74 results about "Knowledge modelling" patented technology

The invention discloses a dynamic intention recognition and GraphRAG fusion method and system for open domain questions and answers, and relates to the technical field of intelligent and natural language processing. The method comprises the following steps: constructing and dynamically updating a heterogeneous knowledge graph; performing dual-stage intention recognition on user query; subtasks are disassembled based on an intention result, and cooperative execution is carried out through multiple agents; service standardization integration is realized by means of a model context protocol; and closed-loop optimization is carried out in combination with user feedback. The system comprises an image knowledge base, an intention recognition module, an agent collaboration module, an MCP service integration module and a feedback optimization module. The system is a novel open domain question-answering system integrating structured knowledge modeling, dynamic intention understanding, intelligent collaborative reasoning and standardized service interaction, and through fusion of structured knowledge modeling and intelligent collaborative reasoning, question-answering accuracy and complex intention understanding ability are improved, system adaptability and efficiency are enhanced, multi-system collaboration is promoted, and the system has a wide application prospect. The method is suitable for scenes such as knowledge services and intelligent assistants.

The invention relates to the technical field of process optimization, in particular to a knowledge graph-driven manufacturing process optimization system, which comprises a graph node generation module, a dependency relationship construction module, a semantic entity matching module, a process path screening module and a trend-driven early warning module. According to the method, semantic implicit relation recognition is achieved through sequential alignment and numerical difference comparison, quantitative screening between the machining size and the tolerance grade is introduced in path construction, and it is ensured that a path connection structure is optimized under the condition that the size precision constraint is met; in combination with the time sequence trend splitting and continuous fluctuation consistency discrimination mechanism of the equipment operation state, the dynamic marking and early warning annotation set generation of the process deviation trend can be completed at the node path level, and the closed-loop processing from knowledge modeling, path screening to trend pre-judgment is realized. And the process path adaptability, the parameter configuration precision and the abnormality identification advancement are effectively improved.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention discloses a hypergraph structure knowledge representation-based question and answer method for retrieval enhancement generation, which comprises the following steps of: constructing a knowledge hypergraph based on a multivariate relationship of a natural language document; obtaining question data input by a user, and extracting a first entity in the question data; based on the first entity, performing entity retrieval from the knowledge hypergraph to obtain a second entity; performing hyperedge retrieval from the knowledge hypergraph based on the problem data to obtain a first hyperedge; performing hypergraph knowledge fusion on the second entity and the first hyperedge to obtain a hypergraph knowledge result; and generating a target answer based on the hypergraph knowledge result and question data input by the user. Therefore, according to the method, the limitation of binary relation representation is effectively solved, the accuracy and reasoning ability of knowledge modeling are improved, and then the retrieval efficiency and the accuracy of answers are improved.

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 discloses a code repository question and answer method based on a hierarchical code knowledge graph, which comprises the following steps: collecting all files of a target code repository, and preprocessing to obtain code files and text files; generating an abstract syntax tree from the code file, and extracting related entities; carrying out block segmentation on the text file, and extracting to obtain text blocks; constructing a code knowledge graph representing a code structure based on the extracted entities and text blocks; generating a hierarchical code knowledge graph by adopting a community detection algorithm and a hierarchical clustering method, and generating a functional abstract for each community in combination with a large language model; a Monte Carlo tree search-based code warehouse question and answer agent is used for performing question and answer path optimization, an optimal query track is dynamically planned through the steps of node selection, expansion, simulation, reward evaluation and the like, and a path with the highest accumulated reward is selected to generate a final question and answer result. The code knowledge modeling quality and the reasoning accuracy and efficiency of the question answering system can be effectively improved.

The invention relates to a superconducting power knowledge system construction method and system based on a lightweight large model. According to the method, multi-source data is utilized for unified representation, deep semantic information is obtained through a lightweight large model, knowledge extraction is achieved, and a basic knowledge graph is constructed; further forming a knowledge graph which can be dynamically updated through knowledge fusion; and in combination with real-time data, adaptive multi-hop reasoning is executed, and a diagnosis and decision result with a causal chain is generated. According to the method, the semantic understanding and reasoning ability of a lightweight large model is taken as a core, the structural expression advantage of a knowledge graph is combined, the limitation of updating lagging and fixed rule reasoning rigidness of a traditional static knowledge base is broken through, and precise cognition and causal chain inference of a complex system state are achieved. The method has universality and mobility, and is suitable for knowledge modeling and intelligent diagnosis of various electric power scenes and other complex industrial systems. The method is verified by taking a superconducting power system as an example, and the feasibility and effectiveness of the construction path are proved.

The invention belongs to the field of artificial intelligence and enterprise strategic technology prediction, and particularly relates to an enterprise technology evolution trend identification method and system based on large model reasoning. The system is composed of six modules: a data acquisition and preprocessing module unifies structured multi-source data; the enterprise technical portrait module constructs a research and development capability and technical trajectory map; the task analysis module utilizes a large model to understand natural language requirements and generates a multi-layer task structure; the knowledge modeling module is fused with the industry ontology to construct a semantic map; the trend identification module predicts a technical evolution path through time sequence modeling and multi-round reasoning; and the feedback optimization module forms a closed-loop learning mechanism based on the user behavior fine tuning model. According to the method, the semantic understanding and reasoning capabilities of a large model are fused, the knowledge graph, task analysis, trend modeling and a feedback mechanism are combined, the problems of insufficient structuralization, prediction lag and the like are effectively solved, the accuracy and foresight of enterprise technology trend recognition are improved, and intelligent support is provided for enterprise innovative deployment.

The invention provides an engineering carbon emission factor dynamic calculation and traceability analysis method and system, and belongs to the field of engineering construction. The method comprises the following steps: acquiring multi-source heterogeneous data of the whole engineering construction process, and preprocessing the multi-source heterogeneous data to form a data set supporting knowledge modeling and computational analysis; constructing a semantic knowledge structure in the field of carbon emission factors in the whole engineering construction process by utilizing a knowledge graph technology; carrying out dynamic calculation on the carbon emission factor and correcting the weight; and carrying out visual analysis on a dynamic calculation result and a correction result to complete dynamic calculation and traceability analysis on the engineering carbon emission factor. According to the method, the defects in a carbon emission accounting system in the existing engineering construction field are overcome.

The invention discloses an intelligent contract risk perception fuzzy testing method based on a large language model, and the method comprises the steps: introducing a risk perception mechanism based on the large language model, testing a resource scheduling strategy and semantic knowledge modeling, precisely guiding a fuzzy tester to focus on a high-risk region in an intelligent contract, and achieving the intelligent contract risk perception fuzzy testing. Therefore, the efficiency and accuracy of vulnerability detection are remarkably improved. According to the method, a risk strategy is generated by using a large language model, and the method comprises the steps of identifying a high-risk function and outputting a structured variation parameter suggestion; test resource allocation is optimized for the three key stages, and a fuzzy tester can test a high-risk code path preferentially; a knowledge graph is constructed by analyzing a function call relationship and a data dependency relationship in an intelligent contract, and the knowledge graph is used as a context semantic basis to assist a large language model in more accurately identifying a potential risk function and positioning a key mutation point.

The invention relates to the technical field of intelligent power grid monitoring, and discloses a power grid monitoring bionic robot intelligent decision-making method supporting rule self-learning, and the method comprises the following steps: S1, sensing and fusing multi-source heterogeneous data; s2, dynamic rule self-evolution and knowledge modeling are carried out; and S3, multi-mode collaborative decision and closed-loop control are carried out. Through a visual acquisition unit, a voice interaction unit and a plurality of industrial communication protocols, such as OPCUA, Modbus TCP and the like, multi-source heterogeneous data such as meter reading, indicator lamp state, mechanical part displacement, monitor voice instructions, equipment operation noise and the like of power grid equipment are comprehensively acquired, cross-system association is established by taking equipment codes as main keys through processing such as wavelet denoising, semantic coding conversion and the like, and multi-source heterogeneous data of the power grid equipment is acquired. A unified data integration view is constructed, and the problem that data acquisition is limited in a traditional monitoring mode is solved.

The invention discloses a water pollution traceability analysis and ecological restoration decision support system. The system comprises a data acquisition layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer and a user interaction and visualization layer. The system collects multi-source water quality, hydrology and discharge data by deploying a distributed water quality sensing network, remote sensing and unmanned aerial vehicle monitoring equipment and a pollution discharge source online access module; through data cleaning, fusion and knowledge modeling, a pollution propagation model and a traceability inversion algorithm are constructed, and a pollution source and a contribution rate are identified; and simulating a multi-class repair measure response process in the digital twin water environment, and automatically recommending an optimal repair scheme in combination with a multi-objective optimization algorithm. The method has the advantages of accurate traceability, scientific decision, eco-friendliness, dynamic optimization and the like, and is suitable for pollution treatment and ecological restoration scenes of various water bodies such as drainage basins, lakes, urban rivers and the like.

The invention provides a chat question-answering method and device based on knowledge precipitation, equipment and a storage medium, relates to the technical field of data processing, in particular to the fields of artificial intelligence, domain knowledge modeling and the like, and can be applied to application scenes such as chat conversation intelligent question-answering and the like. The specific implementation scheme is as follows: in response to a current message event in a chat dialogue, determining context information of the current dialogue; performing intention recognition based on the context information to obtain an intention recognition result; retrieving related domain knowledge from a knowledge base based on the intention recognition result; using the retrieved domain knowledge and context information to generate candidate answers; and carrying out quality scoring on the candidate answers, screening the answers with scores reaching a preset threshold value as target answers, and sending the target answers to the chat conversation. According to the scheme, the context can be constructed in the chat conversation, the intention can be recognized, the precipitated knowledge can be retrieved, and the high-quality answer can be generated, so that real-time reliable reply is provided, efficiency and accuracy are improved, and knowledge reuse and dynamic update are promoted.

The invention discloses a self-adaptive education system based on knowledge gene evolution, and belongs to the technical field of knowledge gene evolution. The system comprises a dynamic knowledge graph engine, a defect response type training sequence generator, a multi-modal memory intensifier and a hypercycle coordination module, wherein the dynamic knowledge graph engine decomposes subject knowledge into heritable units and constructs a dynamic association network of which the weight is updated in real time along with a learning data flow; a defect response type training sequence generator generates a target variable type training sequence according to the cognitive defect node coordinates; the multi-modal memory intensifier generates multi-sensory nerve coding pulses according to training feedback; and the hypercycle coordination module is connected in series with the modules to form a self-adaptive closed loop of knowledge modeling, defect clearing, memory curing and knowledge iteration. The system can generate a cognitive defect thermodynamic diagram, realizes accurate positioning of cognitive vulnerabilities, provides clear targets for teaching intervention, and improves the pertinence and effectiveness of adaptive education.

The invention is applicable to the technical field of tenant management, and provides a cloud-native-based power knowledge graph multi-tenant cluster management system, which comprises a multi-tenant cluster, a cloud native platform and a central dispatching center, and is characterized in that the multi-tenant cluster manages power grid data of tenants and allocates computing resources through a data access unit and a tenant management unit; the cloud native platform generates an electric power knowledge graph through a knowledge modeling unit and a data extraction unit; and the central dispatching center determines a fault propagation path and key equipment through the knowledge fusion unit, the reasoning calculation unit and the service interface unit, generates a fault diagnosis result, feeds back the fault diagnosis result to the tenant management unit, and triggers a tenant alarm or operation and maintenance instruction. According to the invention, through cooperative work of the multi-tenant cluster, the cloud native platform and the central dispatching center, the precision and efficiency of substation equipment fault diagnosis are effectively improved.

The invention discloses an online learning system based on cloud fusion multi-modal analysis. The online learning system comprises a multi-modal data acquisition module, a knowledge graph construction module, a learning state analysis engine, a dynamic course generation module and a virtual teacher interaction interface, wherein the multi-modal data acquisition module comprises an eye movement tracking sensor and a bone conduction microphone array; according to the system, in the aspect of data acquisition, the defects of single behavior data and low sampling rate are overcome, multi-modal physiological and environmental data fusion is adopted, and the effect that the eye movement sampling rate is 120 Hz is achieved; meanwhile, in the aspect of knowledge modeling, the system overcomes the defects of a static knowledge base and subject isolation, dynamically updates an interdisciplinary map, and achieves the effect of weight iteration per hour.

The invention provides a dam safety evaluation method and system based on a knowledge graph, and relates to the technical field of dam safety evaluation. The method comprises the following steps: collecting monitoring data of a typical dam section, and establishing a statistical model of related measuring points according to the collected monitoring data, so as to analyze the working state of the dam; establishing a finite element model of the typical dam section, and performing simulation calculation on the overall strength and stability of the typical dam section and the state of the new and old concrete joint surface to obtain a finite element simulation result; constructing a dam safety evaluation index system according to the collected monitoring data of the typical dam section, the finite element simulation result and the evaluation related data of the typical dam section; sorting a hierarchical system of the dam safety comprehensive evaluation index system, and performing knowledge modeling, knowledge extraction, knowledge fusion and knowledge storage processing according to a sorting result to construct a knowledge graph; and carrying out data calling and safety evaluation by utilizing the constructed knowledge graph. According to the scheme, dam safety evaluation work can be carried out more clearly, visually and efficiently.

The invention discloses a DIKWP natural language analysis enhancement method combined with a large language model, and belongs to the technical field of artificial intelligence and natural language processing. The method is realized through the following steps of: firstly, performing semantic pre-analysis on natural language input of a user by utilizing a large language model (LLM), and extracting five semantic fragments of data, information, knowledge, wisdom and intention; then the content is mapped to a DIKWP semantic map node in a structured mode through a DIKWP semantic mapping template system; then semantic consistency verification and logical reasoning are carried out through an LLM and DIKWP dual-channel cooperation mechanism; and finally, iteratively optimizing ambiguous, missing or conflicting contents by means of a language-semantic interaction closed loop until a complete and consistent DIKWP semantic map is generated. According to the method, the semantic comprehension ability of a large language model and the structural reasoning advantage of the DIKWP map are effectively fused, the accuracy, consistency and interpretability of complex natural language analysis are remarkably improved, and the method can be widely applied to intelligent question answering, knowledge modeling and decision support systems.

The invention relates to an abnormal sound identification method and device for an electric drive speed reducer, a vehicle and a readable storage medium, and the method comprises the steps: collecting the abnormal sound data of the electric drive speed reducer of at least one vehicle model; constructing an abnormal sound fault tree of the electric drive speed reducer according to the fault category of the abnormal sound data; and at least one abnormal sound key feature of the fault category is extracted according to the abnormal sound fault tree to construct a structured abnormal sound feature database, and an abnormal sound recognition model of the electric-driven speed reducer is constructed by using the abnormal sound feature database, so that an abnormal sound recognition result of the target electric-driven speed reducer is output by using the abnormal sound recognition model. The abnormal sound fault tree of the electric drive system can be constructed by combining abnormal sound feature engineering and knowledge modeling, the abnormal sound intelligent identification model is established based on a knowledge and data dual-drive method, and a maturely trained AI program is put into a service store end, so that the market support frequency of research and development personnel can be greatly reduced, and the development efficiency is improved. And efficient and accurate fault judgment of abnormal sound of the electric-driven speed reducer is realized.

This invention discloses a 3D electrical intelligent design system integrating image recognition and multimodal knowledge graphs. It includes a multimodal perception layer, a multimodal knowledge graph layer, an intelligent design engine layer, a full-process interaction layer, a dual-engine collaborative core unit, and a non-functional support unit. The multimodal perception layer collects multi-source data from drawings, 3D scenes, and operations and maintenance. The multimodal knowledge graph layer enables cross-modal knowledge modeling and reasoning. The intelligent design engine layer completes core design tasks such as automatic scheme generation and conflict detection. The full-process interaction layer enables multimodal human-computer interaction and collaborative design. The dual-engine collaborative core unit ensures efficient collaboration between image recognition and the knowledge graph. The non-functional support unit ensures the safe and reliable operation of the system. This invention solves the problems of shallow multimodal fusion, disconnect between design and operations and maintenance, and poor collaboration in existing electrical design systems, significantly shortening the design cycle, reducing the error rate, and improving the intelligence and accuracy of the design.

The application provides an engineering carbon emission factor dynamic calculation and traceability analysis method and system, and belongs to the field of engineering construction. The method comprises the following steps: acquiring multi-source heterogeneous data of the whole process of engineering construction, and pre-processing the multi-source heterogeneous data to form a data set supporting knowledge modeling and calculation analysis; constructing a semantic knowledge structure of the carbon emission factor field of the whole process of engineering construction by using knowledge graph technology; dynamically calculating the carbon emission factor and correcting the weight; and visualizing the dynamic calculation result and the correction result to complete the dynamic calculation and traceability analysis of the engineering carbon emission factor. The application solves the deficiencies existing in the current carbon emission accounting system in the field of engineering construction.

The present application discloses a data compression method and system based on a hybrid compression algorithm, which relates to the field of compression of time series measurement data. The method includes: obtaining a measured monitoring parameter sequence and historical measured monitoring seed data, and calling a prediction model to determine a predicted monitoring parameter sequence, and calculating a residual sequence; dividing the residual sequence into a low-variance segment and a high-variance segment according to the standard deviation of the residual sequence in each sliding window; performing clustering processing on the residual values of the low-variance segment to generate corresponding dictionary indexes, and performing frequency-domain transformation, quantization processing and entropy coding on the residual values of the high-variance segment to generate corresponding frequency-domain compression codes; performing lossless coding on the historical measured monitoring seed data to generate corresponding seed data compression codes, and obtaining measured monitoring compressed data for the measured monitoring parameter sequence through packing and combination. Thus, a hybrid compression algorithm is introduced at the edge device coding end, and the dependence on the full-scale analysis of real-time data is reduced through prior knowledge modeling.

The invention discloses a fair competition review case library construction and expansion method based on knowledge modeling and large language model generation, and the method comprises a case library construction stage and an intelligent expansion stage. Collecting data from the heterogeneous data source, analyzing the data into structured case data through an information extraction technology, and finally constructing case knowledge elements for describing case types, subjects and law article application relationships; in the expansion stage, based on existing case knowledge elements, a'case-legal provision 'relation model with variable scene parameters is constructed, diversified candidate case texts are generated by utilizing a large language model, and the candidate case texts are stored after being subjected to validity and logic consistency rechecking, so that efficient and high-quality automatic expansion of a case library is realized.

The invention discloses a cross-knowledge-point combined examination question generation method. The method comprises the following steps: decomposing data of each knowledge point into atomic knowledge points; mapping each atomic knowledge point into a leaf node of AST; calculating the comprehensive correlation degree of the two leaf nodes; constructing a composite node; determining a language expression sequence of the corresponding leaf nodes according to the associated logic labels; according to the language expression sequence, the content of the corresponding leaf node and the basic information corresponding to the knowledge point type, initial examination question information is generated; and performing semantic verification on the initial examination question information, and outputting the initial examination question information passing the verification to obtain target examination question information. The abstract syntax tree is used as a knowledge modeling and combination carrier, organic coupling of a plurality of independent knowledge points is achieved, the comprehensive examination questions are finally generated, multi-dimensional skills are covered, and actual operation logic is met. The invention further discloses a system for implementing the method, electronic equipment and a storage medium.

The application relates to the technical field of computer information processing, and provides an education resource semantic retrieval method based on a knowledge graph. The method constructs a science education knowledge graph by structurally processing course textbooks, courseware, multimedia resources and experimental videos; in the knowledge graph construction process, sequential hypothesis testing and e-process evidence accumulation mechanisms are introduced to statistically control the significance of entity recognition results, thereby improving the stability and reliability of knowledge modeling; meanwhile, a subgraph graph neural network model with relationship strength perception is constructed, a resource structure semantic representation conforming to teaching logic is obtained, and the resource structure semantic representation is fused with a text semantic representation, so that semantic retrieval and sorting of the knowledge graph are enhanced. The method can output retrieval results that are coherent in teaching logic and adapt to different teaching scenes, and improves the accuracy of education resource retrieval and the teaching auxiliary effect.

The present invention discloses a fine-grained entity classification method for a road and integrated utility tunnel standard knowledge graph based on prompt learning, comprising the following steps: S1: Collect data, perform knowledge modeling and data annotation; S2: Train a model according to the training data and optimize the parameters; S3: Decode the sample labels according to the paths in the label ontology structure. It can make full use of the hierarchical structure of the label ontology and the ability of prompt learning to solve the few-shot problem in the low-resource professional field scenario, making the prediction results of the model more accurate.

The invention discloses a freight unmanned aerial vehicle fault diagnosis method based on hybrid driving of a multi-relation fault knowledge graph and a large model thinking chain prompt, and the method comprises the following steps: constructing a freight unmanned aerial vehicle multi-relation fault knowledge representation, and determining the direct connection between entities and reasoning fault knowledge; entity recognition, relation extraction and knowledge fusion are carried out on the fault knowledge base under the knowledge representation category to complete construction of a multi-relation fault knowledge graph; obtaining a freight unmanned aerial vehicle fault problem, and adopting a large model thinking chain to prompt query; guiding the large model to generate a graph query language through a predefined thinking chain prompt template; and determining the logic relationship of the multi-relationship fault knowledge graph, and carrying out successive reasoning query to obtain an accurate fault reason and a corresponding solution. According to the method, through accurate knowledge modeling and logical reasoning questions and answers, the problems of expression and retrieval of rich relations hidden among fault knowledge are solved, and the strain capacity of a freight unmanned aerial vehicle system to complex factors is improved through rapid and accurate fault diagnosis.

The invention relates to the technical field of computer information processing, and provides an educational resource semantic retrieval method based on a knowledge graph. The method comprises the following steps: structuring course teaching materials, courseware, multimedia resources and experiment videos to construct a science education knowledge graph; in a knowledge graph construction process, sequential hypothesis testing and an e-process evidence accumulation mechanism are introduced, statistical significance control is performed on an entity recognition result, and the stability and reliability of knowledge modeling are improved; meanwhile, a sub-graph neural network model of relation strength perception is constructed, resource structure semantic representation conforming to teaching logic is obtained and fused with text semantic representation, and knowledge graph enhanced semantic retrieval and sorting are achieved. According to the method, the retrieval results which are coherent in teaching logic and adaptive to different teaching scenes can be output, and the accuracy of educational resource retrieval and the teaching assistance effect are improved.

The invention discloses a knowledge modeling and data acquisition system driven by multi-dimensional analysis. The knowledge modeling and data acquisition system comprises a multi-dimensional analysis engine module, a dynamic knowledge modeling module, a self-adaptive data acquisition module, a high-speed data bus and an Ethernet interface, the multi-dimensional analysis engine module is used for performing multi-dimensional feature extraction and relation strength quantification on the knowledge graph; the dynamic knowledge modeling module is bidirectionally connected with the multi-dimensional analysis engine module through a high-speed data bus, and is used for dynamically updating a relationship between knowledge Schema and an entity according to an analysis result; and the adaptive data acquisition module is in one-way connection with the dynamic knowledge modeling module through an Ethernet interface, and is used for generating an acquisition strategy according to the knowledge graph and executing data acquisition. According to the method, deep collaboration of knowledge modeling and data acquisition is realized through a multi-dimensional analysis driving mechanism, remarkable breakthroughs are made in the aspects of knowledge updating efficiency, data acquisition quality and cross-domain adaptability, and the knowledge updating period is shortened to day-level incremental updating / week-level mode optimization from the month-level mode optimization of the traditional technology.