125 results about "Basic knowledge" patented technology

The invention discloses a learning assistance method and device based on a question bank knowledge graph, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: firstly, carrying out the extraction of knowledge points based on a textbook text, and obtaining a basic knowledge point cluster; performing importance degree evaluation on knowledge points in the basic knowledge point cluster, and performing screening according to an evaluation result to obtain an important knowledge point cluster; determining knowledge point distribution information of a basic knowledge point cluster and an important knowledge point cluster in the textbook, generating a knowledge tree containing the knowledge point distribution information, and determining association strength among knowledge points according to the number of branches of the knowledge tree; constructing a question bank knowledge graph according to the association strength among the knowledge points; and matching the question bank knowledge graph with a learning dialogue record of the user, and monitoring a question brushing condition of the user according to a matching result. An important knowledge point cluster is extracted from a teaching material, and a question bank knowledge graph is constructed, so that personalized learning assistance is realized.

The invention discloses a review data recommendation method and device, a storage medium and electronic equipment, and relates to the technical field of education, and the method comprises the steps: obtaining a teaching material text and a historical question bank, extracting and combining teaching material knowledge points to form a basic knowledge point cluster, screening out an important knowledge point cluster through a preset model, and generating target question banks with different difficulty levels. And mapping the data to a learning map, generating a learning track of the student, and recommending adaptive review data. The review data suitable for the current learning stage of the student can be recommended to the student according to the learning progress and the knowledge mastering degree of the student, and the problem that the review data is lack of coherence so that the review data can be matched with the learning state of the student is effectively solved.

The invention discloses a learning path generation method and device based on a knowledge point cluster, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: carrying out the extraction of knowledge points based on a textbook text, and obtaining a basic knowledge point cluster; determining a difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refining degree of the knowledge points in the basic knowledge point cluster; evaluating the importance degree corresponding to each knowledge point in the basic knowledge point cluster based on the difficulty level, and extracting an important knowledge point cluster from the basic knowledge point cluster according to the importance degree; and based on the basic knowledge point cluster and the important knowledge point cluster, generating question banks corresponding to the questions with different difficulty levels and mapping the question banks on a learning map so as to generate a learning path of the student in the learning process through the learning map. The important knowledge point cluster is analyzed and extracted from the textbook, so that the screening accuracy of the important knowledge points is improved, and personalized learning paths and key learning guidance are provided for students.

The invention discloses a knowledge graph recommendation algorithm oriented to a large language model, and relates to the technical field of knowledge graph recommendation, and the algorithm comprises the following steps: extracting terms and concepts in a subject text through a natural language processing (NLP) technology, marking entities by using a deep learning model, and setting the entities as nodes; a GCN method is adopted to extract relationships between entities and mark the relationships in the knowledge graph, a hierarchical graph is constructed, the first layer is a basic knowledge layer, and the second layer is a high-level knowledge layer; according to the method, historical behaviors, preference information and subject fields of the user are comprehensively considered, multi-dimensional scoring factors such as similarity, timeliness and correlation are combined, the weight coefficient is dynamically adjusted, it is ensured that each recommendation result is more accurate and personalized, and particularly in the aspect of user preference and interest changes, the recommendation efficiency is improved. The algorithm can capture and timely adjust the recommended content in real time and ensure that the recommended content is highly matched with the user demand, so that the use stickiness and satisfaction of the user are improved.

The invention discloses a learning content determination method and device based on an intelligent education platform and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: extracting a basic knowledge point cluster and a key knowledge point cluster from a student teaching material, and generating a target question bank containing different difficulties and question types; generating a knowledge point association network according to the association relationship and strength of the knowledge points corresponding to the questions in the target question bank; and in combination with the learning track of the student in the learning map, generating the target association strength between the target knowledge points, updating the knowledge point association network according to the target association strength, obtaining a student exclusive target association network, and updating the target question bank to determine the target learning content of the user. According to the scheme, the knowledge points are extracted through the textbook, the target question bank of the multivariate question types is generated, the problem of single question type in the prior art is solved, the question bank is dynamically updated in combination with the learning track and the association network, students can focus on core knowledge points, and the learning efficiency is improved.

The invention discloses a knowledge graph-based geophysics teaching assistance method and system. The method comprises the steps of obtaining associated graph information of gravitational exploration professional knowledge and basic physical knowledge; constructing a learning track map of the student according to the associated map information; analyzing answer data of the students based on the learning track atlas, and generating a knowledge blind spot diagnosis report; generating a progressive explanation path from basic physics to professional knowledge according to the knowledge blind spot diagnosis report; generating supplementary learning content based on the progressive interpretation path; and tracking and analyzing the test performance of the student after supplementary learning, and evaluating the question answering effect. Therefore, according to the geophysics teaching auxiliary method based on the knowledge graph, the physical basic knowledge defects of students can be made up in a targeted mode, the learning effect is improved, and the interest of the students in the geophysics major is enhanced.

The invention belongs to the technical field of education course recommendation, and discloses a personalized learning path recommendation method based on a knowledge graph, which comprises the following steps: acquiring a knowledge point set according to a learning target, and constructing a basic knowledge graph; matching the first similar group, and obtaining learning records of the first similar group to form a first reference record set; marking difficulty values for the knowledge points according to the mastery degree in the first reference record set, and generating a knowledge point learning sequence; taking the first reference record set as a target reference set, and matching an optimal learning track for each knowledge point to obtain a comprehensive learning path; after learning records of the learner are obtained, matching a second similar group, and obtaining a second reference record set; and optimizing the comprehensive learning path according to the second reference record set, and adding review nodes for the knowledge points with low mastery degree. According to the method, knowledge point difficulty values are marked based on actual learning data, a learning sequence of cognitive load balance is generated, a dynamic recommendation process from general to individuation is realized, and learning efficiency and experience are improved.

The invention provides a question bank generation method and device, a storage medium and electronic equipment. The method comprises the following steps: collecting a first feature data set containing influence factors of wrong question making of students by using an intelligent education terminal; a hierarchical clustering algorithm is utilized to screen out the characteristics of wrong questions made by the students due to objective factors from the first characteristic data set; utilizing a preset wrong question correlation evaluation model to evaluate correlation scores among the characteristics of the wrong questions made by the students caused by the objective factors, sorting the characteristics of the wrong questions made by the students caused by the objective factors from large to small according to the correlation scores, and determining knowledge points corresponding to the characteristic with the maximum correlation score as a first target knowledge point cluster; based on the basic knowledge point cluster and the first target knowledge point cluster, question banks corresponding to the questions of different difficulty levels are generated and mapped on a learning map, the learning map comprises one or more key learning grids, and the key learning grids are grids corresponding to the first target knowledge point cluster.

The invention discloses a teaching optimization method, system and device based on knowledge space and a medium. The method specifically comprises the steps that a secondary node platform is analyzed based on national industrial internet identification, official teaching materials are analyzed to generate knowledge point entities with identification codes, and a relation network is established; based on the relational network, collecting a multi-platform question bank problem solving step, performing authentication in combination with an identification code matching mechanism, synchronizing to a basic knowledge space after authentication, marking as an extended knowledge point library, and calculating a difficulty threshold value of a knowledge point association combination in combination with wrong question statistics; and on the basis of the relational network, cross-platform problem solving steps of the learner are captured in real time, authentication is carried out in combination with an identification code matching mechanism, if authentication is not passed, a preposed knowledge point unmastered state is marked, and a learner problem solving knowledge space with a state mark is generated. According to the invention, comprehensive analysis of the knowledge point mastering condition of the learner, difficult point positioning and personalized teaching optimization suggestion generation are realized, and the teaching quality and the learning effect are improved.

The invention provides a retrieval enhancement generation method and a retrieval enhancement generation system. The retrieval enhancement generation method comprises the following steps: segmenting basic knowledge text segments and carrying out semantic vectorization processing; establishing a historical relation graph; carrying out vectorization processing on nodes of the historical relation graph; obtaining a direct query result vector and a corresponding direct matching degree according to user query content; obtaining similar historical query vectors and corresponding similar historical query result vectors according to user query contents; calculating a historical matching degree through the direct query result vector and the similar historical query result vector; and calculating the overall matching degree, reordering the direct query result vectors, and then generating cue words and inputting the cue words into the large model. According to the technical scheme, the similarity analysis can be performed on the historical query result confirmed by the user and the direct query result, and the direct query result is reordered, so that the target query result which is more accurate and better meets the user requirement is obtained.

The invention provides a dynamic updating method and system of a knowledge graph based on reasoning enhancement. The method belongs to the cross technical field of artificial intelligence, knowledge engineering and dynamic system modeling. The method comprises the following steps: performing multi-dimensional feature extraction on original knowledge data to generate a knowledge feature vector set; constructing an initial knowledge graph structure based on the knowledge feature vector set, and defining an initial association rule between knowledge nodes to form a basic knowledge graph model; building a multi-order causal inference engine according to the basic knowledge graph model, performing deep mining on potential causal relationships among knowledge nodes, and generating a knowledge causal relationship network; and performing confidence evaluation on each causal chain in the knowledge causal relationship network to obtain the knowledge causal relationship network subjected to quality verification. Based on a multi-order causal reasoning engine, the system can mine and verify potential causal relationships among knowledge nodes in real time, and continuous updating and self-optimization of the knowledge graph are ensured.

The invention discloses a method and a system for generating a manufacturing process disturbance knowledge graph, and a storage medium. The method comprises the following steps: constructing a mode layer of the knowledge graph; inputting the SMT manufacturing process disturbance data set into the trained entity recognition model for processing to obtain an entity in the SMT manufacturing process disturbance data set; extracting the context of the entity to obtain an attribute corresponding to the entity and a relationship between the entities; fusing entities in the SMT manufacturing process disturbance data set, attributes corresponding to the entities and relationships between the entities based on the mode layer, and constructing a first SMT manufacturing process disturbance knowledge graph based on a Neo4j database; performing relation completion and attribute completion on the SMT manufacturing process disturbance basic knowledge graph to obtain a final SMT manufacturing process disturbance knowledge graph; according to the method for generating the disturbance knowledge graph in the manufacturing process, system modeling and efficient utilization of bad process knowledge are realized.

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

The invention provides a ground vibration spectrum acceleration prediction method, a transfer learning method, equipment and a medium, a global model is obtained through first training by using a basic knowledge data set, the first training is evaluated by using a regional knowledge data set, and then a transfer model is obtained through second training by using the regional knowledge data set. And the second training is evaluated by adopting the regional knowledge data set, and the establishment method of the migration model retains the data features of the regional database and learns the difference between the global data set and the regional data set, so that the prediction precision of the model is ensured, and the generalization ability of the model is enhanced.

The invention discloses a dynamic hierarchical knowledge graph construction method and system for an operator test, and mainly relates to the technical field of educational information. Comprising the following steps that a three-layer knowledge graph is constructed, the three-layer knowledge graph comprises a basic knowledge point layer, a question type rule layer and a current political examination point layer, and each layer comprises a plurality of entities, entity attributes, intra-layer association relationships and cross-layer association relationships; s2, updating the three-layer knowledge graph in the step S1 through a dynamic iteration mechanism: acquiring data from a data source, automatically extracting entities and relationships, triggering graph updating and performing conflict verification; based on post information input by a user, post characteristics are analyzed, weights of test points are calculated, and a post exclusive knowledge graph is generated according to the weights. The beneficial effects of the invention are that the system achieves the dynamic updating of the examination knowledge of the public operator, achieves the hierarchical management, achieves the precise matching of positions, and remarkably improves the examination preparation efficiency and pertinence.

The invention provides a scientific discovery intelligent system embedded with a scientific and technological text reverse reasoning mechanism, and relates to the technical field of data processing, and the system comprises a planning module which is used for extracting and clustering research problems, research methods and research limitations, and generating structured information; the storage module is used for forming a management knowledge base; the tool module is used for providing operation tools for the planning module, the storage module and the execution module; and the execution module is used for reasoning and optimizing a scientific discovery direction by combining a reverse reasoning mechanism based on the management knowledge base. The technical problems that in the prior art, an analysis result is limited by knowledge reserve and prejudice of experts, a current large model mainly depends on basic knowledge to conduct forward derivation, fusion of fine-grained knowledge is lacked, subjective dependence is high, and a reasoning path is single exist, the possible future research direction can be reversely deduced from existing research limitations, and the research efficiency is improved. And full-flow support of the scientific discovery process is realized, so that the accuracy of the reasoning direction is ensured.

The invention relates to the field of artificial intelligence, machine learning and data mining, and discloses a user behavior information intelligent analysis method based on a knowledge graph, and the method comprises the following steps: (1) carrying out the preprocessing of multi-source user behavior data, and generating a materialized behavior sequence; (2) constructing a domain basic knowledge graph; (3) performing incremental expansion on the knowledge graph according to the real-time behavior data to generate a user personalized sub-graph; (4) carrying out dynamic updating and pruning on the personalized subgraph; (5) fusing multi-user sub-graph knowledge through a federal learning framework; and (6) providing an intelligent analysis interface based on the updated knowledge graph. According to the method, real-time semantic modeling of user behaviors is realized through incremental expansion and causal verification, and the analysis precision and timeliness are improved; and in combination with federal fusion and map path reasoning, traceable decision support is provided under privacy protection, and the service credibility is enhanced.

The invention discloses an AI-based modern apprentice system personalized teaching system and method for medical students. Firstly, multi-dimensional data collection is carried out, and an initial data set is constructed by covering theoretical and practical scores, learning preference styles and the like; analyzing data by using an AI algorithm combining an analytic hierarchy process and machine learning, calculating a comprehensive capability value and defining a learning hierarchy; personalized teaching plans are formulated for students at different levels, such as high-level matching of high-difficulty practice tasks and subject research, low-level strengthening of basic knowledge and the like. And new data is collected every week to dynamically adjust the level and the teaching plan. According to the system and method, multiple factors are comprehensively considered, accurate teaching is achieved through AI, and the teaching effect and student ability are improved.

The invention discloses a smart home control method and system based on graph retrieval enhanced generation and multi-agent gaming, and the method comprises the steps: constructing a basic knowledge graph based on a current smart home environment, carrying out the semantic enrichment of service entities in the basic knowledge graph, generating an enhanced knowledge graph, and storing the enhanced knowledge graph in a vector database; constructing an example library containing a plurality of formalized observation-logic hypothesis pairs; acquiring a situation snapshot of the user, and outputting a structured scene analysis report based on the situation snapshot and the vectorization database through a front-end LLM; collaborative review is carried out by combining a factor-tracing reasoning agent, a user preference agent and a conflict detection agent with a scene analysis report and an example library, and a final decision is output by a core decision LLM and mapped into an equipment control instruction to call corresponding equipment for execution. According to the method, the fuzzy instruction of the user is combined with the knowledge graph, so that the intention recognition accuracy is improved; the multi-agent parallel auditing mechanism improves the robustness and safety of decision making.

The invention discloses a knowledge graph construction method and device, a storage medium and electronic device.The method comprises the steps that a comprehensive data set is constructed, accurate semantic relation representation is determined by recognizing entities in the comprehensive data set and relations between the entities, similar entities in the comprehensive data set are fused on the basis of the semantic relation representation, and the knowledge graph is obtained. In combination with heterogeneous data fusion, a basic knowledge framework is constructed, new knowledge is generated by utilizing an inference engine based on airspace monitoring requirements and the basic knowledge framework, and an airspace target knowledge graph is constructed in combination with an anomaly detection mechanism. The accuracy, reliability and safety of airspace target monitoring are improved, and intelligent development of airspace monitoring is promoted.

The present invention relates to the field of wood processing technology, and more specifically to a continuous fixed-width cutting system and method for wooden doors and door frames, comprising: an instruction input module, the instruction input module including a plate width input unit and a plate quantity input unit; a fixed-width module, the fixed-width module including a laser sensor, a plate drive component, and a controller, the laser sensor emitting a laser light path, and generating an action signal according to a state change of the laser light path at a first position. The present invention aims to simplify the cutting process. After a worker fills in a plate and inputs the size and quantity of a target plate, the system can automatically measure the width of the plate and complete the cutting of the target plate in order from wide to narrow. Workers do not need to participate in plate measurement and how to distribute the remaining material during the cutting process. This is very beneficial for workers with no basic knowledge, and enterprises can also reduce training costs and improve production efficiency.

The embodiment of the invention relates to the technical field of knowledge maps, and particularly discloses a knowledge map construction method and system for enterprise dynamic risks. According to the embodiment of the invention, risk original information is obtained; performing entity, attribute, relation and time information extraction on the risk original information; performing unified processing of entity linking, attribute merging and relation conflict resolution on the multiple pieces of structured information; constructing a basic knowledge graph; and based on the basic knowledge graph, constructing a dynamic risk model, performing future relationship prediction and risk trend prediction, and generating and displaying dynamic prediction information. According to the method, time span credibility evaluation and timeliness conflict interval positioning are carried out on multi-source attributes, the problems of attribute description conflicts and timeliness dislocation in multi-source data are effectively solved, meanwhile, permeation expression of risk signals on the attribute level is enhanced, and a consistent data basis is provided for accurate enterprise risk portraits.

The invention relates to the technical field of artificial intelligence, and provides a robot control method, device and equipment based on security enhancement and a medium, which can be applied to financial and medical health care service scenes, and can construct a security knowledge system of a target environment to provide comprehensive and structured security basic knowledge. Task disassembling is carried out according to the control instruction, then a security constraint label is added to each sub-task in a sub-task sequence according to a security knowledge system to obtain a sub-task label sequence, and thus security elements are fused into the sub-tasks; according to the method, the subtask labeling sequence is input into the security risk assessment model, and the security optimization is performed on the subtask sequence according to the risk prediction data, so that the subtask sequence can be adjusted in a targeted manner after the security risk in the subtask sequence is accurately identified, the security risk is effectively reduced, and the security and feasibility of the subtask sequence are ensured; and the robot is controlled to execute the safety subtask sequence, so that the safety and rationality of the robot control process are improved.

The invention belongs to the technical field of power failure repair, and provides an intelligent generation method and system for a power failure repair scheme, and the method comprises the steps: constructing a multi-modal instruction template, obtaining the basic knowledge of the power industry, constructing a data set, training a general large language model through the data set, and employing a hierarchical learning rate strategy to achieve the intelligent generation of the power failure repair scheme. Obtaining a large language model after fine tuning of domain knowledge; obtaining a power failure first-aid repair case, constructing the power failure first-aid repair case as a sample according to a pre-constructed multi-modal instruction template, and optimizing the parameters of the large language model finely adjusted by the domain knowledge by using the sample by taking semantic loss, physical constraint loss and safety loss as a weighted mixed loss function; obtaining a large language model after multi-modal task fine tuning; and processing the target data by using the large language model subjected to multi-modal task fine tuning to obtain a final power failure repair scheme. According to the invention, the accuracy and intelligence of power fault first-aid repair scheme generation are improved.

The invention discloses a network security dynamic early warning method and system based on a knowledge graph, and relates to the technical field of network security early warning, and the method comprises the steps: collecting the original data Raw of a network security event, analyzing core network entities and the interaction relationship thereof, constructing a structured basic knowledge graph KG, constructing a behavior feature algorithm based on the core network entities and the interaction relationship thereof, and carrying out the dynamic early warning of the network security. The method comprises the steps of generating a behavior feature set CE, generating a potential attack path set CP based on dynamic trigger points of the behavior feature set CE, screening a high threat path pmax in combination with a candidate path propagation score pps, calculating a threat index ATI to generate an early warning report AS, and performing linkage response action and closed-loop optimization. According to the method, the scattered events are associated as the attack chain through the knowledge graph, the risk is dynamically quantified, the threshold value is adaptively adjusted, the problems that early warning is lagged, the false alarm rate is high and multi-stage attacks cannot be associated in a traditional method are solved, and the early recognition and blocking capacity of complex threats is remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a child education quality analysis method and system based on artificial intelligence, and aims to evaluate education quality based on basic knowledge point learning influence analysis results of children and learning conditions of corresponding links of child courses. The score condition of the basic knowledge points of the corresponding students and the influence degree of the basic knowledge points are comprehensively considered, the learning influence condition of the students is comprehensively evaluated through a formula, and the limitation that only a single factor is concerned is avoided. In this way, the learning condition of students on each basic knowledge point and the influence of the knowledge points on the overall learning of the students can be more accurately known, in education quality evaluation, the teaching score condition of child courses and the basic knowledge point influence analysis result are comprehensively considered, and through a teaching quality analysis formula, the teaching quality of the students can be evaluated. The influence coefficient of previous knowledge points on courses is fully considered, so that the education quality evaluation is more comprehensive and reasonable.

The embodiment of the invention discloses a method for training a large language model for health notification. The scheme can comprise the following steps: acquiring initial training data; the initial training data comprises sample data and label data for the sample data; the sample data comprises a user input sample and a first health notification text; according to the incidence relation data, knowledge extension information for the user input sample is generated; the association relationship data is used for reflecting an association relationship among a plurality of pieces of data extracted from a preset text and basic knowledge of a field to which the preset text belongs; the preset text at least comprises a first health notification text; generating training sample data based on the initial training data and the knowledge extension information; and performing supervised fine tuning training on the pre-trained large model by using the training sample data to obtain a large language model for health notification.

The invention discloses a network security attack coping method based on a generative large model, and relates to the technical field of network security, and the method comprises the steps: obtaining metadata: automatically obtaining network security basic knowledge and network security attack technique data, and forming a knowledge base; classifying and labeling the metadata, namely automatically classifying and labeling network security basic knowledge and network security attack technique data; establishing a security model: constructing a network security analysis model, and cleaning malicious data and error data; model training is executed, wherein training and fine tuning are carried out on network security data; and performing evaluation feedback for coping with network security attacks. According to the method, the automation level and efficiency of network security analysis are improved, the manual analysis cost is reduced, the model can adapt to continuously evolved network security threats through continuous learning and optimization, and more intelligent and reliable technical support is provided for network security protection of a power system.

The invention belongs to the technical field of virtual training analysis, and particularly relates to an online virtual psychological training scoring system and a scoring method thereof. Comprising a training simulation module, a simulation evaluation module, a database module, a wide area network module, a management end and a student end. The training simulation module comprises a simulation server and a simulation output unit; the simulation evaluation module comprises an identity management unit, an index management unit and an evaluation unit; the database module is used for providing data support and processing service for each resource of the system and generated data; the wide area network module is used for establishing contact between equipment in the system and the management end and the student end; the management end is used for establishing an interface and a control mechanism related to system configuration management and system monitoring; and the student end is used for establishing a simulation training and student interaction operation mechanism. The virtual psychological training system is used for online virtual psychological training, meets the requirements of vocational skill training of related posts and the like, and enables learners to be familiar with and master basic knowledge, basic processes and practical operation skills of psychological training.

The invention provides a knowledge graph construction method and system for intelligently fusing multi-modal data, and the system employs a layered architecture, and comprises a data input layer, a mode special extractor, a multi-modal aligner, a graph construction / mining device, and a multi-modal retrieval service layer. The modular design enables the system to have good expandability and maintainability, and each module can be independently developed and optimized. The characteristics of different modal data are fully utilized, and the efficiency and accuracy of feature extraction are improved. Through the accurate alignment processing, the accuracy of entities and relationships in the knowledge graph can be effectively improved, and the alignment error is reduced. The deep association relationship among the entities in the basic knowledge graph is mined through the configured deep association mining engine, so that potential knowledge hidden in data can be found, and the content of the knowledge graph is enriched. A user can obtain knowledge from multiple angles, different application requirements are met, and the utilization efficiency of the knowledge is improved.