140 results about "Knowledge element" patented technology

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a multi-source knowledge processing and querying method, which comprises the following steps: data acquisition, cleaning and standardization processing, and standardized database construction; extracting core concepts and association relationships, and generating knowledge elements; constructing a multi-dimensional knowledge graph based on knowledge elements, and establishing a semantic index to realize data semantic annotation and bidirectional mapping; and analyzing the query intention, extracting a query constraint condition, and executing association reasoning based on the multi-dimensional knowledge graph to generate a query result. According to the method, the standardized database of the multi-source heterogeneous data is constructed, so that the data consistency is improved; through multi-dimensional knowledge graph construction and semantic index establishment, the relevance and structural expression of data are enhanced, so that the relationship between knowledge elements is clear and traceable; and through association reasoning based on query constraint conditions, the query accuracy and efficiency are improved.

The invention discloses a personalized learning recommendation method based on a personalized knowledge graph. The method comprises the steps that the knowledge graph is constructed, and a learner fills in learning style self-description and learning target expected content; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of the students are analyzed to determine the personal basic conditions of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt the personalized learning features to the learning knowledge element sequence, and a personalized learning push path recommendation mode is constructed to assist the students in improving the learning effect.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to the technical field of data processing, and discloses an AI knowledge base construction method and system for intelligent water conservancy. The method comprises the following steps: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literatures; knowledge sprouts are identified, and a water conservancy native knowledge unit is generated; key features are extracted, knowledge element decomposition is carried out, and a knowledge evolution chain network is constructed through correlation analysis; screening in combination with a historical application effect and expert evaluation to obtain a preferred knowledge body; performing intelligent reconstruction on a scheduling rule, risk prevention experience and an operation and maintenance method to form an innovative knowledge base; and finally, intelligently matching with real-time engineering requirements to generate an intelligent water conservancy AI knowledge base. According to the application, by establishing a self-adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to the environment change, the knowledge structure is optimized, and the intelligent operation management level of the water conservancy project is improved.

The invention discloses an enterprise-level knowledge base construction method based on a large model. The enterprise-level knowledge base construction method comprises the following specific steps: acquiring original data of an enterprise and preprocessing the original data to obtain first target data; performing knowledge extraction and representation based on the first target data to obtain second target data; based on the random-degree large model and the second target data, performing knowledge graph construction and storage to obtain an enterprise-level knowledge base; performing knowledge retrieval and recommendation on the enterprise-level knowledge base by using a modular retrieval enhancement generation method; and periodically updating and maintaining the enterprise-level knowledge base. According to the method, keywords and concepts in various knowledge carriers such as internal documents, reports and communication records of enterprises can be automatically identified based on the strong analysis capability of the random-degree large model, and automatic association among knowledge elements is realized; the enterprise-level knowledge base is quickly constructed, the knowledge base is periodically updated and maintained in time, the content of the enterprise-level knowledge base is enriched, and a foundation is laid for subsequent enterprise knowledge search.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention discloses a personalized learning recommendation method for a learner. The method comprises the steps that the learner completes learning style evaluation and learning target setting of the learner; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of students in the platform learning process are analyzed to determine the personal basic condition of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt personalized learning features to the learning knowledge element sequence, a personalized learning push path recommendation mode is constructed, and the learning recommendation efficiency is improved. And the learning effect can be improved.

The invention discloses a document analysis and query method based on a knowledge graph, and the method comprises the steps: carrying out the part-of-speech tagging of a received to-be-analyzed document, and obtaining a part-of-speech tagging result; extracting knowledge element information from the part-of-speech tagging result based on a preset power grid domain ontology knowledge base, and constructing an initial knowledge graph based on the knowledge element information; combining nodes in the initial knowledge graph to obtain a fused knowledge graph; constructing a mapping table according to the fused knowledge graph, and generating a candidate query template set based on the mapping table; receiving a natural language query statement input by a user, selecting a target query template from the candidate query template set based on the natural language query statement, and generating a target query statement; querying from the fused knowledge graph by using the target query statement, and outputting a query result; according to the method, the accuracy and comprehensiveness of information analysis can be effectively improved, the query intention of the user can be accurately understood, and the accurate query and analysis requirements of professionals on project documents are met.

The invention relates to the technical field of process scheme generation, and discloses an intelligent scheme reasoning generation method based on a process knowledge graph, and the method comprises the steps: firstly collecting multi-source process data, constructing a knowledge element extraction system, and outputting standardized knowledge entries through semantic analysis; establishing a process knowledge graph, and when a scheme reasoning demand is detected, positioning a target knowledge node through a logic association algorithm and generating an association relationship label; meanwhile, constructing a historical scheme case information base, and dynamically recording reasoning to generate a record; establishing a multi-dimensional weight configuration model, calculating a scheme matching degree score, and generating a recommendation priority sequence; solving an optimal scheme reasoning result by adopting an improved ant colony algorithm; and finally, outputting a result through the interactive verification platform, tracking and feeding back. The method improves the efficiency and quality of process scheme generation, and is suitable for process scheme formulation in industrial production.

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

The embodiment of the invention discloses a document knowledge element extraction method and device based on a large model and a medium, relates to the technical field of knowledge maps, and is used for solving the problem that an existing mode is poor in accuracy and low in interpretability. The method comprises the steps of obtaining original documents of a specified field based on a multi-source interface, and performing part-of-speech tagging and named entity recognition processing according to document contents and document fields of the original documents to obtain standardized documents; corresponding domain terms are collected, a corresponding domain knowledge graph is constructed, and entities and relationships of the domain knowledge graph are mapped to a low-dimensional vector space for data amplification; inputting a preset large model according to the amplified domain knowledge graph, and performing domain fine tuning on the preset large model based on a preset strategy to obtain a domain adaptive model; and inputting a to-be-extracted document in a specified field into the field adaptation model to extract knowledge elements of the to-be-extracted document, screening important knowledge elements based on the attention scores corresponding to the knowledge elements, and performing visual display.

The present application provides a method and system for determining regulation-violating behaviors underground. The method comprises: acquiring historical personnel behavior hidden danger data within a target area underground, and performing text mining and data processing on the historical personnel behavior hidden danger data to obtain spatial-temporal patterns of hidden dangers and prediction and early-warning results; performing association analysis on the basis of personnel regulation-violating data, the spatial-temporal patterns of the hidden dangers and the prediction and early-warning results, and carrying out knowledge modeling to determine an ontology model; using a knowledge extraction method to extract knowledge elements of a knowledge graph from the ontology model; performing knowledge fusion processing on the knowledge elements to determine knowledge fusion data; on the basis of the knowledge fusion data, using a top-down and bottom-up hybrid construction method to construct the knowledge graph; and determining regulation-violating behaviors of personnel underground on the basis of the knowledge graph. The present application can dynamically diagnose safety production risks and regulation-violating behaviors in mines, and can discover and predict related accident signs and provide early warnings to ensure safety production.

The invention provides a knowledge-enhanced medical illusion static detection and correction method and system, and relates to the technical field of medical illusion detection.The method comprises the steps that first feature information is obtained; determining a causal association strength coefficient; constructing a dynamic knowledge element network; obtaining first evidence information according to the first feature information and the dynamic knowledge element network; obtaining a first consistency coefficient; determining a timeliness coefficient and a confidence coefficient of the first evidence information; processing the causal association strength coefficient, the first consistency coefficient, the timeliness coefficient and the confidence coefficient through a trained medical illusion detection model to generate a medical illusion detection probability; determining whether the first feature information needs to be corrected; and when it is determined that the first feature information needs to be corrected, correcting the first feature information through a trained medical illusion correction model. According to the invention, the accuracy of medical illusion static detection and correction can be improved.

The invention discloses a cross-scale space-time registration and alignment method, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of obtained multi-modal data, and generating feature vectors and knowledge elements; based on the feature vectors and the corresponding knowledge elements, analyzing time updating characteristics and spatial position characteristics, and determining the data type of the multi-modal data; according to the data type, screening in an association relationship between the data type and an alignment registration strategy, and determining a target strategy corresponding to the multi-modal data; based on a target strategy, fusing the feature vectors and the corresponding knowledge elements, and performing time-space registration and alignment on the fused data to generate a target result; and based on the evaluation index and the target result, in combination with a learning algorithm, generating a target parameter so as to optimize related parameters of alignment and registration. By defining the new normal form of time and space combined registration, the problem of poor multi-source data fusion effect is solved, and the precision of multi-source data fusion is improved.

The invention provides an intelligent scheduling method and system applied to cable operation and maintenance, and the method comprises the steps: firstly deconstructing migration knowledge elements in a cable operation and maintenance migration knowledge base, dividing resource adaptive, process adaptive and risk adaptive knowledge elements, and generating a knowledge element classification set; constructing an operation and maintenance scene dynamic portrait of the to-be-scheduled cable, and integrating the operation characteristics, the operation and maintenance task requirements and the historical operation and maintenance performance data to generate a three-dimensional scene portrait; calculating an adaptation coefficient of each migration knowledge element and the portrait, and screening out a target knowledge element combination; constructing an initial operation and maintenance scheduling scheme framework based on the target knowledge element combination, and integrating various adaptation rules; and updating and adjusting a scheme framework through multi-turn portrait iteration, optimizing resource configuration, a step sequence and an abnormity coping plan, obtaining a final scheme, generating an operation and maintenance scheduling instruction, and sending the operation and maintenance scheduling instruction to an operation and maintenance execution terminal. According to the invention, intelligentization, precision and high efficiency of cable operation and maintenance scheduling are realized.

The invention discloses a fine-grained knowledge generation Agent method of an embedded metadata cue word project, and relates to the related technical field of data processing, and the method comprises the following steps: analyzing research problem generation elements, and determining research problem generation knowledge elements; constructing a knowledge element training data set; performing training fine tuning on the DeBERta model to obtain a fine-grained knowledge data set; constructing an embedded metadata cue word project; obtaining a research problem fine tuning data set; and carrying out fine tuning optimization on the large language model by adopting a LoRA fine tuning technology, and generating a research problem. The technical problems that in the prior art, due to the fact that model training is conducted based on a general data set, knowledge elements in a specific field are ignored, data are redundant due to the fact that fine-grained knowledge is not analyzed and screened, a language model is inaccurate, and the accuracy of a research problem generation result is poor are solved. The technical effect of improving the accuracy and quality of language model training and research problem result generation is achieved.

The invention provides a knowledge graph-driven medical data agent collaborative governance method, system and device and a medium, and belongs to the technical field of medical data governance, and the method comprises the following steps: constructing a knowledge graph containing a quintuple, and defining mapping relationships among medical data entities, attributes and standards; extracting multi-modal medical data knowledge elements through a structured / unstructured engine, and converting the multi-modal medical data knowledge elements into a quintuple for storage; executing table / field / value domain mapping based on the knowledge graph to generate a data mapping result; binding the quality control rules and the medical specifications to form a quality control quintuple; arranging the task into a directed acyclic execution sequence through an agent group cooperation task; and dynamically updating the knowledge graph and performing version management. Medical data full-link governance, knowledge graph unification heterogeneous data semantics, intelligent agent cooperation automatic processing complex tasks, update guarantee knowledge timeliness and quality control rule binding guarantee data quality are achieved, and medical data governance efficiency and accuracy are improved.

The invention relates to the technical field of chemical data processing, and provides a retrieval enhancement generation method and system oriented to chemical corpus data. The method comprises the steps that a special lexicon and a general lexicon for chemical knowledge are built, comprehensive and accurate chemical term resources are provided for a large model, and the large model and the RAG technology can better extract and understand the terminologies of the chemical industry; by designing a chemical engineering knowledge weighted graph, all entity words and text blocks in the graph are endowed with different weights, and each knowledge element can be paid attention to different degrees according to the weights in the retrieval process; a dynamic self-adaptive mechanism is introduced, the content of the chemical knowledge special lexicon can be dynamically enriched according to the updating and changing conditions of the knowledge in the chemical field, new chemical terms are contained in the chemical knowledge special lexicon in time, it is ensured that the large model can rapidly learn and master the latest knowledge, and the application performance and effect of the large model in the chemical field are remarkably improved.

The invention belongs to the technical field of electric power intelligent operation and maintenance, and relates to an electric power equipment fault prediction knowledge graph updating method which comprises the following steps: continuously receiving multivariate heterogeneous information from an electric power system operation environment, and applying at least one technology of natural language processing, time sequence analysis and statistical pattern recognition. Extracting new knowledge elements to form a to-be-verified new knowledge set, performing semantic consistency verification on the to-be-verified new knowledge set and an existing knowledge graph, detecting logic conflicts between the to-be-verified new knowledge set and existing knowledge triples and rules through logical reasoning, and obtaining a to-be-verified new knowledge graph based on a context environment where the logic conflicts occur. A conflict resolution strategy is automatically selected and executed by evaluating evidence reliability of new and old knowledge, and an optimized knowledge rule is generated, so that an existing knowledge graph is dynamically updated, the problems that an updating mechanism of the existing knowledge graph is lagged and evolution of context-related knowledge is difficult to process are effectively solved, and the reliability of the knowledge graph is improved. And the knowledge graph is pushed to be converted from static storage to dynamic evolution intelligent agents.

The invention relates to the technical field of textbook construction and optimization, in particular to an automatic textbook construction and optimization method and device.The method comprises the following steps that original resources of multiple types of textbooks are collected, and core knowledge elements in the original resources are recognized; constructing a structured mapping relationship between the knowledge elements and the teaching resources based on the core knowledge elements; on the basis of the structured mapping relationship, constructing a knowledge graph covering multidisciplinary knowledge points and a preposition dependency relationship, and automatically identifying knowledge faults and complementing missing association so as to form a knowledge network with self-evolution ability; based on the knowledge network, utilizing a hybrid generation model to cooperatively work, and quickly generating a structured textbook framework containing knowledge veins, teaching cases and exercise resources; learning condition data including classroom behaviors, learning feedback and knowledge mastering conditions are obtained, and a quantity and chemical condition report is obtained after data processing; and updating data based on the quantitative chemical situation report and the knowledge graph.

The invention relates to the technical field of virtual reality simulation, in particular to a virtual reality immersive practical training content generation method, which comprises the following steps of: constructing a domain knowledge graph: extracting atomic knowledge elements representing operation actions, equipment entities, state parameters and safety rules from a standard document and a case library of a target training domain, establishing a multi-dimensional logic relationship including a causal relationship, a time sequence relationship, a hierarchical relationship and a probability relationship among the atomic knowledge elements, and forming and storing a domain knowledge graph; acquiring trainee data in real time; constructing and updating a student state model; a dynamic content decision step; and generating and injecting nonlinear practical training content. By collecting student data in real time and matching the student data with the knowledge graph, the system can provide accurate feedback for the student after each practical training, and help the student find and overcome own knowledge blind spots.

A system can comprise a memory that stores computer executable components, and a processor, operably coupled to the memory, that executes the computer executable components comprising: a linking component that associates one or more unmasked elements of the logical form with one or more corresponding structured knowledge elements of a knowledge base and a prediction component that predicts the one or more masked elements based on extended context of the corresponding structured knowledge elements of the knowledge base to generate one or more predicted elements. In an embodiment, the prediction component predicts the one or more masked elements based on scores of one or more candidate elements. In an embodiment, the system can determine one or more rules that describe the natural language text segment in terms of the structured knowledge elements and associated weights of the knowledge base paths.

The invention relates to the technical field of computers, and discloses a learning difficulty intelligent adjusting device and method, and the method comprises the steps: extracting key knowledge elements from pre-obtained user learning data, and constructing a learning graph; according to the learning map, analyzing the knowledge mastering degree and the learning ability of the user to obtain a user ability evaluation result; generating corresponding learning content in combination with the user capability evaluation result and the node association relationship in the learning map; in the learning process of the user, the updating condition of the knowledge elements and the change condition of the learning ability of the user are analyzed, and the learning graph is dynamically updated to obtain an updated learning graph; according to the updated learning map, the difficulty of the learning content is adjusted in real time, updated learning content is obtained, and intelligent adjustment of the learning difficulty is achieved; the learning content difficulty is intelligently adjusted, the requirements of different users in different learning stages are met, and the learning effect and the knowledge mastering degree are effectively improved.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

The invention provides an automatic construction and semantic understanding enhancement method and device for a large model knowledge base. Internet multi-type data sources are integrated through multi-source data collection, and text data related to the large model application field is obtained; a knowledge extraction and fusion technology is utilized to extract key knowledge elements from the collected data and eliminate redundant contradictory information to form a unified knowledge representation system; based on the unified knowledge representation system, automatically constructing a knowledge base with rich semantic association according to a predefined structure mode, and introducing a data quality evaluation mechanism to guarantee the reliability of the knowledge base; and based on the constructed knowledge base, the deep semantic analysis technology is used to enhance the semantic understanding ability of the large model, including semantic coding, knowledge graph embedding, semantic reasoning and knowledge completion. According to the scheme, efficient, accurate and automatic construction of the knowledge base can be achieved, the semantic understanding ability of a large model is remarkably improved, the large model can understand and apply knowledge more accurately, and powerful support is provided for artificial intelligence application.

The invention relates to the technical field of data generation, and discloses a training content generation method and system based on a deep learning model and a storage medium, and the method comprises the steps: obtaining basic content data, and extracting text, table and format content to generate structured data; identifying a content structure and extracting training knowledge elements through deep learning model semantic analysis, and constructing a knowledge graph in combination with an association relationship between the elements; calculating the occurrence frequency, the content chapter and the core degree of the training knowledge elements to obtain a difficulty coefficient; obtaining training parameters, calculating a knowledge coverage coefficient and a knowledge depth coefficient, and generating initial content in combination with the first text framework and the large language model; and performing full-content verification and correction through the second text framework to obtain training content. According to the method, the training content is automatically generated, the problems of low efficiency, high cost, unstable quality and the like of manual question setting are solved, and the scientificity and pertinence of the training content are improved.

The invention relates to the field of computer data processing, in particular to a guided learning assistance method, system and device and a computer readable storage medium. The method comprises the steps of obtaining to-be-learned knowledge content corresponding to a target user, wherein the to-be-learned knowledge content comprises a plurality of knowledge elements; sequentially displaying the plurality of knowledge elements to the target user in multiple rounds, so that the target user can learn the plurality of knowledge elements; wherein the display process of each knowledge element in the plurality of knowledge elements comprises the following steps: acquiring user behavior data of a target user for the currently displayed knowledge element; analyzing the user behavior data according to a preset evaluation index corresponding to the currently displayed knowledge element to obtain an evaluation result of the currently displayed knowledge element by the target user; and adjusting the knowledge elements displayed in the next round according to the evaluation result. The method can better assist the user in learning based on the feedback of the user.

The invention discloses a natural resource data management method and device, equipment and a storage medium, and relates to the technical field of data management, and the method comprises the steps: collecting original natural resource data, and initializing a natural resource data set; performing structured processing operation on the natural resource data set, constructing a storage structure for storing and managing the data set, and forming a structured natural resource data set; performing element extraction on the structured natural resource data set, performing fusion processing on key knowledge elements obtained by extraction, and generating a target retrieval database according to a processing result; according to demand information input by a user, knowledge information matched with the demand information is retrieved and determined from the target retrieval database, and a corresponding visual scene is generated and displayed to the user based on the knowledge information. According to the method provided by the invention, the efficiency and convenience of acquiring and using the natural resource data by the user can be improved, and the requirements on visual display and analysis of the data in a natural resource management service are met.

The invention discloses an autonomous evolution type 5G protocol fuzzy test system based on a large language model, and the system comprises a protocol extraction unit which employs the large language model to analyze a 5G protocol specification document, and extracts structured protocol knowledge elements containing a message format, state machine logic and parameter constraints; the vulnerability analysis unit generates a potential vulnerability description list through big language model reasoning based on the knowledge elements; the test strategy generation unit drives a large language model to generate a targeted test case based on protocol knowledge, vulnerability description and historical test knowledge in the dynamic test knowledge base; the test execution unit executes the use case and captures a result; and feeding back an analysis result of the internalization unit, extracting a causal rule, generating new test knowledge and feeding back the new test knowledge to the knowledge base. According to the system disclosed by the invention, test targets and resources can be autonomously scheduled according to continuous updating of the knowledge base, so that the test process has an iterative optimization capability, and full-process automation and intelligentization of 5G protocol fuzzy testing are realized.

The invention relates to the technical field of knowledge graph construction, and provides a knowledge graph construction method and system based on a traditional Chinese medicine classical prescription, and the method comprises the steps: obtaining multi-source heterogeneous data composed of a digital text of a classical ancient book, a teaching material, a traditional Chinese medicine authority dictionary, an academic journal literature and structured clinical data; the standard text data is subjected to mixed strategy knowledge extraction, discrete knowledge elements such as entities, relations and attributes are separated out, and the discrete knowledge elements form an original fact unit of a knowledge graph; the extracted discrete knowledge elements are subjected to ontology modeling and formalized representation, and a multi-dimensional semantic network with the prescription-syndrome-disease-machine as the core is constructed; and performing semantic similarity comprehensive judgment by calculating the character string similarity of the entity names and combining context features of the entity names. The system comprises a heterogeneous data processing module, an entity recognition module, a semantic network construction module and a similarity judgment module. Structured organization, deep association and intelligent utilization of classical prescription knowledge are realized.