99 results about "Relational knowledge" patented technology

A computer-implemented method of generating an answer from an input query and input documents, comprising extracting input query entities from the input query and input document entities from the input documents, sampling a schema of in-domain queries with the input query to generate a query sampled schema, generating an entity-document graph from the input documents and input document entities, generating a hyper-relational knowledge graph by extracting, for each input query entity, a document title and relation to an input document entity of the input document entities from the input documents in the entity-document graph, sampling the hyper-relational knowledge graph with the query sampled schema to generate a query focused hyper-relational knowledge graph, predicting an answer to the input query by inputting the query focused hyper-relational knowledge graph and input query into a pretrained neural network, outputting the answer.

The invention relates to the technical field of financial data analysis and artificial intelligence, in particular to a multi-source heterogeneous financial data fusion and intelligent analysis system which comprises a data standardization processing module, a time sequence event fusion module, a knowledge graph construction module, a relation reasoning module and a self-adaptive anomaly detection module. The data standardization processing module is used for converting heterogeneous financial data from different sources into unified tensor representation; the time sequence event fusion module adopts a double-clue cooperation mechanism to establish a mapping relation between continuous time sequence data and discrete events; the knowledge graph construction module extracts financial entities and relationships thereof, and constructs a multi-level knowledge graph; the relation reasoning module performs deep reasoning based on a graph attention mechanism; and the adaptive anomaly detection module dynamically adjusts the detection threshold according to the market environment. According to the system, implicit association in heterogeneous financial data can be deeply mined, market anomalies are recognized in advance, and comprehensive support is provided for financial decision making.

The invention provides a medical intelligent causal decision-making and scheduling system based on an improved large language model, and the system comprises a pre-training language model module which is used for processing unstructured medical text data, and extracting a text feature vector and a medical entity relationship; the knowledge graph enhancement module is used for constructing a medical knowledge graph based on the medical entity relationship, and reasoning to generate a knowledge enhancement feature vector; the multi-modal feature fusion module is used for generating a fusion feature vector based on the text feature vector and pre-stored structured data; the dynamic time sequence modeling module is used for generating a time sequence prediction vector based on the fusion feature vector and the knowledge enhancement feature vector; the self-adaptive scheduling optimization module is used for making a medical scheduling decision based on the time sequence prediction vector and generating a scheduling scheme; and the scheduling calibration module is used for matching similar case features for the scheduling scheme through a context learning mechanism based on the knowledge enhancement feature vector and calibrating the similar case features to generate an optimized scheduling scheme. And medical resources and scheduling can be allocated more accurately.

The invention discloses a collaborative recommendation method and device fusing lightweight relation path completion and dynamic negative sampling, equipment and a medium, and relates to the technical field of recommendation. The collaborative recommendation method comprises the following steps: acquiring historical interaction data of a user, and constructing an interaction relationship knowledge graph; vectorizing the interactive relationship knowledge graph, and then dynamically constructing a multi-hop relationship path based on a lightweight relationship path completion mechanism to obtain a path completion representation. According to the lightweight relation path completion mechanism, a path order L belongs to {1, 2, 3} is set according to the sparse degree of a user-article pair, and a first-order, second-order or third-order reasoning path is constructed. And then performing intelligent feature fusion on the constructed path through a triple pooling strategy. And projecting the embedded representation of the entity to the corresponding relation subspace through orthogonal projection based on the path completion representation. And calculating a recommendation score according to the embedded representation after projection, and obtaining an article recommendation list.

The invention discloses an unregistered geographical name discovery and spatial position reasoning method and a related device, and relates to the technical field of geographic information, and the method comprises the steps: constructing a geographical name space-time derivation relation knowledge graph based on an open source geographic database; inputting the target place name text data into a sequence labeling model to obtain a labeling result, and extracting candidate derived place names based on the labeling result; performing general name segmentation on the candidate derived geographical names to obtain potential basic geographical names, and judging whether the potential basic geographical names meet preset association conditions or not based on a geographical name space-time derivation relationship knowledge graph; if yes, eliminating potential basic place names; if not, determining the potential basic place name as an unlogged place name; and determining the spatial position of the unregistered place name according to the general name of the unregistered place name and the place name space-time derivation relationship knowledge graph. According to the invention, the automation degree of unregistered place name discovery and spatial position reasoning can be improved.

The invention discloses a defect hidden danger data diagnosis method based on a distribution network SVG single line diagram. The method comprises the steps that basic analysis is conducted on the input SVG single line diagram, basic geometric figure elements are extracted and normalized, a topological connection relation is preprocessed, and a multi-level index structure is constructed; a dynamic topology path reasoning mechanism is constructed, and reasoning and self-adaptive optimization of the topological relation of the electrical equipment are realized through path consistency calculation, topological entropy minimization and a dynamic topology updating strategy; a self-adaptive implicit relational knowledge graph is constructed, and semantic association between the electrical equipment is established through explicit topological relation fusion, implicit function dependence reasoning and dynamic semantic embedding optimization; and constructing a multi-modal time sequence feature vector, calculating the hidden danger risk of the equipment by adopting a space-time defect probability estimation model, and giving an alarm through a dynamic risk early warning strategy to realize prediction and early warning of the hidden danger of the defects of the electrical equipment. According to the method, the problems of semantic understanding, topological reasoning, state inference and defect prediction of the SVG single line diagram are effectively solved.

The invention relates to the technical field of drug safety evaluation, in particular to a multi-center combined evaluation method and system for drug safety based on a graph neural network and federated learning. Known and unknown drug interaction is systematically predicted based on a drug multi-relation knowledge graph and a graph neural network, a key path of DDI is identified through a graph attention mechanism, a molecular mechanism of the interaction is revealed, and natural language interpretation is generated. And meanwhile, through privacy protection and multi-center cooperation, a federal learning architecture is utilized to break data islands and improve the external effectiveness of an evaluation conclusion on the premise of protecting patient privacy and meeting data compliance requirements. The heterogeneity of data of different mechanisms is effectively evaluated through distribution deviation detection, deviation caused by blind extrapolation is avoided, a real-world evidence methodology report and a data traceability auditing clue are automatically generated, the requirement of a supervision mechanism for real-world evidence quality is met, medicine supervision decision is supported, and medicine research, development and review are accelerated.

The invention discloses an intelligent dialogue memory management method and system based on the logistics field, and relates to the technical field of logistics intelligent dialogues, and the method comprises the steps: obtaining the historical logistics dialogue data of a target user, generating a user feature-interactive entity-semantic relationship initial knowledge graph based on a QWen2.5-32B large language model, and constructing a Milvus semantic vector library; monitoring a logistics dialogue flow in real time, and starting an adaptive memory management mechanism by taking three rounds of dialogue as a judgment threshold value; extracting user dialogue content attribute features and behavior preferences, and dynamically updating a user feature-interactive entity-semantic relationship initial knowledge graph and a Milvus semantic vector library; and triggering a logistics dialogue according to a target user, starting a multi-modal memory recall mechanism, cooperatively retrieving a user feature-interactive entity-semantic relationship knowledge graph and a Milvus semantic vector library, generating a personalized intelligent dialogue response, and realizing a logistics intelligent dialogue memory management closed loop. The beneficial effect of the invention is that the intelligent and personalized capabilities of the system are enhanced.

The invention discloses a multi-modal semantic understanding method and system oriented to an unstructured PDF document, and relates to the related field of data processing.The method comprises the steps that a relational knowledge representation plan is called to analyze a target PDF document, a target relation framework is obtained, cross-modal alignment processing is conducted on the target relation framework, and a target alignment framework is obtained; performing multi-modal interaction analysis on the target alignment framework to obtain target fusion information; performing reconstruction processing on the target PDF document based on the target fusion information to obtain a target reconstruction document; and taking the semantic information of the target reconstructed document as multi-modal semantic understanding of the target PDF document. The technical problem that the semantic understanding precision is insufficient due to the fact that modal semantic association is missing and interaction is insufficient in existing unstructured PDF document-oriented multi-modal semantic understanding is solved, and the technical effect of improving the semantic understanding precision by integrating the multi-modal information in the document is achieved.

The invention discloses a multivariate time series anomaly detection method and system based on spatio-temporal knowledge. The method comprises the steps that specified sensor data or index data form multivariate time series data; periodic time sequence decomposition and causal discovery are carried out to construct a space-time knowledge graph, nodes in the space-time knowledge graph represent the state of each variable in specific time, directed edges represent the causal relationship between the variables, and the weights of the directed edges reflect the causal influence intensity; performing data preprocessing and feature extraction on the multivariate time sequence data to obtain sequence features; extracting space-time synchronous attention features from the space-time causal knowledge graph and the sequence features by using a space-time synchronous attention network; and performing anomaly detection based on the spatio-temporal synchronization attention features to determine whether the device or the network is abnormal or not. The invention aims to simultaneously model complex short-term and long-term space-time dependencies for multivariate time series anomaly detection and fuse prior causal relationship knowledge to improve the anomaly detection accuracy and reduce false alarms.

The invention provides an intelligent warehouse integrated management and control system for port logistics, and relates to the technical field of port warehouse management, and the system comprises a graph module which is configured to be used for constructing an operation relation knowledge graph based on the entity relation of a port warehouse, the nodes of the knowledge graph represent warehouse operation elements, and the edges of the knowledge graph represent the incidence relation between the warehouse operation elements; the analysis module is connected with the graph module and is configured to be used for monitoring the node state of each node in the knowledge graph and reasoning the potential influence of the abnormal node state on the global operation plan by analyzing the association relationship between the nodes when the abnormal node state is monitored; and the strategy generation module is connected with the analysis module and is configured to be used for generating a target coping strategy according to the potential influence and outputting a corresponding management and control instruction. According to the invention, the risk that local abnormity is not disposed in time and is evolved into global operation interruption is avoided, so that stable execution of a port logistics overall operation plan is guaranteed, and the throughput efficiency and operation toughness of a warehouse are improved.

The invention discloses an AI artificial intelligence-driven tax, accounting and auditing method and system, and relates to the technical field of tax and financial management, and the method comprises the following steps: S1, data collection and preprocessing; s2, performing iterative optimization on the xLSTM model through an improved Adame optimizer; s3, extracting a financial data articulation knowledge graph; s4, tracking an abnormal source by using the articulation knowledge graph; s5, generating a comprehensive regulation suggestion; and S6, generating a standardized audit report. According to the method, the problems of long manual calculation time, low tax risk prediction precision, auditing response lag and the like in a traditional financial management method are solved, and an efficient, accurate and real-time financial management technical solution is provided through intelligent automatic analysis.

The invention relates to a tin smelting process flow super-relation knowledge graph embedding representation method, and belongs to the technical field of knowledge representation. The method comprises the following steps: based on each stage of a tin smelting process flow, collecting and sorting a to-be-constructed tin smelting process flow field knowledge text; setting cue words extracted by the tin smelting process flow super-relation fact information; the large language model extracts a super-relation fact in the to-be-constructed domain knowledge text according to the cue word, and a super-relation knowledge graph is constructed; arranging the extracted super-relation facts as samples in a training set for training a representation model; and the final embedding of the tin smelting process flow knowledge graph is obtained through the trained characterization model and is used for downstream tasks. According to the method, knowledge-based expression of the tin smelting process flow is realized, and digital and intelligent development of the tin smelting industry is promoted.

The embodiment of the invention discloses a key anaphora analysis method and device in voice-to-text conversion, a medium and a product. The method comprises the following steps: constructing an interpersonal relationship knowledge graph of a user and splitting according to an interpersonal relationship type; when the text sequence of the user voice conversion contains the third-person pronouns and meets a preset condition, acquiring context information and acquiring anaphora objects of the third-person pronouns, a relationship type between the anaphora objects and the user and the confidence coefficient of the relationship type; sorting and traversing the relation sub-atlases based on the relation type and the confidence coefficient to obtain candidate relation sub-atlases; and when the reference object is successfully matched with any one of the sub-nodes in the candidate relation sub-graph and the first annotation fields of the sub-nodes, acquiring a character sequence containing a correct third-person pronoun based on the second annotation fields of the sub-nodes. According to the method, the anaphora objects of the third-person pronouns are obtained through semantic analysis, and the anaphora objects are matched in a multi-dimensional mode in combination with the interpersonal relationship knowledge graph so as to obtain the correct third-person pronouns.

The invention provides an identification analysis data governance and association method and system based on an AI large model, and the method comprises the steps: carrying out the pre-classification of a new registration identification through an identification analysis registration module in the AI large model, and determining the industry type of the new registration identification; using a template industry attribute matching module in the AI large model to perform template classification on the new registration identifier, and determining a template category to which the new registration identifier belongs; performing identification analysis on the industry category and the template category by utilizing the inter-enterprise relationship knowledge graph, and mining enterprise information involved in identification analysis data; based on the preset association preconditions, causal association and process association between enterprise information are established, and through automatic identification analysis classification and identification, the association relationship between different identification analyses is automatically mined, so that the treatment level and value mining capability of identification analysis data are effectively improved; and powerful technical support is provided for intelligent development of an industrial internet identification analysis system.

The invention discloses a causal relationship knowledge graph construction method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting input data which comprises topological structure data, performance index data, event log data and external environment data of a network; constructing a causal relationship knowledge graph between network entities based on the input data; generating potential causal relationship candidate pairs of the causal relationship knowledge graph by using a causal discovery algorithm; and optimizing the causal relationship knowledge graph on the basis of the causal relationship candidates to obtain an optimal causal relationship knowledge graph, thereby being beneficial to solving the problem that in the prior art, in large-scale wireless network diagnosis, the causal relationship between network entities is difficult to accurately and efficiently excavate, so that the causal information cannot be effectively utilized to perform network fault diagnosis.

The invention discloses a data insight report generation method and system based on knowledge enhancement and fact verification. Building a domain index relation knowledge graph by training a large language model; mapping the insights to a map, constructing a weighted insights map, carrying out spectral clustering, and dividing insights theme areas; a viewpoint unit is generated through the writing agent, and consistency verification and correction are carried out through the fact verification agent; the user intention is analyzed, candidate topics are screened, and a report outline is generated through heuristic search arrangement based on SRCI scoring, semantic progression and logic entropy calculation; finally, the verified viewpoint units are integrated according to the outline, and a final report is output. According to the method, the full-automatic and high-credibility report generation is realized, and the accuracy, logicality and interpretability are improved.

The invention provides an enterprise supply chain financial risk prediction method fusing a dynamic knowledge graph and a graph neural network. The method comprises the steps of multi-source data loading and preprocessing, supply chain knowledge graph construction, financial feature extraction, risk label generation and the like. Aiming at the problems that multi-subject information of core enterprises, suppliers, customers and the like in supply chain data is dispersed and is inconsistent with independent financial data structures, the method solves the problems that manual integration is low in efficiency and prone to errors through automatic data cleaning, entity matching and relation mapping, and a unified and structured enterprise supply chain relation knowledge graph is constructed; the limitation of manually designing network features is overcome by utilizing a graph attention network; an effective feature fusion strategy is designed, and joint feature representation with higher discriminative force is formed; and constructing an end-to-end evaluation framework, training classifiers such as a support vector machine to learn the fusion features, realizing accurate and stable classification of high-risk and low-risk enterprises, and providing probability output to enhance result interpretability and decision support capability.

The invention relates to a cross-language news core event analysis method based on graph knowledge distillation, and belongs to the technical field of natural language processing. Due to scarcity of news core event analysis annotation data in a low-resource language scene, discourse news event global features and inter-event association features are difficult to mine and model. Aiming at the problem, a cross-language news core event analysis method based on graph knowledge distillation is provided; according to the method, an event hypergraph with event elements as vertexes is constructed to represent event global features, and an event line graph with events as vertexes is constructed to represent correlation features between the events; and event feature knowledge and relation knowledge in a rich resource language are migrated to a low resource language by utilizing cross-language graph knowledge distillation, so that the news core event analysis effect of the low resource language is improved. Experimental results show that the method can effectively mine and model global features of chapter news events and correlation features between the events, and effectively relieve the problem of scarcity of news core event analysis annotation data in a low-resource language scene.

Disclosed are a method and system for constructing a multivariate relational knowledge base. The method includes: constructing a multivariate relational model for tuples in a multivariate relational knowledge base, and establishing a corresponding scoring function and loss function; training the multivariate relational model based on the scoring function and loss function using training samples obtained from the multivariate relational knowledge base; predicting and completing missing entities in tuples with missing entities in the multivariate relational knowledge base based on the trained multivariate relational model, and constructing a complete multivariate relational knowledge base based on each tuple with completed missing entities.

The invention discloses a recognition method for a target weak part under a complex background fused with a cognitive map, and the method comprises the steps: obtaining a to-be-recognized input image, inputting the to-be-recognized input image into a trained target weak part recognition model based on visual perception, carrying out the auxiliary reasoning through combining with a spatial position relation knowledge map of the target weak part, and carrying out the recognition of the target weak part. Obtaining a target weak part identification result; wherein the target weak part identification model comprises a backbone network, a multi-scale sensing network and a candidate region generation network; the multi-scale sensing network is used for fusing the multi-scale feature maps extracted by the backbone network to obtain a fused feature map; the candidate region generation network is used for generating a target candidate region according to the fusion feature map so as to realize classification of target weak parts, and a label distribution strategy based on Gaussian distribution measurement is set so as to improve the positive sample preset anchor frame distribution number of the target; and a target weak part identification multi-task loss function is set, and training of an identification model is realized.

The present invention discloses a quantitative source tracing method for river and lake water pollution that combines knowledge graphs and machine learning. The present invention relates to the technical field of water pollution source tracing. The present invention collects and processes data from target river sections, constructs a hydrodynamic-water quality model, and based on the outlet location of the target river section, obtains the pollutant diffusion characteristics of the downstream river channel and the concentration time series characteristics of the target section under different discharge scenarios of each outlet through unit pulse response testing, constructs a "source strength-time-concentration" relationship knowledge graph, randomly extracts a sample set from the graph, uses a machine learning method to train the sample set, learns the nonlinear mapping relationship between the downstream section concentration time series and the source strength of multiple outlets, performs dynamic inversion of the river pollutant diffusion process, realizes the rapid positioning of pollution sources and quantification of their contributions, and finally uses the Monte Carlo sampling method to generate a probability distribution of the pollution source location, which helps to improve the priority of source tracing judgment, thereby improving the accuracy and response speed of river water pollution source tracing.

The invention discloses a medical image super-resolution reconstruction method based on incremental learning, and aims to solve the problem that a super-resolution reconstruction network is easy to have disastrous forgetting during incremental learning of a multi-modal medical image through a cooperative strategy of adaptive memory buffer management, relational knowledge distillation and dynamic gradient equilibrium. And efficient learning of the new mode and stable retention of the reconstruction capability of the old mode are realized. According to the method, adaptive gradient weighted difficulty scoring is combined with farthest point sampling to efficiently screen playback samples, a characteristic space geometrical relationship is maintained by utilizing relational knowledge distillation, and conflicts are monitored in real time and weights are automatically adjusted through a dynamic gradient balance mechanism. According to the method, the disastrous forgetting degree is remarkably reduced, a single model can learn multiple modes in sequence and continuously keep the super-resolution reconstruction capacity for all the learned modes, and the method can be widely applied to multi-mode medical image super-resolution reconstruction tasks such as MRI and chest X-rays and has high clinical application value.

The invention discloses an epilepsy medical knowledge graph construction method and system based on rhodic acid C, and relates to the technical field of knowledge graph construction. The method comprises the following steps: acquiring initial medical text information; preprocessing the initial medical text information to obtain non-standard medical text information; performing term conversion on the non-standard medical text information according to a medical term relationship knowledge base to obtain standardized medical text information; extracting medical information related to a mode of action of a rhodic acid C molecule and epilepsy diagnosis and treatment from the standardized medical text information; and adding the medical information into a knowledge graph database. According to the method, the non-standard medical text information can be converted into the standardized medical text information in combination with the medical term relationship knowledge base, so that the knowledge graph is constructed, and the data recognition accuracy and the knowledge graph quality are improved.

The invention provides a family member relationship extraction and reasoning method based on LLM and a knowledge graph, which overcomes the defects of difficulty in family relationship data acquisition, high cost, low automation degree, incomplete relationship extraction, lack of reasoning ability and the like, and can efficiently and completely extract and infer family member relationships from massive unstructured texts. The method comprises the following steps: 1, extracting preliminary structured data based on optimization anaphora resolution: selecting an original data set; determining the range of family members; identifying a family relationship using syntactic dependency analysis; analyzing a family member relationship by using anaphora resolution; 2, enhancing the structured data based on fine-tuning LLM and RAG technologies: performing fine-tuning on an LLM model; extracting the structured data in combination with the RAG; step 3, constructing a family member relationship knowledge graph: defining nodes and edges; converting and importing data; step 4, reasoning by using the knowledge graph: making a reasoning rule; the knowledge graph applies inference rules.

The invention discloses a knowledge hypergraph-based power system query method and related equipment, and the method comprises the steps: obtaining a description document and a data file of a power system, and generating a power system knowledge graph; analyzing each independent data card in the data file, converting each independent data card into a corresponding hyperedge, connecting the hyperedge with all entity nodes related to the corresponding card to form a power system multivariate relation knowledge hypergraph, and converting the hypergraph into a power system bipartite graph for storage; when a query instruction is received, entity nodes matched with key entities in the query instruction and hyperedge nodes related to semantics are retrieved in the bipartite graph of the power system, and after a context subgraph is constructed through bidirectional expansion, a language model is input to generate a query feedback result. The problems that a traditional mode is poor in expansibility, information is lost, retrieval is low in efficiency and answers are fuzzy are solved, and construction efficiency, association integrity, retrieval performance and query accuracy are greatly improved.

Causal discovery is performed using knowledge graph link prediction. Information from a causal network is transformed into a causal knowledge graph according to a mapping, the causal knowledge graph including a plurality of causal links, wherein each causal link includes a cause entity, a causal relation, and an effect entity, with the potential for a mediator. The causal knowledge graph is converted into embeddings, where the embeddings include a latent vector space representation of the causal knowledge graph. The embeddings are trained using a subset of the causal links of the causal knowledge graph. The embeddings are used for causal discovery to predict additional causal links of the causal knowledge graph.

The invention discloses a medicine research and development industry knowledge base system and method based on artificial intelligence, and belongs to the technical field of medicine information processing and artificial intelligence. The system comprises a data acquisition module, a data preprocessing module, a knowledge extraction module, a knowledge integration module, a knowledge reasoning module, a knowledge updating module and an intelligent center layer. The data acquisition module acquires multi-modal medicine research and development data, the preprocessing module performs cleaning, standardization and feature alignment, the knowledge extraction module adopts a deep learning model based on Transformer to extract entity relationships, the knowledge integration module checks conflicts through a chemical rule knowledge base and constructs a knowledge graph, and the knowledge graph is used for establishing a knowledge database. The knowledge reasoning module performs association prediction by using a graph neural network and a time sequence model, the knowledge updating module realizes parameter optimization and incremental updating based on human feedback reinforcement learning, and the intelligent central layer overall plans all the modules to work cooperatively based on a large language model arrangement framework. According to the method, multi-source heterogeneous data can be integrated, domain semantics can be deeply understood, a knowledge structure is dynamically evolved, intelligent decision support is provided, and the efficiency and accuracy of medicine research and development knowledge management and application are remarkably improved.

The invention relates to the technical field of health care knowledge question answering, in particular to a historical health care knowledge question answering method and device based on knowledge graph and large-scale language model fusion, and the device comprises a knowledge graph module, a question processing and retrieval module and a large-scale language model module; the knowledge graph module is used for storing entities and relation knowledge in a historical health-care field and binding time attributes and literature sources; the question processing and retrieval module is used for analyzing user questions and retrieving knowledge entries related to the questions in the knowledge graph module to form an evidence chain; and the large language model module is used for generating an answer result based on the user question and the evidence chain. The method has the advantages that the problems that'illusion 'easily occurs in a general model, answers are difficult to verify, knowledge is difficult to dynamically update and the like in a historical health and care context are effectively solved, and the method can be widely applied to scenes such as health and care policy research, long passport photo-care service knowledge base construction, educational training and academic retrieval and the like.

The invention discloses an elevator inspection clause intelligent matching method and system based on a knowledge graph and a medium, and belongs to the technical field of elevator inspection. The method comprises the following steps: a part and clause digital mapping stage: establishing a mapping relation between elevator parts and inspection clauses; a knowledge graph construction stage: associating the elevator parts, the inspection terms and the detection tools corresponding to the inspection terms to obtain a knowledge graph; in the dynamic rule engine configuration stage, checking terms associated with elevator parts are inquired through a knowledge graph, and a Drools rule file is automatically generated; in the inspection process automatic matching stage, the AR equipment is used for conducting three-level intelligent guidance to complete inspection, and an inspection result is obtained; and in the test result dynamic feedback and learning stage, the test result is recorded, incremental updating is performed on the knowledge graph, and the rule engine is optimized. According to the method, the inspection efficiency is remarkably improved by fusing the knowledge graph, the dynamic rule engine, the machine vision, the AR interaction and the block chain evidence storage technology.