120 results about "Knowledge building" patented technology

The invention provides a multi-agent cooperative processing system and method for multi-scene legal complaint consultation, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a legal or complaint consultation text inputted by a user, and generating structured consultation request data; automatically generating an evidence obtaining template, and forming a standardized evidence obtaining data packet; constructing a demonstration tree and outputting a multi-scheme decision matrix; performing multi-objective optimization by combining the success rate, the timeliness, the cost and the regional qualification index, and outputting a comprehensive recommendation result; generating a knowledge unit set, and updating a regulation index and agent prompt template library; predicting a re-complaint risk and generating a remedy and optimization suggestion; and outputting review result data, and reversely updating the review result data to the agent configuration strategy in the step of the knowledge construction module. According to the method and the device, the technical targets of multi-agent cooperative processing and dynamic optimization management in the whole legal complaint consultation process can be realized, and the technical effects of improving consultation processing efficiency, enhancing scheme recommendation accuracy, reducing manual participation cost and ensuring data compliance and risk controllability are achieved.

The invention discloses an equipment diagnosis and maintenance method and system based on a large language model, a terminal and a storage medium. The method comprises the steps that multi-source heterogeneous data of target equipment is collected and preprocessed; constructing and continuously updating a historical case vector database containing equipment knowledge; state data of target equipment are monitored in real time, after fault diagnosis is triggered, the most relevant knowledge fragment is retrieved from the historical case vector database according to real-time abnormal data, and a diagnosis context is constructed; inputting the diagnosis context into a large language model for reasoning, and outputting a fault diagnosis report and a maintenance scheme; the fault diagnosis report and the maintenance scheme are presented to a user, and user feedback is collected to optimize the system. According to the method, real-time data, historical records and structured knowledge of equipment can be automatically integrated, a dynamic knowledge base is constructed, accurate and efficient diagnosis of equipment faults is realized by utilizing the powerful reasoning capability of a large language model, an executable maintenance scheme is automatically generated, and the maintenance efficiency is improved.

The invention discloses a knowledge graph generation method based on multi-source data integration, and relates to the technical field of knowledge graph generation. The method comprises the following steps: collecting multi-format data, and performing cleaning, standardization and desensitization preprocessing to ensure that the data is integrated; using the fusion model to extract entities and relationships, and adapting to multi-source data types; similarity is calculated in combination with multi-dimensional features, and entity alignment disambiguation is achieved; carrying out weighted fusion on multi-source knowledge to construct a triple, and processing relation conflicts; evaluating the quality of the atlas through multiple indexes; triggering conditions are set, incremental updating and version management are adopted, and dynamic iteration of the atlas is guaranteed. According to the method, the multi-source data preprocessing quality is improved, the entity recognition and alignment precision is enhanced, relation conflicts are solved, and a high-quality time sequence knowledge graph is constructed; incremental updating is efficient and energy-saving, version management is traceable, and multi-field dynamic application requirements are met.

The invention discloses a multi-priori knowledge fused cerebral apoplexy rehabilitation map convolutional network evaluation method, and belongs to the technical field of cerebral apoplexy rehabilitation evaluation. Firstly, a high-precision prior information matrix is automatically generated through a collaborative and causal relationship automatic reasoning method based on Riemannian manifold geometry and transfer entropy, the problem that in the prior art, engineering depends on artificial features is effectively solved, and interpretable physical prior guidance is provided for a model. Then, through a multi-relation graph construction and attention weighting multi-modal adaptive fusion method, spatio-temporal features and priori knowledge are adaptively fused, an optimized graph structure is constructed, and the representation ability and interpretability of the model to complex joint interaction are improved. And finally, through a space-time diagram convolutional network guided by prior information and a comparative learning collaborative optimization method, the generalization ability and evaluation precision of the model in a small sample scene are remarkably improved through data enhancement and loss function optimization.

The invention discloses an intelligent optimization system for enterprise resource planning, and relates to the technical field of enterprise resource management, and the system comprises a data collection and preprocessing layer, a knowledge construction and reasoning layer, a risk early warning and decision support layer, and a resource scheduling and execution layer. According to the invention, a knowledge graph covering various resources of an enterprise and association relationships thereof is constructed by using preprocessed data through a knowledge construction and reasoning layer, a causal relationship network among the data is established, and meanwhile, an intervention effect evaluation unit combines an anti-factual reasoning result of a causal reasoning unit to evaluate the intervention effect of the enterprise. Quantitative evaluation is carried out on different resource pre-allocation intervention schemes, and a detailed evaluation report is output, so that an enterprise can perceive and take corresponding measures in advance before a risk occurs, and can select an optimal scheme according to a scientific evaluation result when a resource planning decision is made, thereby effectively ensuring stable operation of enterprise resources, and improving the resource planning efficiency. And the operation risk caused by the resource problem is reduced.

The invention discloses an electrical equipment defect identification method and system based on large model tuning, and the method comprises the steps: obtaining multi-modal defect data, and carrying out the defect identification through combining a domain knowledge graph tuning large model; and predicting an evolution trend based on an identification result, and providing a maintenance decision. The system comprises a multi-modal sensing module, a knowledge construction module, a large model tuning module, a defect identification module, an evolution prediction module and a decision support module. According to the method, the completeness, accuracy and generalization ability of defect identification can be improved, evolution prediction is realized, an intelligent prospective maintenance decision is provided, the efficiency is improved, and the cost is reduced.

The invention provides a life cycle evaluation automatic modeling method based on a large language model, and relates to the technical field of life cycle evaluation, and the method comprises the steps: extracting the multi-modal information of texts, tables, images and the like from the multi-source data of academic literatures, reports and the like, constructing an instruction data set in combination with LCA process knowledge, carrying out the instruction fine tuning of a base large model, and carrying out the automatic modeling of the life cycle evaluation. And obtaining the LCA field large language model. And generating a standardized prompt by utilizing a prompt word optimizer, driving a model to generate a product LCA model framework, filling list data, and perfecting process data in combination with semantic search and a correction mechanism. And intelligent connection recommendation among unit processes is realized by calculating the semantic similarity of input and output streams. And finally, the verified structured data is output to LCA modeling software, an automatic modeling process is completed, and the modeling efficiency and normalization are improved.

The invention discloses a knowledge intelligent analysis and question-answering method and system based on time sequence knowledge graph retrieval enhancement, relates to the technical field of natural language processing, and aims to solve the problem that a wrong answer is generated due to the fact that a model possibly retrieves semantic-related but time-inconsistent information due to a time-insensitive retrieval mechanism in an existing method. According to the method, a dynamic time sequence knowledge graph modeling and hierarchical interactive retrieval mechanism is introduced. According to the framework, entities and relationships are bound with specific timestamps through time annotation in a knowledge construction stage, and a dynamic graph capable of evolving along with time is formed; in a retrieval stage, through time query decomposition (decomposing a multi-time constraint complex query into single-time sub-queries, and gradually screening related knowledge by a three-layer interactive retriever (time sequence sub-graph retrieval, node-level retrieval and knowledge-level retrieval), time conflicts and redundant interference are reduced, and finally answers with consistent contexts and accurate time are generated.

The invention discloses a question answering system and a construction method for a motor vehicle driving teaching knowledge graph, and aims to overcome the defects of knowledge coverage integrity, scene adaptability, reasoning depth and interaction naturalness in the prior art. A traffic regulation document, coach voice and short videos and vehicle-mounted real-time data are integrated through a data acquisition module to cover rules and experience knowledge, a knowledge construction module constructs a space-time dynamic knowledge graph containing static and dynamic nodes based on Dgraph, space-time association is achieved by means of GeoHash coding, real-time updating is completed through an MQTT protocol, and the time-space dynamic knowledge graph is stored through a storage module. The intelligent reasoning module calls a DoWhy model to construct an'operation, principle and result 'causal chain in combination with a physical formula so as to output deep interpretation, and the interaction module achieves multi-mode immersion teaching through speech synthesis, gesture recognition and 3D animation, dynamically optimizes knowledge pushing according to student operation data, and achieves multi-mode teaching. The comprehensiveness, the real-time performance, the scientificity and the interaction naturalness of driving teaching are improved, and the method is suitable for intelligent driving teaching scenes.

The invention discloses a multi-dimensional capability construction knowledge tracking method based on learning behavior decoupling, and belongs to the technical field of knowledge tracking. The method comprises the steps of obtaining test question embedding and test question-response embedding, and obtaining learning deviation behavior characterization and multi-scale behavior characterization based on the test question-response embedding; inputting the learning deviation behavior representation and the multi-scale behavior representation into a binary state model to obtain a knowledge construction representation; obtaining a test question sequence structure representation based on test question embedding, and performing multi-scale association on the test question sequence structure representation to obtain a multi-scale test question association representation; the multi-scale test question association representation and the knowledge construction representation are subjected to student ability modeling through frequency domain perception fusion, and differential learning ability is obtained; and predicting the student test question performance based on the differentiated learning ability to obtain a student test question performance prediction value. The method improves the accuracy of student test question performance prediction.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

The invention discloses a thermal power plant intelligent question and answer method based on multi-source corpus fusion, and aims to solve the problems that knowledge construction depends on manual annotation, the corpus format is single, the context understanding ability is insufficient and the credibility of a generated result cannot be guaranteed in an existing question and answer method. The method comprises the following steps: introducing a vectorization processing mechanism and a semantic block model generation module to perform unified embedding and compression expression on various types of texts such as thermal power plant operation regulations, equipment manual, operation records and the like, so as to realize accurate retrieval and structured answering based on semantic correlation; and an answer judgment mechanism is introduced to reduce the risk that a language model generates an illusion phenomenon, so that the accuracy and interpretability of question and answer output are improved, and the intelligent question and answer requirements of operators in the aspects of equipment diagnosis, regulation query, accident handling and the like are met.

The invention relates to the technical field, and particularly discloses a big-data-based question and answer information processing method for AI online education, which comprises the following steps: S1, constructing a subject knowledge graph, taking concept nouns as large nodes, taking question solving methods as small nodes, establishing a derivation relationship between concepts, and forming a structured knowledge base; s2, processing questions of the user, identifying question knowledge points by extracting subject keywords, positioning activation nodes in the knowledge graph, and determining a derivation path; and S3, generating an answer text, generating a connection text according to a node sequence, and performing deviation detection and correction through real-time semantic vector comparison to ensure the answer accuracy and logic continuity. The whole process realizes a complete closed loop from knowledge construction to intelligent question and answer.

The invention discloses a multi-source document management method and device based on knowledge construction and fusion storage, and relates to the technical field of document management, and the method comprises the steps: receiving a multi-source document to an object storage system, and recognizing the document type; performing document analysis and structure extraction according to the document type; extracting pictures in the image-text mixed content, uploading the pictures to an object storage system, generating a mapping dictionary, and inserting picture marks in a text part of the mapping dictionary; carrying out structured processing on contents of the table key value pairs to extract summaries and abstracts; performing standardization processing and semantic slicing on the processed image-text mixed content, table key value pair content and / or plain text content to generate knowledge fragments; constructing a knowledge extracting questions from knowledge fragments; the knowledge fragments are stored in a first index, and the questions and IDs of the corresponding knowledge fragments are stored in a second index; generating vectors for each knowledge fragment and question, embedding and writing the vectors into a vector database; and performing document management based on the knowledge base. The document management efficiency is improved.

The invention discloses a vehicle refitting scheme generation method and system based on a large model and a knowledge graph, and relates to the technical field of electric vehicle networking, and the method comprises the steps: inputting an obtained user refitting demand text into a cloud large model, analyzing the refitting demand text, extracting key information, converting the key information into a query vector, and sending the query vector to a cloud server; the method comprises the following steps: establishing a vehicle refitting knowledge graph, performing matching retrieval with entity embedding in the commercial vehicle refitting knowledge graph, constructing cue words according to the retrieved knowledge, generating a vehicle refitting scheme through cloud big model thinking chain reasoning, calling a 3D component to render a scheme effect graph, and feeding back the scheme effect graph to the local; vehicle data, accessory data, scene data, law and regulation data and historical modification case data are obtained in advance, after data preprocessing, entities and relationships are obtained through named entity recognition and relationship extraction, a commercial vehicle modification knowledge graph is constructed, and entity embedding in the knowledge graph is obtained through graph neural network learning. According to the invention, a refitting scheme which better meets user requirements can be obtained.

The invention discloses a knowledge base construction method and system based on urban planning implicit knowledge deduction, relates to the technical field of urban planning, and aims to solve the problems that an existing planning knowledge base is insufficient in implicit knowledge mining, poor in scene adaptability and lack of dynamic optimization capability. The method comprises the steps of obtaining multi-source data and performing standardization processing to obtain an enhanced data pool; optimizing the large language model to extract explicit and implicit planning knowledge; constructing a planning knowledge association network with association strength; building an initial knowledge base containing classification, retrieval and implicit knowledge deduction units; generating an adaptability evaluation result through a multi-scene test; constructing a knowledge evolution rule based on an evaluation result and completing knowledge base iterative reconstruction; and establishing a real-time spatial data dynamic triggering mechanism to realize continuous optimization of the knowledge base. According to the method, accurate association and deduction of explicit and implicit knowledge are realized, the scene adaptability and dynamic updating capability of the knowledge base are improved, scientific and efficient knowledge support is provided for urban planning decision making, and the method is suitable for various planning scenes.

The invention discloses a financial field knowledge base construction method, device and equipment. The method comprises the following steps: firstly, identifying a text region, a table region and an image region from a target image; then extracting text data of the text region by using an OCR algorithm, and extracting semantic features and visual features of the image region and a second entity set and an entity type corresponding to the table region by using a multi-modal AI model; carrying out illusion detection by utilizing the similarity of the two entity sets, and when judging that the illusion of the AI model does not trigger a fault-tolerant mechanism by utilizing a detection result, carrying out intelligent alignment processing on the two entity sets, and screening out an optimal entity matching pair; carrying out formatting verification on the entity type; and then context knowledge association processing is carried out on text data corresponding to the text region, semantic features and visual features corresponding to the image region and entity matching pairs passing verification, and a more stable and accurate knowledge base is constructed by utilizing the obtained graph structure document to represent corresponding structured knowledge.

The invention discloses a power grid channel selection thematic spatio-temporal knowledge graph construction method and device and a power grid channel screening method and device, and the method comprises the steps: obtaining data information and expert knowledge related to power grid channels, constructing a power grid channel selection digital base based on the data information and the expert knowledge, and selecting a power grid channel selection digital base based on the power grid channel selection digital base. And constructing a power grid channel selection thematic spatio-temporal knowledge graph for power grid channel selection. Through the method and device provided by the embodiment of the invention, informatization expression, unified management query, efficient screening and intelligent reasoning classification analysis of power grid channel selection data can be realized, power grid channel planning and construction are supported, and compared with manual screening, the data screening efficiency is improved, the error rate is reduced, and the power grid channel selection efficiency is improved. And the labor intensity of engineers and decision makers is greatly reduced.

The invention relates to the technical field of earth space information, in particular to a super-resolution land coverage mapping method coupled with a large language model knowledge base. Comprising the following steps: acquiring a remote sensing image and geoscience text data of a target area and constructing a geoscience knowledge system; mining geoscience knowledge from the geoscience text data based on a large language model, constructing a structured geoscience knowledge base, and further generating a geoscience knowledge graph; obtaining a deep feature graph of the remote sensing image based on a convolutional neural network, and inputting a knowledge graph into a graph attention network to extract a regional geoscience semantic embedding vector; realizing geoscience semantic embedding vector and deep feature map fusion based on a global-local attention fusion mechanism; and performing up-sampling on the fused feature map to obtain a high-resolution depth feature map, and generating a super-resolution land coverage map of the remote sensing image by adopting a nonlinear activation function. According to the method, dynamic modeling and computable expression of regional global knowledge can be realized, and the problems of surface feature boundary fuzziness and category confusion are remarkably improved.

The invention relates to a deep learning-based breast cancer rehabilitation question and answer method, apparatus and device, and a medium. According to the method, patient demands are accurately captured through multi-modal feature analysis to generate knowledge demand vectors, and medical knowledge sub-graphs and general knowledge fragments are retrieved in parallel to form a knowledge basis; based on demand vector parameter dynamic weighting fusion of the two types of knowledge, a collaborative knowledge unit is constructed, and then importance distribution is performed on the medical knowledge by using a graph attention neural network to generate attention distribution; semantic integration is performed in combination with attention distribution and general knowledge to form fusion representation, and conflicts are arbitrated through a rule engine to ensure safety and reliability; finally, the conflict-free reasoning path and the patient demand vector are combined to generate professional, accurate and living personalized rehabilitation guidance, and seamless cooperation of medical preciseness and life practicability is achieved.

This invention discloses an intelligent detection method for power monitoring data security risks that integrates knowledge graphs. The method includes: a data acquisition layer that integrates and collects raw power data streams and sends them to a preprocessing layer; a preprocessing layer that standardizes the data, extracts features from the data, groups and associates the data according to the features, and performs a preliminary evaluation; a knowledge construction layer that receives standardized event streams, power equipment features, and abnormal data tags, and updates the knowledge graph in real time based on the above data; an intelligent detection layer that queries the graph display results to ensure reliable graph display, detects threat events and issues warnings, and simultaneously performs risk quantification scoring on threat events, analyzes the risk composition, and determines whether the detection is genuine; and a decision response layer that receives threat reports, integrates and sorts the reports, and predicts subsequent threats. This method solves the technical problems of fragmented power monitoring detection, delayed response, and isolated protection in existing technologies.

The application discloses a feature and knowledge driven optimal parallelism prediction method, system and medium, the method comprises the following steps: obtaining the multivariate runtime features of a target parallel program and embedding and coding to obtain multivariate fusion features; inputting the multivariate fusion features into a pre-trained speedup ratio prediction model to predict the speedup ratios of the target parallel program under a plurality of given parallelisms, the speedup ratio prediction model is a neural network prediction model, and a mapping relationship between the multivariate fusion features of the parallel program and the speedup ratios under the plurality of given parallelisms is established by training the training sample of the multivariate fusion features of the parallel program sample and the speedup ratio prior knowledge under different parallelisms; selecting the parallelism with the maximum speedup ratio as the optimal parallelism obtained by prediction. The application aims to realize efficient, accurate and reliable prediction of the optimal parallelism of the program, improve the computing efficiency, reduce the system resource consumption and reduce the execution time of the program.

This invention discloses a method and system for automatic extraction and knowledge construction of data quality rules for power equipment, belonging to the fields of artificial intelligence and power data governance technology. To address the problems of low parsing accuracy of complex tables in power technology standard documents, lack of physical constraint verification of extraction results, and catastrophic forgetting of models due to standard updates, this invention utilizes a multimodal large language model to parse documents. A structured document object model is obtained through hierarchical table structure encoding, cross-page context preservation, and semantic binding of table annotations. The large language model, fine-tuned with domain knowledge, extracts entity relationships and performs consistency verification in conjunction with a physical constraint knowledge base in the power domain. The verified entity relationships are assembled into structured quality rule records and stored in a graph database. A differential-adaptive elastic weight consolidation strategy is employed for continuous learning, combined with a human-machine collaborative quality inspection optimization model, achieving high-precision complex table parsing, physical consistency verification, and anti-forgetting continuous learning.

The invention discloses an intelligent agent creating and optimizing method for the sintering field, and relates to the technical field of intelligent industries, and the method comprises the specific steps: collecting a sintering field multi-source document, and constructing a structured knowledge base after processing; splitting and preprocessing the document to generate question and answer pairs; building a retrieval framework, processing the text by using a vectorization model, and calculating semantic similarity matching; finely adjusting the base model according to the question-answer pair to obtain a special large language model; finally, the model is deployed in a localized mode, performance is verified through an evaluation algorithm, a knowledge base, the model or retrieval logic is optimized according to a result, and a closed loop is formed; according to the method, a structured knowledge base in the sintering field is constructed by integrating multi-source knowledge, accurate query is realized through hierarchical problem tree and scene perception retrieval, a large language model is optimized by adopting a field adaptation fine tuning algorithm, closed-loop optimization is formed by combining a dual evaluation algorithm, professional and reliable intelligent agent output is ensured, efficient intelligent support is provided for iron and steel enterprises, and the method is suitable for popularization and application. And intelligent development of sintering production is promoted.

The application relates to a question and answer knowledge base and a method for establishing the same, and an intelligent question and answer method and system, comprising a source data acquisition unit, which is used for acquiring data in a computer system and marking the data as source data; a feature extraction and classification unit, which extracts feature data such as text semantic structure, keyword type and information format of the source data to classify the source data, obtains distinguished data QB and marks the distinguished data, and can analyze associated data in the distinguished data QB to generate pretreatment data Yc. The application specifically relates to the technical field of question and answer knowledge base technology, and through a source data processing module, the application can accurately extract feature data such as text semantic structure, keyword type and information format of the source data, thereby realizing effective classification of the source data, obtaining distinguished data QB and marking the distinguished data. This is helpful for quickly positioning and screening data related to question and answer, improves the efficiency and accuracy of data processing, and lays a solid foundation for subsequent question and answer knowledge construction.

The application discloses a retrieval method and a retrieval system. The retrieval method is applied to heterogeneous knowledge data, and the heterogeneous knowledge data includes index type structure knowledge, narrative type structure knowledge and graph type structure knowledge. The retrieval method comprises the following steps: acquiring the heterogeneous knowledge data, constructing an adaptive graph structure for the heterogeneous knowledge data, generating a graph structure representation signal according to the graph structure, wherein a multi-level index graph is constructed for the index type structure knowledge, a keyword index graph is constructed for the narrative type structure knowledge, and a knowledge graph is constructed for the graph type structure knowledge; receiving a retrieval question and the graph structure representation signal, performing retrieval reasoning based on a preset retrieval model, obtaining an evidence chain supporting the retrieval question, outputting a retrieval answer according to the evidence chain, and generating evidence traceability data. The technical scheme of the application can effectively improve the accuracy of the retrieval result, improve the retrieval efficiency, and meet the efficient utilization of the heterogeneous knowledge data.

The invention discloses a feature and knowledge driven optimal parallelism degree prediction method and system and a medium. The method comprises the steps that multivariate runtime features of a target parallel program are obtained, and codes are embedded to obtain multivariate fusion features; inputting the multivariate fusion features into a pre-trained speed-up ratio prediction model to predict the speed-up ratio of the target parallel program under a plurality of given parallelism degrees, the speed-up ratio prediction model being a neural network prediction model, a mapping relation between the multivariate fusion features of the parallel program and the speed-up ratios under a plurality of given parallelism degrees is trained and established by a training sample which is constructed by utilizing the multivariate fusion features of a parallel program sample and the speed-up ratio priori knowledge under different parallelism degrees in advance; and selecting the parallelism degree with the maximum speed-up ratio as the optimal parallelism degree obtained by prediction. According to the method, efficient, accurate and credible prediction of the optimal parallelism degree of the program is achieved, the calculation efficiency is improved, system resource consumption is reduced, and the execution time of the program is shortened.

The invention discloses an operation method of a network knowledge construction system based on big data, and the method comprises the steps: S1, randomly selecting K different basic parts as sampling target parts, the sampling target parts comprise a basic part A, a basic part B,..., and a basic part K, and the K is the number of the sampling target parts obtained by the system through sampling inspection expectation; s2, detecting scheme entries in a plurality of shops and merchants corresponding to the part retrieval result, and detecting equipment assembly variability of selling equipment of the merchants in the shops; s3, after obtaining the equipment assembly variability of a plurality of detection result shop merchants, dividing shops into spot check shops, first priority selection target shops and second priority selection shops; s4, evaluating to obtain the part redundancy rate of the optimal solution of the equipment; s5, the system outputs the recommended spot check shop name to a spot check worker through an output module; the method has the characteristics of low sampling inspection cost and high sampling inspection benefit reliability.

The application provides an OpenAPI multi-step state fuzzy testing method and related device, and relates to the technical field of network security. Through two stages of offline knowledge construction and online state exploration, the problems of too large sequence space, poor request executability and disconnection of exploration are solved, the search space is effectively compressed, semantic guidance is provided, and combination explosion is avoided; through runtime load multiplexing, the executability of deep state paths is ensured; deep-first search scheduling and snapshot rollback are adopted to ensure exploration efficiency and result reproducibility, thereby having the advantages of efficiently detecting multi-step state vulnerabilities.

The invention discloses a highway tunnel precise maintenance dynamic knowledge graph construction method and system. The method comprises the following steps: firstly, extracting corresponding knowledge from highway tunnel maintenance text data by adopting an existing knowledge graph construction method to construct an initial tunnel maintenance knowledge graph; secondly, dividing the initial tunnel maintenance knowledge graph into a plurality of knowledge networks with different dimensions by adopting a manual mode, and respectively expanding each divided knowledge network by taking a corresponding node as a network center; and then, combining the expanded knowledge networks into a fused knowledge graph based on the same entity or concept. And finally, performing knowledge graph reasoning on the fused knowledge graph, and continuously fusing graph knowledge obtained by reasoning into the fused knowledge graph to obtain the highway tunnel precise maintenance dynamic knowledge graph. Therefore, the limitation of the current knowledge graph construction method in understanding the semantic relationship and the complex structure of the tunnel maintenance knowledge is solved, and the correctness and the reliability of the knowledge graph are ensured.