46 results about "Knowledge modelling" patented technology

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 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 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 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 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 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.

The invention provides a knowledge graph construction method and system based on AI and an automated process robot, and the method comprises the steps: obtaining a health behavior sequence which comprises a behavior event tag and an index value; and extracting data with a preset window length from the health behavior sequence to form a health event unit. And obtaining a semantic center vector of the health event unit. And calculating an edge weight between each health event unit according to the window starting time, the semantic center vector and the low-frequency behavior proportion. The health event units with the edge weights larger than a preset edge weight threshold value are defined as candidate causal pairs, and causal scores of the candidate causal pairs are obtained according to the behavior event labels, the index values and the time interval of the window starting time. And defining the candidate causal pairs of which the causal scores are greater than a preset causal threshold as causal event pairs, constructing a graph triple according to the causal event pairs, and storing the graph triple into the medical knowledge graph. The medical knowledge graph with automatic acquisition, dynamic knowledge modeling and localized deployment is constructed.

The invention provides an R-KGCN emergency disposal method for the silt danger of a floating wing suspension door in the storm surge period of a super-huge tide gate. According to the method, a system framework covering knowledge modeling, reasoning definition and reasoning optimization is constructed on the basis of a relationship-enhanced knowledge graph convolutional network, and structured expression and intelligent reasoning of sediment disaster emergency disposal knowledge are realized. The method comprises the following main technical links: (1) constructing a knowledge graph mode layer and a data layer oriented to a sediment disaster scene, and establishing a'feature-event-disposal 'ternary model and a hierarchical relationship; (2) proposing a structured reasoning problem definition framework of an emergency processing task, and supporting modeling requirements of multi-source, multi-scale and multi-relation semantics; (3) designing an R-KGCN inference algorithm integrated with a semantic relationship enhancement module, and improving the accuracy and interpretability of inference; and (4) forming a knowledge reasoning and intelligent recommendation method oriented to sediment dangerous cases, and providing decision support and emergency response guarantee for operation scheduling of the oversize tide gate.

The invention relates to the field of artificial intelligence, knowledge maps and science and engineering logical reasoning, and provides a strict triple construction and reasoning method based on a science and engineering knowledge point logical network. The method comprises the steps of knowledge point input and unique identifier generation, logic connection line (triple) construction, Fog aggregation and hierarchical division, bridge cross-layer logic generation and hierarchical reasoning based on lowest common superior fog (LCAF). The system realizes multi-layer aggregation and dynamic adaptive adjustment of knowledge points through fog, establishes a cross-domain logic path through a bridge, and generates a verifiable optimal logic chain in combination with a graph theory algorithm. The method can be widely applied to the fields of scientific research knowledge modeling, artificial intelligence training, educational reasoning and interdisciplinary knowledge visualization, and has the advantages of being rigorous in structure, traceable in reasoning, high in expandability and the like.

This invention discloses a method for generating cross-knowledge point combined exam questions, comprising the following steps: decomposing each knowledge point data into atomic knowledge points; mapping each atomic knowledge point to a leaf node of an Abstract Syntax Tree (AST); calculating the comprehensive correlation between two leaf nodes; constructing composite nodes; determining the linguistic expression order of the corresponding leaf nodes based on the association logic labels; generating initial exam question information based on the linguistic expression order, the content of the corresponding leaf nodes, and the basic information corresponding to the knowledge point type; performing semantic verification on the initial exam question information; and outputting the initial exam question information that passes the verification to obtain the target exam question information. This invention uses an abstract syntax tree as a knowledge modeling and combination carrier to achieve the organic coupling of multiple independent knowledge points, ultimately generating comprehensive exam questions that cover multi-dimensional skills and conform to actual job logic. This invention also discloses a system, electronic device, and storage medium for implementing the above method.

The application discloses a policy knowledge graph construction method for infrastructure field, and relates to the technical field of knowledge graph, which comprises the following steps: S1, knowledge modeling is performed on the policy knowledge graph of the infrastructure field, and entity types, entity attributes and entity relationships are determined; S2, provincial and ministerial infrastructure policy provisions are collected and preprocessed to obtain an initial data set; S3, an initial keyword library of the infrastructure field is constructed, the initial keyword set and historical infrastructure policy texts are input into a large model, the initial keyword library is expanded, and then the infrastructure policy texts are classified; S4, the classified infrastructure policy texts are jointly extracted by using a pretraining language model to obtain structured knowledge; and S5, a Neo4j graph database is used to construct an infrastructure policy knowledge storage system, and the structured knowledge is stored in association. The method realizes automatic collection, structured analysis, associated mining and dynamic updating of multi-source policies, and provides accurate and timely policy knowledge services for enterprises.

The invention relates to the technical field of new energy power generation and operation and maintenance management, in particular to a new energy intelligent operation and maintenance resource configuration system suitable for multi-station cooperation, which is characterized in that equipment operation data, external environment data and an operation and maintenance resource real-time state are acquired through a multi-source data access and knowledge modeling layer, and a unified asset knowledge model is constructed. And the task generation and priority distribution module automatically generates operation and maintenance tasks and distributes initial priorities based on the equipment health state quantitative indexes and the operation and maintenance risk levels. The cross-station space-time diagram modeling module integrates positions and states of operation and maintenance resources, and constructs an association diagram model with space-time constraints and energy constraints as edge weights in combination with task nodes, resource nodes and energy nodes. The intelligent scheduling and optimization engine adopts a multi-target robust optimization and reinforcement learning algorithm, outputs a global optimization scheme including task allocation, a resource scheduling path and mobile energy storage vehicle energy supply planning under the condition of meeting security permission constraint conditions, and realizes efficient cooperation and intelligent scheduling of cross-station operation and maintenance resources.

The invention discloses a digital teacher knowledge modeling and content generation method based on artificial intelligence. The method comprises the following steps: constructing a teaching knowledge graph and forming a capability graph; generating a node vector, an edge vector and a path mode set; inputting the node vector, the edge vector and the path mode set into a Hopfield network, and executing pre-training; outputting a corresponding teaching knowledge path sequence and an association weight thereof based on teaching task information extracted from the teaching knowledge graph and the capability graph; generating a teaching content organization plan; according to the teaching content organization plan, generating structured teaching content including teaching explanation text, teaching example text, interactive practice tasks and evaluation question content; and performing increment adjustment on the Hopfield network memory state and the external memory of the neural Turing machine. The method integrates knowledge modeling, intelligent memory and content generation capabilities, and is suitable for intelligent education scenes.

The application discloses a domain knowledge graph autonomous modeling method and system, and the method comprises the following steps: acquiring unstructured text of a target domain to construct a text corpus, and acquiring a training data set from the text corpus; constructing an ontology model of the target domain according to the text corpus, labeling the training data set based on the ontology model to obtain a model training data set; fine-tuning and training a pre-constructed BERT model based on the model training data set to obtain a triple knowledge mining model of the target domain; loading a text sentence in the text corpus into the triple knowledge mining model for knowledge mining, and constructing a domain knowledge graph according to a result of the knowledge mining. The embodiment of the application abstracts domain multi-source heterogeneous data and text into a structured knowledge graph for knowledge modeling, provides reliable assistance for intelligent manufacturing of enterprise knowledge empowerment under the premise of improving the accuracy and efficiency of domain knowledge graph modeling, and can be widely applied to the technical field of data processing.

The application discloses a kind of intelligent legal consultation question and answer method based on knowledge graph, comprising the following steps: step 1: constructing super-relationship legal knowledge graph;Step 2: based on MAYPL, the super-relationship legal knowledge graph constructed in step 1 is carried out structure representation learning;Step 3: receiving the consultation question of user, extracting key legal entity, legal relationship, consultation intention and limited condition from consultation question, and mapping as graph query structure;Step 4: according to graph query structure, recall candidate answer in super-relationship legal knowledge graph;Step 5: according to the best candidate result, generate the legal consultation answer for user;Step 6: obtain the feedback result of question and answer, update super-relationship legal knowledge graph.The application realizes the intelligent question and answer processing for complex legal consultation scene, can more effectively express the limited condition and position relationship in legal fact, improve legal knowledge modeling precision, question and answer reasoning ability and result explainability.

The embodiment of the application provides a vertical numerical control honing machine module configuration method for carbon emission. The method comprises the following steps: obtaining product configuration information of a vertical numerical control honing machine; determining concepts, relationships between the concepts and configuration constraint information in the product configuration information; obtaining demand information of a customer; establishing a corresponding optimization configuration model according to the demand information, the concepts, the relationships between the concepts and the configuration constraint information; determining basic facts and rules in the optimization configuration model according to a preset rule inference machine; screening out a candidate instance set meeting the requirements of the customer according to the basic facts and the rules; in the candidate instance set, combining instance sets in different categories and meeting constraint conditions contained in the configuration constraint information to obtain a final configuration instance, and through the multi-layer configuration solving process of configuration knowledge modeling-candidate module reasoning-configuration optimization, the disadvantage of strong subjectivity is overcome, so that the simultaneous optimization of cost and carbon emission can be realized.

The present application relates to the technical field of electric digital data processing, and especially relates to a target activity rule intelligent analysis method based on sky net big data, comprising the following steps: constructing a knowledge graph and knowledge organization of a target based on sky net big data; acquiring real-time or non-real-time situation data of the target, and tracking and analyzing the activity situation of the target based on the knowledge graph and knowledge organization; analyzing the activity rule of the target based on the tracking analysis result of the activity situation, the knowledge graph and the knowledge organization, and generating and visually displaying a target portrait. Through the research on related technologies such as target knowledge modeling, knowledge organization, intelligent identification, situation tracking research and judgment, activity track information extraction, activity rule reasoning, target portrait generation and final target portrait visual display, the present application realizes the establishment of the correlation between target activities and space-time events from a large number of fragmented and static data, and the mining of certain activity rules.

The application provides an Internet of Things equipment after-sales inspection operation and maintenance large model system fused with RAG technology, relates to the technical field of data processing, and comprises an equipment state analysis module, an operation and maintenance knowledge modeling module, an abnormal state identification module, a combined fault reasoning module and a retrieval enhancement generation module; the equipment state analysis module is used for analyzing equipment operation data and constructing an equipment operation state sequence; the operation and maintenance knowledge modeling module is used for performing semantic processing on equipment manuals and historical operation and maintenance data and generating an operation and maintenance knowledge vector set; the abnormal state identification module is used for identifying abnormal information, constructing an abnormal state set and a corresponding fault feature vector; the combined fault reasoning module is used for performing correlation analysis based on multiple fault feature vectors and screening to generate a combined fault knowledge set; the retrieval enhancement generation module is used for generating an inspection operation and maintenance diagnosis result in combination with the equipment operation state sequence and the combined fault knowledge set; and the system improves the consistency of the diagnosis result and the actual equipment state.

The invention discloses a method for intelligently generating a test case based on a large model, and belongs to the technical field of test case generation, and the method comprises the following steps: pre-collecting an original document containing product knowledge, carrying out multi-modal analysis and knowledge modeling on the original document, and constructing a structured knowledge base; the method further comprises the steps that multi-modal analysis is conducted on a requirement document submitted to be tested, relevant knowledge is retrieved in a pre-built knowledge base based on an analysis result, and a test case is generated by combining the optimized cue word and utilizing a large model; and taking the generated test case as a query condition, performing similarity retrieval in a historical case library, and recommending a regression test case. According to the method, through structured knowledge base construction, accurate retrieval based on the knowledge base, cue word optimization generation and regression recommendation based on the newly generated use case, the problems of excessive generalization, low generation quality, insufficient coverage rate and the like of use case generation are solved.

The invention provides a fault self-healing method and system based on a business semantic map and a large language model, electronic equipment and a storage medium, and the method comprises the steps: converting enterprise unstructured business knowledge into a dynamic business semantic map through knowledge modeling, real-time perception, intelligent reasoning, automatic restoration and continuous evolution full-link closed loop; and fault self-healing is realized in combination with a large language model. The method comprises the following steps: constructing and updating a business semantic map, collecting multi-dimensional observable data of a production environment in real time and detecting anomalies, combining data and a map input model to output a root cause and a repair suggestion, automatically generating a repair request for a high-confidence fault, completing publishing after auditing, and reversely updating the map by a redisk report after the fault is recovered to form a self-evolution closed loop. According to the scheme, more than 80% of conventional fault recovery time is shortened to a minute level from several hours, MTTR is reduced, the fault risk is eliminated through precipitation organization level knowledge, manual intervention and regression risks are reduced, system intelligence is continuously improved, and production system stability and research and development efficiency are guaranteed.