demand problem, and a second response result is generated for feedback; and if the intention is to execute the target operation and maintenance task, retrieving an operation and maintenance instruction set matched with the target operation and maintenance task in the knowledge base, and issuing the operation and maintenance instruction set to an operation and maintenance task execution module, so that the operation and maintenance task execution module arranges and executes the target operation and maintenance task according to the task priority. According to the embodiment of the invention, the workload of operation and maintenance personnel is reduced while the operation and maintenance efficiency is improved.

The invention relates to an intelligent question and answer method, system and device for electric power safety knowledge and a storage medium, and the method comprises the following steps: carrying out the semantic analysis of a standardized electric power safety knowledge text, and obtaining an electric power safety knowledge graph; obtaining an electric power security natural language question input by a user, and performing word segmentation and part-of-speech tagging on the electric power security natural language question to obtain an electric power security question keyword; performing mapping matching on the power security question keyword and the power security knowledge graph to obtain a preliminary answer candidate set; and scoring the preliminary answer candidate set, and taking the answer of the power security question with the highest score as the intelligent question and answer of the power security natural language question, thereby solving the problems that an existing power security knowledge acquisition mode mainly depends on manual query and expert consultation, time and labor are consumed, and the efficiency is high. And the influence of human factors is easily caused.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

The invention discloses an integrated emotion metaphor recognition method and device based on multi-scale semantic knowledge acquisition, equipment and a medium, and relates to the technical field of natural language processing. The identification method comprises; the method comprises the following steps: S1, acquiring text data, and constructing an emotion metaphor corpus; s2, performing multi-scale marking on the emotion metaphor corpus according to the text data to obtain a multi-scale feature matrix; and S3, performing multi-scale semantic feature extraction on the multi-scale feature matrix according to the decision set to obtain an optimal-scale semantic matrix. And S4, separating a prototype mode vector and a test mode vector from the optimal scale semantic matrix, and constructing a metaphor sequence parameter and an emotion sequence parameter which reflect the similarity of the two vectors through an inner product method. And reconstructing sequence parameters through the integrated dynamic evolution model, and screening out an emotion metaphor labeling mode of the sentence to be recognized from the reconstructed sequence parameters. And S5, identifying metaphor labels and emotion categories in the text according to the emotion metaphor labeling mode to obtain an emotion metaphor identification result.

The invention relates to the technical field of term libraries in the field of constructional engineering, and discloses a construction application method and system for a term library in the field of constructional engineering, and the method comprises the steps: preprocessing unstructured data to obtain structured constructional engineering text data, and extracting the structured constructional engineering text data based on a joint extraction architecture to obtain an extraction result; the method comprises the steps of obtaining structured data of terms and building engineering triple information, establishing a standard library and a term library according to the structured data, constructing a term-standard double-layer knowledge graph based on internal association of the standard library and the term library and the building engineering triple information, and responding to query of a user. And calling the standard library, the term library and the term-standard double-layer knowledge graph by a mixed decision-making mechanism based on rule reasoning and graph neural network learning to obtain a query result. According to the invention, the full-life-cycle intelligent closed loop of construction engineering terms from extraction, management to application is realized, and the problems of difficult acquisition of industry term knowledge, complex management and low application efficiency are solved.

The invention relates to the technical field of knowledge management, in particular to a knowledge base management system based on artificial intelligence, which comprises a knowledge acquisition module, a knowledge arrangement module, a knowledge updating module, a knowledge recommendation module, a user interaction module, a knowledge security module and a system management module. The system automatically obtains and sorts structured and unstructured data from multi-source data, generates a knowledge graph and performs hierarchical classification. The content of the knowledge base is dynamically adjusted and updated through time sequence analysis and a self-adaptive updating mechanism, and personalized recommendation is achieved in combination with user behaviors and historical queries. The system adopts an advanced encryption standard and a role access control technology to ensure the security of the knowledge base, so that the knowledge acquisition, arrangement and recommendation efficiency is remarkably improved, meanwhile, the high availability and data security of the system are ensured, and the system is suitable for a large-scale knowledge management scene.

The invention discloses a group chat questioning method and device, equipment, a storage medium and a computer program product, and relates to the technical field of intelligent AI, the group chat questioning method comprises the steps that user questions in a discussion group are acquired, the discussion group comprises a user and a target model group, the target model group comprises at least one large model group member, and the user questions in the discussion group are acquired; the large model group members correspond to at least one large model; inputting the user question into a target model group corresponding to the discussion group to obtain large model answer information of at least one large model group member to the user question; and generating and displaying a model reply result according to the large model reply information. Through the mode, the user can obtain collaborative answers and help of a plurality of large models without additional operation, the interaction efficiency with the AI large model is improved, and the comprehensiveness of knowledge acquisition is expanded.

The invention provides a digital human intelligent question-answering system and method for a water affair scene, and relates to the technical field of intelligent water affair, and the method comprises the steps: an inquiry receiving module obtains a user question through a digital human; the data updating module obtains real-time water affair data and updates the extended database; the question retrieval module is connected with the intelligent question and answer model and the water affair knowledge base, semantic matching retrieval is performed in the water affair knowledge base based on the user question through the intelligent question and answer model, and a matched question is obtained; an answer matching module queries a matching answer in an answer data set of the water affair knowledge base based on the matching question; the real-time data query module queries the associated real-time water affair data in the extended database based on the matching problem, and generates a real-time data query tag; and the interaction feedback module displays the matching answer and the real-time data query tag through a digital person. According to the invention, the technical problem of low water affair knowledge propagation efficiency caused by tedious water affair knowledge acquisition modes in the prior art is solved, and the water affair knowledge propagation efficiency and propagation effect are improved.

The invention relates to an AD-RAG-based vehicle after-sales maintenance document analysis and maintenance knowledge acquisition system and method, and the system comprises a document analysis module which carries out the layout analysis and document recognition of after-sales maintenance document information, and obtains structural data; the knowledge organization module is used for establishing an association relationship between the text data and the structural relationship in the structured data to obtain a maintenance knowledge graph; the knowledge retrieval enhancement module is used for analyzing and expanding the vehicle fault query request based on an AD-RAG model to obtain an expanded query request, and querying in a maintenance knowledge graph based on the expanded query request to obtain document fragment information; and the task decomposition and reasoning optimization module analyzes and decomposes the extended query request to obtain at least one fault sub-problem sequence, and performs fault reasoning through a preset fault reasoning model based on the fault sub-problem sequence and the document fragment information to obtain a fault maintenance suggestion result. The retrieval requirement can be better met, and the obtaining efficiency of the maintenance knowledge can be improved.

The invention discloses a knowledge network construction and recommendation method for artificial intelligence teaching, and relates to the technical field of education informatization, and the method comprises the steps: collecting structured course database data, semi-structured teaching material data and unstructured audio and video resources; executing cross-modal knowledge analysis by adopting a bidirectional sequence labeling model, and extracting an artificial intelligence teaching knowledge subject object and a multi-dimensional feature description thereof; constructing a knowledge unit, and adopting a model of'knowledge subject-feature dimension-feature value 'and'knowledge subject-association relationship-association knowledge subject'; vectorized semantic calibration is realized through an embedded space mapping technology, and heterogeneous semantic unit fusion is completed in combination with a dynamic similarity threshold mechanism; and screening high-quality knowledge units based on a credibility verification engine to construct an artificial intelligence teaching knowledge network. According to the invention, the organization efficiency and personalized service accuracy of artificial intelligence education resources are effectively improved, and the problem of knowledge acquisition efficiency bottleneck caused by discretization of multi-source teaching resources is solved.

The invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, and relates to the technical field of intelligent manufacturing and artificial intelligence. Natural language output of a large language model is converted into rules, facts and ontology fragments which can be directly consumed by an inference engine through knowledge obtaining and compiling, continuous increment updating of cross-domain knowledge is achieved in cooperation with metadata with sources and timestamps, and the limitation that a traditional static knowledge base is difficult to cover dynamic faults is overcome; secondly, a blackboard and agenda mechanism is used as a cooperative carrier, intermediate assertions of intelligent agents such as vibration, circuits and logs are published and subscribed in a structured mode, and a conflict resolution and consistency verification module carries out unified judgment according to specificity, time freshness and source credibility; therefore, delay and uncertain accumulation caused by long-chain natural language dialogues are avoided in a strong real-time scene.

The invention relates to the technical field of computers, and discloses a fragmentation block chain federal learning method based on a large language model multi-agent, and the method comprises the steps: S1, initializing a client; s2, dynamic fragmentation scheduling and distribution; s3, generating and uploading local knowledge; s4, intelligent agent collaborative routing and knowledge acquisition; s5, knowledge fusion and model updating; and S6, repeatedly executing the steps S3 to S5 until the model converges or reaches a preset number of iterations. According to the invention, under a decentralized and fragmented federated learning architecture, the complex reasoning ability of a large language model and the autonomous cooperation mechanism of three multi-agent systems, namely a fragmented scheduling agent, a fragmented knowledge state agent and a global knowledge routing agent, are deeply fused; according to the mechanism, dynamic optimization of a bottom layer fragment structure and intelligent routing of high-value knowledge are achieved in an intelligent mode, and therefore the overall efficiency and model performance of a system under the condition of heterogeneous data and heterogeneous equipment are remarkably improved.

The invention relates to the technical field of language analysis, and discloses a medical literature interpretation method and system based on a language large model, and the method comprises the steps: constructing a relationship network between words, and obtaining a first relationship network; determining a first association relationship between each text word and other words in the text, and endowing the first association relationship with a weight according to the number of association times; constructing a relationship network for words in the text according to the text content to obtain a second relationship network; determining a second association relationship between each text word and other words in the text, and endowing the second association relationship with a weight; and according to the first relationship network and the weight endowed by the first relationship network, performing optimization of the association path, and obtaining an interpretation result through content interpretation on the path. The defects of a traditional calculation method in the aspects of processing efficiency and semantic understanding are overcome. The accuracy and efficiency of literature analysis are greatly improved, and the convenience of knowledge acquisition is greatly improved.

The invention relates to the technical field of data center intelligent operation and maintenance, and particularly provides a power supply and distribution digital twinning application method and device for emergency deduction, and the method comprises the following steps: S1, data collection and processing; s2, power supply and distribution twin modeling; s3, knowledge acquisition; s4, constructing a knowledge graph; and S5, knowledge reasoning. Compared with the prior art, the method can solve the current situation that the emergency drilling scene and the online application are relatively separated, and deeply integrates the power supply and distribution digital twinning and the emergency drilling scene.

The invention relates to the technical field of intelligent assistance, and discloses an auxiliary system based on an intelligent wearable device used in daily life, and the system comprises a health signal monitoring and analysis module which is used for monitoring the heart rate, blood oxygen, sleep and body temperature of a user, generating a health report, and according to the health data of the user; the safety and emergency calling module is used for calling for help, positioning and safety monitoring in emergency; the smart home control module is used for controlling smart home appliances through Bluetooth and Wi-Fi, adjusting indoor temperature and humidity and being linked with a safety system; and the education and learning module is used for providing online learning and knowledge acquisition functions. Real-time monitoring of key health indexes such as heart rate, blood oxygen, body temperature and sleep of a user is achieved, and early warning is given out in time when abnormal conditions are detected. Besides, the system provides multi-level safety guarantee for the user through the functions of emergency call, position sharing, home monitoring and the like, and is suitable for daily health management of the old and chronic disease patients.

The invention discloses an agricultural multi-source knowledge migration method based on a graph model, and belongs to the technical field of agricultural information, and the method comprises the steps: carrying out the reduction, feature selection and feature fusion of agricultural literature data; the data processing efficiency and accuracy are improved by utilizing agricultural data reduction based on self-organizing mapping and feature selection and fusion based on spatial mapping; constructing a knowledge migration network based on a graph model, and performing knowledge semantic extraction and knowledge migration relationship mining; agricultural multi-carrier meta-knowledge acquisition and information feedback are adjusted, and meta-knowledge acquisition and network weight adjustment are optimized. According to the method, large-scale and heterogeneous agricultural data can be effectively processed, effective migration of knowledge is realized, the performance of agricultural learning tasks is greatly improved, and the method is of great significance to improvement of agricultural production efficiency.

The invention discloses a generative artificial intelligence-based banquet creative design system, which comprises: a demand determination module, which is used for interacting with a user terminal, obtaining a user demand and generating a demand theme; the banquet creativity generation engine is used for determining and operating a proxy artificial intelligence assembly line and generating a banquet creativity design project; comprising a main control unit, a domain knowledge acquisition unit, a creativity generation unit and a integrating degree evaluation unit, each unit is embedded with a multi-level intelligent agent as an execution main body, and prompt information is defined as input of the execution main body; the brand domain knowledge vector library is used for providing domain knowledge; the banquet design domain knowledge vector library is used for providing banquet design knowledge; and the design output module is used for obtaining the banquet creative design project and forming a banquet creative design scheme. According to the technical scheme, multi-modal brand domain knowledge and banquet creative design can be deeply fused, and automatic generation of multi-modal information of a high-end banquet catering art scheme is achieved.

The invention specifically relates to the technical field of multi-modal large models, and discloses an automobile production teaching evaluation optimization system based on a large model, and the system comprises an industry knowledge obtaining module, a knowledge graph construction module, a teaching resource generation module, a capability data collection module, an intelligent capability evaluation module, and a resource matching cooperation module. An industry knowledge acquisition module acquires an automobile production and teaching fusion dynamic knowledge data set, a knowledge graph construction module constructs an automobile production and teaching knowledge graph, a teaching resource generation module generates teaching resources, and a capability data acquisition module acquires learning capability associated data of students. Student ability comprehensive scores are calculated through the intelligent ability evaluation module, personalized learning paths and learning materials are recommended for students through the resource matching cooperation module, talent lists are recommended for enterprises, automatic and high-frequency updating of teaching content is achieved, and intelligent scheduling of the whole process and intelligent upgrading of automobile production teaching evaluation are achieved. And the production and education docking efficiency is greatly improved.

The present invention relates to a platform management method and system based on low-code development, the method comprising: obtaining multiple heterogeneous data sources, extracting first data from the multiple heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph. A plurality of different entities and entity relationships between different entities are obtained from the standard data, and a knowledge graph is constructed based on the plurality of different entities and entity relationships between different entities. The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures. A predefined business process and a plurality of decision points corresponding to the execution of the business process are obtained, and the execution results of the business process are evaluated by executing global knowledge and current business process variables to obtain corresponding guiding decisions.

The invention discloses an intelligent auxiliary decision-making method and system for multi-modal data fusion of multiple myeloma, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-source clinical data and authoritative literature of a patient, and building a literature database after preprocessing; configuring an interface to automatically capture new literatures, and generating a dynamic knowledge base in combination with semantic conflict detection, knowledge extraction and expert auditing; according to the knowledge base, fusing the multi-modal clinical data by adopting federal learning to generate a fusion feature matrix; constructing a causal relationship network, combining matrix prediction and generating a causal evidence network, and grading the causal relationship; and associating the knowledge base with the evidence network, generating an interpretable report containing a prediction result and the like, and realizing full-process traceability. Through three designs such as automatic knowledge acquisition and updating, the static lag problem of the knowledge base is broken through, and the medical frontier is synchronized; a causal reasoning system is constructed, defects of a traditional model are overcome, transparent reasoning is achieved, and the credibility of doctors is improved.