74 results about "Basic knowledge" patented technology

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 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 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 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 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 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 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 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.

The application discloses a knowledge graph construction method and device of a promotion path, wherein the method comprises the following steps: step S1, combing core concepts related to the promotion path and semantic relations thereof, constructing a basic knowledge framework, and creating an ontology model based on the basic knowledge framework; step S2, setting mapping rules between ontology data and a graph database, and mapping the ontology model to the graph database; step S3, collecting multi-source information, performing knowledge extraction on the multi-source information to obtain knowledge triples and constructing entities, and constructing a knowledge graph by comprehensively combining the ontology model and the entities; and step S4, performing retrieval and correlation analysis based on the knowledge graph, forming promotion path instances, dynamically updating the promotion path instances to the knowledge graph, and obtaining a promotion path knowledge graph. The promotion path knowledge graph is obtained based on dynamic updating of the knowledge graph.

The invention relates to a digital delivery-based building operation and maintenance corpus construction method, which comprises the following four core processes: step 1, constructing a footstone corpus containing structured multi-source information based on building full-life-cycle digital delivery data; 2, generating professional question and answer pairs covering multiple categories in combination with operation and maintenance scenes and expert experience; 3, marking a decision-making thinking chain containing a judgment basis, an analysis process and a processing suggestion for the complex scene; and step 4, establishing a multi-dimensional evaluation system covering basic knowledge, problem solving, professional decision making and innovation ability. By integrating full-life-cycle digital delivery data, hierarchical construction of corpora from foundation to complexity is achieved, the speciality and effectiveness of the corpora are guaranteed through quantitative quality evaluation and a dynamic optimization mechanism, the problems that existing corpora are single in data and lack of deep decision knowledge are solved, and the method has the advantages of being high in practicability and easy to popularize. High-quality data support is provided for special large model training in the field of building operation and maintenance, and intelligent upgrading of operation and maintenance is promoted.

The application discloses a kind of based on multi-task depth knowledge tracking method and device, wherein the method comprises: obtaining the question representation, the knowledge point representation and the answer correctness result of all time before current time;Input the question representation, the knowledge point representation and the answer correctness result to the knowledge tracking model after training for processing;And, in response to the processing of the knowledge tracking model, obtain the knowledge point answer correctness result of next time;Wherein, the knowledge tracking model is based on the specified knowledge tracking network, including the first auxiliary task component for predicting the knowledge point corresponding to the question of current time t and the basic knowledge tracking task component for predicting the knowledge point answer correctness result of next time t+1. The present application scheme simultaneously predicts the knowledge point input by student at current time and predicts the historical overall answer accuracy of student at each time step on the basis of the basic task of predicting the correctness of question to be answered by student, and improves the evaluation effect of doing question.

The invention discloses a Unity-based cable accessory installation training system and a construction method thereof. The system comprises a basic knowledge learning module, a professional knowledge learning module, a training examination module, an examination evaluation module and the like. The basic knowledge learning module provides content such as identification graphs related to cable accessory installation, electrical instruments, tool use and safety specifications and the like, and teaching is assisted in a visual mode of images, texts, voice and structures. The professional knowledge learning module is used for displaying key installation processes and process standards in the forms of three-dimensional animation, video explanation, structure explosion diagram and the like; the training examination module is provided with basic examination and practical operation examination, and supports question bank import, interactive answering and action monitoring; and the assessment and evaluation module performs statistical analysis on assessment data and generates visual assessment reports such as a radar map and the like. According to the invention, the learning efficiency of trainees can be improved, the training cost is reduced, the training safety and flexibility are enhanced, and standardization, intelligence and high efficiency of cable accessory installation skill training and assessment are realized.

The invention discloses a marketing product knowledge graph construction method and device, and belongs to the technical field of data processing, and the method comprises the steps: constructing a weighted directed graph comprising user nodes, dish nodes, dish category nodes and consumption scene nodes as a basic knowledge graph; collecting time, weather and geographic position signals in real time, calculating a scene influence value through a quantization algorithm, and dynamically updating a relation weight by adopting a multiplication correction model; performing correlation intensity, diversity and novelty optimal path search in parallel on the dynamic map, and outputting an equilibrium path set based on Pareto optimal selection; generating a multi-modal marketing content package in combination with the user request, the real-time scene and the marketing target; the efficiency is evaluated through A / B test, and the click rate and the order-placing conversion rate are used as reward signals to be fed back to the weight correction process. Through the design of dynamic perception, multi-target collaboration and intelligent generation, the accuracy, diversity and real-time performance of the recommendation system are remarkably improved.

The application discloses a work order processing method, device and equipment and a readable storage medium. After obtaining a to-be-processed work order, first, first prompt information for processing the work order and indicating summarizing the content of the work order is determined, then, based on the first prompt information, the summary content of the work order is obtained; then, based on the summary content of the work order, whether there is a similar question to the summary content of the work order in a basic knowledge base is searched, and a first search result is obtained; finally, according to the first search result, the work order is processed, and a reply corresponding to the work order is obtained. Based on the scheme, automatic work order processing can be realized, and more efficient and high-quality government affairs question answering can be realized.

The system according to this embodiment aims to enable beginners to efficiently learn the basic knowledge of app development and actually develop apps. [Solution] The system according to the embodiment comprises a provisioning unit, a designing unit, and a testing unit. The provisioning unit provides educational materials for beginners to learn the basic knowledge of app development. When a user inputs an app idea, the designing unit generates an app design based on that idea using a generation AI, and then performs coding. The testing unit automatically verifies the app's operation using the generation AI, detects bugs, and corrects them.

The application provides a large model accident investigation auxiliary method and system based on knowledge graph enhanced retrieval, and belongs to the technical field of accident investigation and fire accident investigation. The method comprises the following steps: loading and registering a prefabricated basic knowledge graph of accident investigation; analyzing and block processing original accident investigation data to obtain a fragment set; updating the basic knowledge graph according to the fragment set to obtain a unified accident investigation knowledge graph; obtaining user query description information, performing intent analysis and constraint extraction on the user query description information to obtain structured condition data; performing retrieval according to the structured condition data and the unified accident investigation knowledge graph to obtain a unified candidate evidence set; analyzing and reasoning the unified candidate evidence set to obtain explanation result data with evidence reference and graph path; and completing accident investigation assistance according to the explanation result data. The application embodiment can enhance the auxiliary decision-making ability and engineering landing nature in a complex scene.

This invention provides a method and system for constructing a professional knowledge graph for the hot rolling process of strip steel, belonging to the field of industrial monitoring and intelligent manufacturing. The method first acquires multi-source heterogeneous data of the hot rolling process of strip steel, and transforms structured and semi-structured data into unstructured data before merging them into an unstructured dataset; constructs an ontology for the professional knowledge graph, including entity types and entity relationships; divides the unstructured data into training and testing sets; constructs a joint extraction model based on the CasRel framework and multi-hop attention mechanism, and performs training and testing; inputs all unstructured data into the model, identifies and extracts a set of structured triples, processes and stores them to obtain the basic knowledge graph; acquires hot rolling fault data and constructs a fault knowledge graph, and fuses them to obtain the professional knowledge graph for the hot rolling process of strip steel. This invention constructs a professional knowledge graph for the hot rolling process of strip steel, improving the systematic capture capability of hot rolling process data.

The application relates to the technical field of education training, and discloses a training subject management method and system based on knowledge classification. The system comprises a knowledge classification unit, a knowledge evaluation unit and a matching management unit. Through unique algorithms and technologies, knowledge is accurately divided into three types, namely, basic knowledge, advanced knowledge and application knowledge. For example, basic knowledge is compared by using a keyword library, advanced knowledge is constructed to calculate a correlation degree, and application knowledge is analyzed in combination with actual cases, thereby laying a solid foundation for subsequent training. A standardized evaluation process is designed for different types of knowledge, and the knowledge level of a trainer can be comprehensively understood. Different recommendation strategies are formulated according to the level of each knowledge type of the trainer, for example, excellent basic knowledge is focused on advanced and application knowledge, and large projects are recommended for excellent application knowledge, so that the learning needs of different trainers can be met, the problem of mismatched learning content is avoided, the training effect and efficiency are improved, and the knowledge and application ability of the trainer is more efficiently improved.

The invention discloses a method and a device for evaluating the capability of a financial large model, a storage medium and electronic equipment, relates to the technical field of model capability evaluation, and mainly aims to solve the problems of incomplete evaluation standards and data sample pollution in the performance evaluation of the financial large model. The method comprises the following steps: selecting basic knowledge in the financial field and related academic texts in the industry as a basic data source to generate a basic corpus; performing decontamination processing on the basic corpus, and constructing an evaluation data set based on the decontaminated basic corpus; creating a plurality of evaluation tasks, and configuring a corresponding prompt instruction for each evaluation task; obtaining a to-be-evaluated financial large model, and evaluating the to-be-evaluated financial large model by adopting the evaluation data set, so that the to-be-evaluated financial large model sequentially executes each evaluation task corresponding to the prompt instruction to obtain an execution result; and calculating an evaluation index based on the execution result corresponding to each evaluation task, and determining the comprehensive capability of the financial large model to be evaluated based on the evaluation index.

The invention provides a fault information determination method and device, equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence and the technical field of financial science and technology. The method comprises the following steps: performing entity identification on abnormal data related to operation of the distributed system to obtain an abnormal entity; an associated entity associated with the abnormal entity is determined from multiple entities included in the target knowledge graph, the entities are used for representing objects related to the fault determination requirement of the distributed system, and the target knowledge graph is obtained by supplementing the basic knowledge graph according to a supplementary entity; the supplementary entity is determined according to a clustering result of multiple candidate entity feature data, and the candidate entity feature data is obtained by performing feature extraction on supplementary corpora related to operation of the distributed system; and determining fault information according to the attribute information of the associated entity.

The application relates to the technical field of data processing, in particular to a model construction method and system based on data hierarchical classification. The method comprises the following steps: preliminarily marking building project data according to the data types of the building project, and setting project entities; extracting state keywords from each project entity, and performing semantic clustering according to the scenes to which the state keywords belong, to construct a basic knowledge graph containing state labels; based on the generated basic knowledge graph, defining the correlation of each project entity under the project state change as a trigger condition; determining the adjacent nodes when the project state changes, tracking the change path of the adjacent nodes, and updating the change path to the edge attribute of the basic knowledge graph; and determining the expected classification after each update according to the updated data of the change path in the basic knowledge graph; and the efficiency and accuracy of the data hierarchical classification processing are improved.

A system and method for transforming a computer display into a dynamic knowledge structure representing hierarchical knowledge levels, achievements, and prerequisites that represent foundational knowledge to mastery of a knowledge concept is disclosed. The system and method access a knowledge data that defines knowledge levels. The knowledge levels represent a sequence of knowledge levels to reach mastery levels. An electronic display is transformed to visually present the knowledge levels as interconnected, physical object representations. The interconnections represent the prerequisite knowledge levels. Then personnel knowledge completion levels of a student are accessed. The appearances of the knowledge level physical objects that correspond to respective knowledge levels to be differentiated based on a state of mastery of corresponding knowledge levels by the student that inform the student of progress towards knowledge concept mastery and gaps in the foundational knowledge of the student of the knowledge concept mastery.

This invention relates to the field of intelligent model training technology, specifically a method and system for training and inferring a large-scale knowledge-enhanced model in the water industry. The method includes: confirming the large-scale model's operating environment based on water industry model training and inference instructions; classifying and extracting the water industry dataset based on a basic knowledge parsing unit; abstracting features from multimodal operating data and industry experience knowledge sets based on structured modeling rules; updating parameters of a general basic large-scale model based on model training constraint rules; inputting real-time water industry operating data into the water industry knowledge-enhanced large-scale model for joint inference calculation based on a water industry operating condition inference unit; using a target knowledge representation set to screen candidate control strategies for compliance; verifying the execution of the target strategy; and obtaining the optimal operating strategy based on the strategy deviation evaluation value. This invention can improve the security, real-time adaptability, and reliability of large-scale model training and inference in the water industry.

The application provides a software test case generation method and system based on domain knowledge constraints. A domain knowledge base covering domain basic knowledge and industry testing criteria is constructed; the original requirement text is normalized, the semantic understanding ability of the knowledge base and the large language model is combined, the requirement type is identified and the structured decomposition rule is dynamically matched, and a structured requirement model is constructed; the test focus features in the requirement are extracted, and the test item decomposition requirements are formulated; the large language model is driven to expand the scene and systematically disassemble the requirement, and the systematic test items are generated according to the test item decomposition requirements; the test cases are generated in multiple dimensions with the test items as the input, and the traceable relationship among the requirements, the test items and the test cases is established; the integrity check and consistency check are performed, and the test design result is output. The application enables the large language model to perform the test design task in a controlled and professional knowledge environment, and realizes the systematic generation from the natural language requirement to the executable test case.

The application provides a construction method of a knowledge distillation model suitable for power line fitting detection, comprising the following steps: inputting image data of power line fittings into a basic knowledge distillation model to extract multi-scale feature data; performing redundant compression processing and convolution kernel learning on the multi-scale feature data to obtain a light knowledge vector and fitting detection general knowledge; training the basic knowledge distillation model using the knowledge and teacher-student global knowledge prototypes, and performing differential distillation training on classification heads and regression heads based on objects and information effectiveness respectively in the process to obtain a preliminary knowledge distillation model; and combining a comprehensive loss function to perform end-to-end weighted optimization on a student model in the preliminary knowledge distillation model to obtain an optimized final knowledge distillation model. The application can solve the problems of high model complexity and insufficient adaptability to complex power fitting detection scenes in related art knowledge distillation-based detection methods.