752 results about "Knowledge extraction" patented technology

The invention discloses a multi-source heterogeneous data knowledge graph construction method for railway disaster prevention monitoring, and relates to the technical field of knowledge graph construction, and the method comprises the steps: gathering multi-source heterogeneous data related to railway disaster prevention monitoring, and constructing a domain ontology model used for guiding knowledge extraction and fusion; extracting entities, attributes and relationships among the entities from different modal data after standardization preprocessing by using a targeted extraction algorithm; obtaining fused structured knowledge based on a multi-strategy knowledge fusion process of domain ontology constraint and confidence evaluation; the fused structured knowledge is stored in a graph database, and construction of the knowledge graph in the railway disaster prevention monitoring field is completed; through combination of domain ontology construction, a mixed knowledge extraction engine and a multi-strategy knowledge fusion technology, deep semantic fusion of multi-source heterogeneous data in the railway field is realized. The invention aims to construct a knowledge graph capable of comprehensively and accurately reflecting complex characteristics in the railway disaster prevention field.

The invention discloses a wind power fault diagnosis operation and maintenance method based on multi-source data and knowledge retrieval enhancement. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, knowledge base construction: performing knowledge extraction, and constructing a corresponding wind turbine generator operation and maintenance knowledge graph database; s3, multi-granularity problem decomposition and retrieval optimization are carried out, and a sub-problem list is generated by deeply grading and splitting problems in combination with doubt degree and dependency analysis; s4, knowledge retrieval enhancement generation: combining knowledge graph sub-graph retrieval and SCADA real-time data dynamic weight adjustment to generate a final answer; and S5, result generation and feedback optimization are carried out, and the validity of the diagnosis result is verified through a real-time verification mechanism. According to the method, the accuracy, the reliability and the real-time performance of fault diagnosis can be remarkably improved in a limited fault data environment.

The invention relates to an enterprise-level simulation knowledge graph construction method based on multi-modal data integration, and belongs to the technical field of knowledge graphs. The method comprises the following steps: integrating structured data, semi-structured data and unstructured data through a multi-modal data warehouse; performing knowledge extraction on the semi-structured data and the non-structured data to obtain entities and relationships, and performing knowledge fusion; storing the fused entities and relationships by using a graph database, and constructing a simulation knowledge graph; a vector database is embedded in combination with a simulation knowledge graph, semantic extension search is realized through multi-modal joint search, similar cases are searched through a simulation result graph, and a simulation scheme comparison matrix is automatically generated. The multi-modal data is effectively integrated, the comprehensiveness and accuracy of knowledge graph construction are improved, more powerful, efficient and intelligent support is provided for simulation analysis of enterprises, and the enterprises can be assisted in rapidly making scientific decisions in complex and changeable business scenes.

The invention provides a multi-modal knowledge extraction method and system based on multi-agent collaborative optimization, and relates to the technical field of knowledge extraction, and the method comprises the steps: carrying out the multi-modal deconstruction of an original document to be extracted; constructing a multi-modal agent, respectively executing feature extraction and preliminary knowledge extraction, and outputting a single-modal multi-component system; based on a cross-modal knowledge graph, mapping information of different modals to a unified semantic node, and establishing cross-modal association and analyzing a logic chain through a graph neural network and a causal reasoning module; dynamically allocating resources according to the importance of map nodes, and screening structured knowledge; and through confidence analysis and node traceability evaluation, an intelligent agent cooperation mechanism is optimized, and increment correction is carried out on a result. According to the method and the device, the technical problem of low knowledge extraction accuracy and efficiency caused by insufficient multi-modal knowledge collaborative mining capability due to knowledge extraction of literatures by adopting a single agent in the prior art can be solved, and the knowledge extraction quality and efficiency are improved.

Methods, systems, apparatuses, devices, and computer program products are described. A system may obtain a set of documents associated with a knowledge base for retrieval-augmented generation (RAG). The system may generate multiple representations of the information included in the documents using multiple knowledge extraction pipelines. For example, the system may generate a set of metadata-based vector embeddings based on the documents, a set of knowledge graphs based on the documents, and a set of hierarchical tree representations based on the documents. The system may receive a user query and may retrieve contextual information from the set of vector embeddings, the set of knowledge graphs, and the set of hierarchical tree representations to augment the user query for a large language model (LLM) prompt. The system may input the prompt to the LLM, and the LLM may output a response based on the user query and the contextual information.

The invention provides a clinical decision-making method and system based on a large language model and a knowledge graph, and belongs to the technical field of artificial intelligence and intelligent diagnosis and treatment crossing. The method comprises the following steps: S1, training and deploying a created generative large language model and a knowledge extraction model; s2, extracting medical knowledge from the medical data set through a knowledge extraction model; s3, constructing a medical knowledge graph based on the medical knowledge; s4, acquiring an input medical question, inputting the medical question into the generative large language model, querying medical knowledge corresponding to the medical question by the generative large language model through the medical knowledge graph, generating a medical answer based on the medical knowledge, recording a decision basis chain in the query process, and feeding back the medical answer and the decision basis chain; and S5, recording a question and answer log including the medical questions, the medical answers and the decision basis chain. The method has the advantages that the accuracy, the reliability, the timeliness and the safety of clinical decision making are greatly improved.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses a building elevator detection, diagnosis and decision-making method based on a graph retrieval enhanced agent. The method comprises the steps that 1, elevator detection data are prepared and processed; step 2), knowledge extraction; step 3), knowledge fusion; step 4), visualization and optimization of the knowledge graph; 5) performing graph retrieval enhancement generation; step 6), diagnosing a decision-making agent; according to the method, triple information can be extracted from structural data, text data, visual data and other multi-modal data in the elevator detection field by guiding a multi-modal large model through an elevator detection technical specification, and an elevator detection visual target entity and a text named entity are automatically aligned based on a pre-trained vision-language model; the multi-modal knowledge graph in the field of elevator detection is accurately and efficiently generated, and building elevator detection intelligent diagnosis is carried out on the basis of the multi-modal knowledge graph and the fusion graph retrieval enhancement technology.

The invention relates to the technical field of information retrieval, in particular to an intelligent RAG knowledge base system fused with a dynamic knowledge graph. Comprising a data processing module for preprocessing original data to obtain a standardized data set; the graph construction and updating module is used for carrying out knowledge extraction on the standardized data set by utilizing a large model, constructing a knowledge graph and carrying out dynamic updating; the dialogue management and understanding module is used for recording complete information construction of a current dialogue, updating a dialogue state and calculating a retrieval weight adjustment amount; the mixed retrieval module is used for performing mixed retrieval in combination with DPR and PPR retrieval methods to obtain a first retrieval result and a second retrieval result, setting an initial retrieval weight according to the question type, and obtaining a comprehensive retrieval result in combination with the retrieval weight adjustment amount; and the response optimization module is used for outputting an optimal response according to the comprehensive retrieval result and user feedback by utilizing reinforcement learning based on strategy gradient. According to the method, the retrieval and response generation effect can be optimized, and the user satisfaction is improved.

The invention relates to the technical field of knowledge extraction, in particular to a knowledge graph construction method and system based on a large model, and the method comprises the following steps: obtaining a current input statement of a user through a dialogue state tracker, inputting the statement into a BERT intention classification model for domain label analysis, behavior type recognition and emotional tendency detection, and outputting a three-dimensional classification vector; and extracting entity lexical items and relation predicates based on an LSTM sequence tagging device, and generating an original semantic structural body. According to the method, intention classification, behavior recognition and emotion detection are fused through three-dimensional semantic analysis, semantic comprehension granularity is improved, dynamic entity disambiguation is combined with a Manhattan distance threshold value and dialogue history tracking, semantic boundaries are defined to reduce anaphora ambiguity, and cross-modal alignment is enhanced through relation predicate hierarchical clustering and knowledge base dynamic matching; generative reply and semantic coherence reordering collaboratively keep topic continuation, and structured analysis and unstructured generation closed loop optimize semantic output and interaction fluency.

The invention discloses an expert question and answer technical method, system and equipment based on local geological knowledge graph semantic reasoning, and the method comprises the following steps: collecting and integrating multi-source heterogeneous geological data, including texts, images and remote sensing, and constructing a unified geological knowledge base covering multi-modal data; on the basis of a pre-training language model, semantic analysis is performed on a geological text, and'entity-relationship-entity 'structured knowledge is automatically generated by utilizing a triple extraction module. The method has field breadth and multidisciplinary fusion, is different from a knowledge graph technology focusing on single fields of mineralogy, geophysics and the like in the prior art, innovatively constructs a large-scale comprehensive knowledge graph system covering the whole geological disciplinary, supports cross-field knowledge extraction and reasoning, and is high in practicability. And complex interdisciplinary geological problems can be handled.

The invention provides a root cause positioning method which can be applied to the technical field of artificial intelligence. The root cause positioning method comprises the following steps: acquiring data in a configuration management database, a network topology tool, a monitoring system and a work order system to form a multi-source heterogeneous data set; performing knowledge extraction on the multi-source heterogeneous data set, extracting equipment attributes, network topological relations, fault event entities and timestamps, and storing the equipment attributes, the network topological relations, the fault event entities and the timestamps as structured knowledge; mapping real-time index data in the structured knowledge into dynamic attributes of an entity, and constructing a dynamic knowledge graph; based on a graph neural network and in combination with time sequence features, learning a time sequence dependency relationship and a propagation path between fault events in the dynamic knowledge graph; and outputting a root cause entity, a confidence score and a fault propagation path of the fault event through a causal inference algorithm in combination with the multi-dimensional evidence. The invention further provides a root cause positioning device and equipment, a storage medium and a program product.

The invention discloses a wind turbine generator operation and maintenance knowledge base construction method based on a large model and mechanism self-learning. The wind turbine generator operation and maintenance knowledge base construction method comprises the steps of wind turbine generator operation and maintenance domain knowledge Schema definition and large model cue word template design used for wind turbine generator operation and maintenance knowledge extraction; obtaining operation and maintenance multi-modal data of the wind turbine generator, performing preprocessing, and performing knowledge extraction through a large model based on a designed cue word template; a dynamic knowledge association and wind turbine generator operation and maintenance knowledge base fault mechanism self-learning updating mechanism is established, operation and maintenance data and a knowledge graph are associated in real time, and the knowledge base is automatically learned and updated through an exception triggering mechanism; and constructing and storing a wind turbine generator operation and maintenance knowledge graph based on a knowledge extraction result, generating a semantic association sub-graph through clustering, generating a sub-graph clustering report, and realizing efficient knowledge retrieval. Based on the above content, the wind turbine generator operation and maintenance knowledge base which is efficient, accurate and updated in real time is constructed.

The invention relates to a construction method and device of a network security knowledge graph, equipment and a storage medium, and the method comprises the steps: obtaining multi-source data of a network security application scene, and carrying out distributed processing to obtain a security data set; performing structured storage management and security knowledge extraction on the security data set to obtain an initial knowledge graph; performing zero-day attack prediction construction based on the initial knowledge graph to obtain potential attack data; performing path fusion association on the potential attack data and the initial knowledge graph to obtain attack chain fusion information; and obtaining real-time event data of the network security application scene, and carrying out dynamic iteration updating on the initial knowledge graph by using the real-time event data and the attack chain fusion information to obtain the network security knowledge graph. According to the invention, the timeliness and practicability of the network security knowledge graph can be ensured.

The invention belongs to the technical field of mine ventilation monitoring, and aims to solve the problems that a traditional knowledge system based on a rule base has bottlenecks in the aspects of dealing with sudden working conditions, knowledge updating and semantic reasoning, and is high in construction cost and complex to maintain. The invention provides a mine ventilation knowledge graph construction method based on a large model, and the method comprises the following steps: S100, obtaining and cleaning a multi-source heterogeneous text related to mine ventilation, and obtaining a JSON format knowledge fragment of a unified structure; s200, constructing an ontology model and defining a semantic structure; s300, constructing four types of knowledge extraction tasks, and extracting entities, attributes and relationships; s400, constructing an entity alignment module; and S500, constructing a graph database structure. According to the method, automatic structured expression, semantic consistent fusion and intelligent visual query of knowledge in the mine ventilation field can be realized, and an interpretable, extensible and reasonable intelligent support platform is provided for a mine ventilation system.

The invention discloses a bidding document intelligent analysis and structuring method and system based on a multi-modal knowledge graph. The method comprises the steps of S1, obtaining bidding document content and technical data related to constructional engineering; s2, performing knowledge extraction on the text in the bidding document content, and constructing a multi-modal knowledge graph; s3, performing semantic optimization on the multi-modal knowledge graph to obtain an updated multi-modal knowledge graph; s4, converting the multi-modal knowledge graph into corresponding feature vectors, and fusing the feature vectors; s5, inputting the multi-modal data into the constructed multi-modal deep learning model for processing, and outputting element information of bidding document structuring; s6, performing hierarchical division on bidding document contents according to the element information, and identifying contents of different hierarchies; and S7, constructing a rule base according to the obtained technical data, and performing matching verification on the standard knowledge in the knowledge graph through the rule base.

The invention relates to the technical field of artificial intelligence and material engineering, and discloses a formula optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the knowledge extraction of an obtained structured formula data set and unstructured technical literature data in response to a formula optimization target input by a user, and generating a table literature knowledge set; inputting the table literature knowledge set and the formula optimization target into a large language model to obtain a generated text corresponding to the formula optimization target; performing logic rule screening and risk assessment on the generated text through a knowledge fusion layer to obtain text output conforming to a confidence threshold, and generating a new formula scheme according to the text output; and performing multi-objective optimization on the new formula scheme based on a preset experimental cost constraint condition, and outputting an optimized recommended formula and a support evidence chain. By automatically fusing innovative components and process information in literatures, the output recommended formula has scientific basis and interpretability, and the practicability and innovativeness of an automatic formula are improved.

The invention relates to a knowledge graph construction method, device and equipment based on multi-source data. The method comprises the following steps: acquiring original data from different data sources; preprocessing the original data to obtain target text data; wherein the preprocessing comprises format conversion, text cleaning and normalization and / or sentence segmentation and segmentation; performing knowledge extraction on the target text data through a pre-optimized large language model to obtain original structured data including an original entity, an original relationship and an original attribute; post-processing the original structured data to obtain target structured data including a target entity, a target relationship and a target attribute; wherein the post-processing comprises format analysis, entity standardization and ambiguity elimination, and relation and attribute verification; and updating nodes and edges of the current knowledge graph according to the target structured data. The method can adapt to multi-source heterogeneous data, and the accuracy and consistency of the knowledge graph are improved.

The invention discloses a network information security dynamic early warning method and system based on a knowledge graph, and relates to the technical field of network information security. According to the method, network equipment logs, threat intelligence texts and flow metadata are acquired in real time through a multi-source heterogeneous data acquisition module, and a dynamic knowledge graph is constructed by adopting a streaming knowledge extraction technology. And predicting a threat propagation path by using a graph attention network and gating loop unit hybrid model, and generating a hierarchical defense strategy in combination with a three-dimensional risk assessment value. The system comprises a multi-modal data acquisition module, a dynamic knowledge graph construction module, a threat propagation dynamics modeling module, a self-adaptive strategy generation module, a digital twinborn verification module and the like, and the effectiveness of a defense strategy is verified by simulating an attack path. According to the method, dynamic early warning and automatic response of network security are realized, the network protection capability is effectively improved, and the method is suitable for scenes such as enterprise network security protection and cloud service provider security protection.

The invention belongs to the technical field of urban low-carbon planning, and relates to a low-carbon community evaluation index library dynamic construction method and system. The method comprises the steps of knowledge graph mode layer construction, knowledge extraction, semantic relation enhancement and fusion and graph database storage. Defining the types of the entities and the semantic relationship between the entities to obtain a mode layer with a hierarchical topological structure; knowledge extraction: mapping the entity relationship in the mode layer into a knowledge extraction template; after an extraction result is subjected to subgraph structured organization, vector space mapping is carried out, semantic association strength among indexes is calculated, and entity alignment is carried out by adopting a knowledge fusion mechanism driven by a large language model; and importing the knowledge graph subjected to semantic relationship enhancement into a graph database through a query language. According to the method, efficient construction and expansion of the cross-domain index library can be realized; the adaptability of the index system is enhanced; and in combination with a knowledge fusion feedback mechanism, the mode layer is dynamically adjusted, so that the data processing efficiency and accuracy are improved.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

A knowledge graph construction method based on active learning and incremental learning comprises the following steps: S1, preprocessing data from a plurality of heterogeneous data sources, and extracting entities, relationships and attributes to form an initial knowledge network; s2, vectorizing elements in the initial knowledge network by using a knowledge graph embedding model, and performing entity alignment based on vector similarity to obtain an initial knowledge graph; s3, screening out candidate knowledge triples with high uncertainty and / or high representativeness from the initial knowledge graph by adopting an active learning strategy, and obtaining user labeling information corresponding to the candidate knowledge triples; s4, performing iterative optimization on a knowledge extraction model and / or a knowledge graph embedding model according to the user labeling information; s5, new data are fused into the optimized knowledge graph in an incremental learning mode, and knowledge conflict detection and resolution are carried out in the fusion process; and S6, circularly executing the steps S3 to S5 until the knowledge graph meets a preset quality condition.

The invention discloses a power grid knowledge graph construction method and system based on a large language model, and belongs to the technical field of artificial intelligence and power system crossing. The method comprises the following steps: firstly, carrying out standardized preprocessing and semantic segmentation on a multi-source heterogeneous power grid specification document to generate independent semantic fragments; extracting a power grid entity and a relation triple by using the large language model subjected to field fine tuning; constructing a multi-level knowledge graph taking the equipment as the center and supporting dynamic updating; and finally, performing multi-hop reasoning based on natural language query, and outputting decision support information. The system comprises a preprocessing module, a knowledge extraction module, a graph construction module and an interactive reasoning module. According to the method, the problems of difficulty in semantic analysis, low knowledge extraction efficiency and dynamic updating lagging of the unstructured power grid document are solved, and the intelligent level of power grid dispatching and the fault handling efficiency are remarkably improved.

The invention discloses a personalized learning system based on multiple agents and retrieval enhancement generation. The system comprises a knowledge extraction agent, a retrieval enhancement generation module, a planning teaching agent, a knowledge consolidation agent and a test evaluation agent. The knowledge extraction agent extracts document information from the original learning materials and converts the document information into semi-structured data; the retrieval enhancement generation module is used for storing the semi-structured data into a vector library in a vector form, retrieving information in the vector library according to a request sent by a user, and inputting enhancement information combined by the retrieval information and the request into a downstream agent; and the planning teaching agent, the knowledge consolidation agent and the test evaluation agent are respectively used for constructing a knowledge graph according to the received information to generate a personalized teaching plan, generating exercise questions to consolidate learned knowledge, evaluating a learning effect and providing feedback suggestions. The learning efficiency, the learning effect and the learning experience of the learner can be remarkably improved, and personalized learning can be met.

Methods, systems, apparatuses, devices, and computer program products are described. A system may obtain a set of documents associated with a knowledge base for retrieval-augmented generation (RAG). The system may generate multiple representations of the information included in the documents using multiple knowledge extraction pipelines. For example, the system may generate a set of metadata-based vector embeddings based on the documents, a set of knowledge graphs based on the documents, and a set of hierarchical tree representations based on the documents. The system may receive a user query and may retrieve contextual information from the set of vector embeddings, the set of knowledge graphs, and the set of hierarchical tree representations to augment the user query for a large language model (LLM) prompt. The system may input the prompt to the LLM, and the LLM may output a response based on the user query and the contextual information.

The invention provides a whole-crop explainable disease and pest diagnosis method and system based on a multi-mode large model. The method comprises the following steps: constructing a knowledge extraction model of a two-way cross attention mechanism based on relation guidance, extracting structured knowledge from authoritative agricultural data, and constructing a pest and disease knowledge enhancement database to dynamically retrieve prior knowledge of a target crop; a hierarchical image processing strategy is adopted, and global, local and target area multi-level feature information is extracted from an input image; inputting and priori knowledge are integrated into a comprehensive diagnosis instruction, and a thinking chain guiding module is introduced to guide a large model to carry out multi-step reasoning according to a reasoning path; and performing unified reasoning by using the multi-modal large model, and outputting a disease and pest diagnosis result and a diagnosis basis thereof. The method does not need manual marking of multi-modal data or retraining, can realize efficient diagnosis of whole crop diseases and insect pests under the condition that the multi-modal data and computing resources are limited, has low cost, strong generalization ability and high interpretability, and is suitable for large-scale agricultural production practice.

The invention discloses a field large model geological survey report generation method based on a knowledge graph, and the method comprises the following steps: S1, building an engineering survey field data set through multi-source heterogeneous data collection and structured preprocessing, and the engineering survey field data set comprises five text dimension tags divided according to engineering survey specifications; s2, constructing an engineering investigation knowledge graph; and S2A, knowledge extraction, wherein a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is adopted. According to the method, a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is improved on knowledge modeling to perform high-precision entity-relation joint extraction, a geological knowledge map with consistent semantics and clear structure is constructed based on RDF, rule reasoning and graph neural network reasoning mechanisms are fused, and the method has the advantages of high-precision entity-relation joint extraction and high-precision entity-relation joint extraction. Deep mining and complementation of explicit and implicit knowledge are realized, and the ability of the prior art in knowledge expression granularity and reasoning breadth is improved.

The invention provides a multi-mode PDF (Portable Document Format) document analysis method, device and equipment and a medium, and the method comprises the following steps: loading a PDF document and carrying out preprocessing, including page splitting and content cleaning, to generate standardized document data; dynamically extracting contents in the standardized document data, wherein the contents comprise paragraphs, pictures and table elements; processing the dynamically extracted pictures, including shielding meaningless pictures based on a preset rule, and analyzing picture contents by using a multi-modal model to generate readable picture information; processing the dynamically extracted table, including optimizing and merging the table structure into a single element format to generate structured table data; combining paragraphs, readable picture information and structured table data, converting the paragraphs, the readable picture information and the structured table data into a complete structured document format, and inserting in key positions to enhance coherence context description; and outputting the complete structured document format as a final analysis result. According to the method, the analysis precision and the knowledge extraction efficiency of the complex PDF document can be remarkably improved.

The invention discloses a broadcast television network security operation method and system based on a large model, and the method comprises the steps: collecting log, network flow and terminal behavior data in real time, storing the data in a Kafka message queue, receiving the data through an AI intelligent noise reduction module, extracting alarm text semantic features through an XLM-ROBERTa model, and carrying out the noise filtering through combining with a multi-classification model, thereby obtaining high-value data; inputting the high-value data and security document knowledge corresponding to the network threat intelligence into an RAG knowledge base module, generating enhanced context information data through knowledge extraction, vectorization storage and similarity retrieval, inputting the enhanced context information data into a cue word of a reply model, and outputting a first threat analysis reply; inputting the high-value data and the first threat analysis reply into a multi-model MOE architecture, distributing tasks through a gating network, integrating expert network output results, and generating a second threat analysis reply and a corresponding attack thermodynamic diagram; and executing feedback optimization operation of the corresponding module based on the second threat analysis reply, thereby improving the safety operation efficiency of the broadcast television network.

The invention discloses a ground fracture extraction deep learning method based on direction perception and Bayesian fusion, which solves the problem of feature fracture caused by the fixed form of the traditional convolution kernel by constructing a direction priori knowledge extractor, strengthening the perception ability of a network to the direction and adjusting the receptive field along the fracture trend by adopting the dynamic deformation convolution kernel. And a Bayesian fusion module is designed, the weights of the global semantic features and the direction perception features are dynamically distributed based on a Bayesian probability model, the fusion effect between different features is optimized, and compared with other deep learning methods, the ground fracture extracted by the method is more complete and can adapt to a mining area environment with relatively complex geological conditions.