101 results about "Knowledge level" patented technology

The invention provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

The invention belongs to the field of examination systems, and particularly relates to an online examination automatic test paper composition method based on a knowledge graph, and the method comprises the following steps: S1, data collection; s2, constructing an atlas; s3, collecting and analyzing examinee information; s4, analyzing examination requirements; s5, formulating a test paper generation strategy; and S6, test paper generation and verification. According to the online examination automatic test paper composition method based on the knowledge graph, knowledge materials are widely collected through multiple channels, data mining and semantic network technologies are used for carefully analyzing knowledge point relations, and the constructed knowledge graph is distinct in hierarchy and rich in semantics. And formulating a strategy by using an intelligent algorithm according to knowledge level evaluation and examination requirements of examinees during paper composition. For weak or important examination knowledge points of examinees, the number of questions, score weights and difficulty levels can be accurately controlled, blindness of traditional test paper composition is avoided, test paper is made to closely fit the reality of the examinees, and the knowledge mastering and application ability of the examinees is accurately evaluated.

The invention relates to the technical field of intelligent retrieval, in particular to an engineering knowledge base construction and deep retrieval method, which comprises the following steps of: in a knowledge base construction stage, performing knowledge level identification on an engineering knowledge document to form a multi-level tree structure taking chapter titles and corresponding contents as nodes; performing analysis processing on the heterogeneous content of each node to generate knowledge fragments; and generating embedded vectors of knowledge fragments by adopting a way of combining path embedding and content embedding, and storing association relationships among knowledge, vector data and original information to complete knowledge base construction. In the deep retrieval stage, after a user question is received, a knowledge blank question list is generated through query and rewriting, a knowledge base is retrieved through traversal of the list, the retrieval process is optimized through knowledge grouping, correlation sorting and loop termination judgment, and finally reply content subjected to traceable verification is generated and output based on existing knowledge accumulated in a circulation mode. According to the method, the reuse efficiency and the retrieval accuracy of the engineering knowledge can be improved.

The invention discloses a knowledge learning management method and device based on data analysis, and the method comprises the steps: obtaining a scene behavior data set, the scene behavior data set comprises the behavior data of each pharmacy employee in different business scenes, and the business scenes at least comprise a drug recommendation scene, a drug taboo reminding scene and a special medication consultation scene; constructing a behavior model according to the scenarized behavior data set, and extracting behavior feature information of the drugstore employees in different business scenes through the behavior model; determining a knowledge point set and a knowledge blind area which need to be mastered by the drugstore employee in each business scene based on the behavior feature information; constructing a knowledge graph according to the scenarized behavior data set, the knowledge blind area, the knowledge point set and the association relationship between the knowledge point set and the business scene; and based on the knowledge graph, determining professional learning contents of the drugstore employees and priorities corresponding to the professional learning contents. The professional knowledge level of drugstore employees can be comprehensively improved.

The invention discloses a resource learning path planning method and device based on deep learning, and the method comprises the steps: obtaining the knowledge levels and historical record information of a plurality of learners, building a learner model according to the historical record information and the knowledge levels, calculating the learner similarity between any two different learners in the learner model, and obtaining the learning path of the learners. Constructing a learning behavior model corresponding to the learner according to the learner similarity, predicting the cognitive level of the learner according to the learning behavior model and the learner model, constructing a learning path recommendation model according to the cognitive level, and planning a target learning path corresponding to the learner according to the learning path recommendation model and a preset knowledge graph, the learning behavior indexes are quantified according to the learning behaviors of the learner to construct the learner model, the learning path is recommended to the learner, the knowledge graph is combined to further complete accurate learning path planning, and a practical, reasonable and explainable learning path is provided for the learner, so that the learning efficiency and quality of the learner are improved.

The invention discloses a knowledge base construction method, a knowledge processing method and a related product. The construction method comprises the following steps: acquiring a plurality of first documents included in a first knowledge base; integrating the plurality of first documents to obtain a plurality of different document sets, wherein each document set in the plurality of different document sets comprises the first documents with semantic association relationship; carrying out inductive reasoning on the first document included in each document set based on the large language model and the document generation instruction to obtain a second document corresponding to each document set; and constructing a second knowledge base based on the plurality of first documents and the second documents corresponding to each document set. Therefore, the cross-document reasoning ability can be introduced, and the knowledge hierarchy of the second knowledge base is expanded through the induction reasoning ability of the large language model, so that the second knowledge base contains more information to meet the use requirements of the large language model, and the accuracy of the output result of the large language model is improved.

The invention relates to the technical field of artificial intelligence, and discloses a marketing knowledge base construction method, device and equipment based on artificial intelligence, and the method comprises the steps: obtaining basic information and session information, and converting the session information into text data according to the basic information; analyzing implicit relation data among the employees, the products and the clients based on the basic information, and extracting marketing knowledge from the text data; and carrying out hierarchical classified storage on the implicit relation data and the marketing knowledge, and pasting multi-dimensional labels on different knowledge to form a marketing knowledge base. According to the invention, through integration and structured processing of multi-source data, potential association among employees, products and clients is mined, and actual marketing knowledge is accurately extracted; hierarchical storage enables a knowledge system to be clearer, and multi-dimensional labels improve retrieval efficiency; the dynamic updating mechanism ensures the timeliness of knowledge, provides accurate knowledge support for a marketing scene, assists employees to quickly match verbal skills and schemes, and improves the communication efficiency and the transaction rate.

The invention discloses a personalized tutoring method integrating collaborative cognitive diagnosis and adaptive teaching. The method comprises the following steps: firstly, constructing a college entrance examination data set, constructing an initial knowledge hierarchy tree of each college entrance examination subject according to the college entrance examination data set, and sequentially performing cognitive diagnosis processing on knowledge points corresponding to unmarked nodes on the knowledge hierarchy tree according to the initial knowledge hierarchy tree in an inverse sequence traversal sequence to obtain an initial student cognitive state tree; the teacher performs tutoring processing on all the sub-question-solving steps of each college entrance examination question according to the initial student cognitive state tree until tutoring processing on all the sub-question-solving steps is completed; and all college entrance examination questions are tutored in a repeated traversal manner until all college entrance examination questions are tutored. According to the method, through two-stage personalized tutoring of cognitive diagnosis and adaptive teaching, accurate evaluation of student individual cognitive states and adaptive adjustment of teaching strategies are realized, and the performance of the method is superior to that of an existing method.

The invention discloses an aircraft fly-by-wire teaching simulation system, which comprises a flight operation table, a simulation computer and a comprehensive display system, the flight operation platform is in communication connection with the simulation computer; and the comprehensive display system is electrically connected with the simulation computer, and the comprehensive display system is used for displaying teaching contents of the fly-by-wire control system of the airplane, virtual flight scenes and virtual cabin instruments. A virtual reality technology is adopted, and technical means such as computer simulation and system modeling are comprehensively adopted, so that flight trainees can more intuitively understand theoretical knowledge such as basic composition, working principle, control rate calculation and control instruction response of an aircraft telex system, and the aircraft telex system can be simulated through control simulation training. Feedback of each functional module of the aircraft telex system to an aircraft control instruction is sensed in an immersive manner, the telex system theoretical knowledge level of a pilot can be improved, meanwhile, the actual installation loss is reduced, and economy and safety are achieved.

The invention discloses a building safety education training system based on interactive experience, relates to the technical field of construction site safety education, and aims to solve the problems that traditional building worker safety education lacks pertinence and is difficult to meet specific safety requirements of various types of work, and the teaching mode is relatively single. The system comprises a database, a personalized learning recommendation module, an interactive learning module and a learning progress and achievement tracking module. The database is used for pre-storing safety knowledge of various construction work types; the personalized learning recommendation module is used for screening and recommending learning content suitable for the worker from a pre-stored database covering safety knowledge of various construction work types according to the work type information and learning history of the construction worker, and the learning content comprises a document and a video course. The method has the advantages that precise education content conforming to the work type and the knowledge level of each worker is provided for the worker, and the safety awareness and the skill level of the building worker are improved.

An object of the system according to the embodiment is to provide a summary according to a knowledge level of a reader and optimize a reading experience.SOLUTION: A system according to an embodiment includes a summary generation unit, a marker function unit, a summary generation unit, and an expression change unit. The summary generation part generates a summary corresponding to the knowledge level of the reader. The marker function unit adds a marker to a specific range of the summary generated by the summary generation unit. The summary generation unit generates a summary of the range marked by the marker function unit. The expression changing unit changes an unclear part of the summary generated by the summary generating unit to a simple expression.SELECTED DRAWING: Figure 1

The invention provides an industrial process zero sample fault diagnosis method based on a similarity consistency relation, and relates to the technical field of fault diagnosis and classification. From the perspective of similarity between faults, on one hand, understanding of experts on the faults is deeply mined, and the similarity between fault categories is constructed based on the knowledge level; and on the other hand, the model is trained by using the known fault data, the data features of the known faults are extracted on the data level, and the similarity between the fault features is quantified from multiple angles. By solving a group of consistency parameters, mapping of faults from knowledge similarity to data similarity is constructed. Through the mapping, the abstract and comprehensive similarity relation of the knowledge level can be specifically explained by utilizing the similarity measurement of the data level, and the diagnosis of unseen class faults can be effectively supported.

The invention relates to the technical field of industrial automation and intelligent control, in particular to a multi-mode data and knowledge driven industrial process control core component, which comprises a multi-mode data interface and a preprocessing unit, the system and the method have the advantages that multi-modal sensing data and domain knowledge are deeply fused, accordingly, comprehensive and three-dimensional perception of industrial process states can be realized, data analysis can be guided and constrained by the aid of the knowledge, and the system and the method can be applied to the industrial process states. Therefore, the accuracy and robustness of the control decision are remarkably improved, and the data-driven decision result has the interpretability of the knowledge level by constructing a bidirectional fusion channel of the data and the knowledge, so that the trust problem of a black box model is solved.

The invention provides an educational question and answer method based on a multi-modal knowledge graph and multiple agents and a related device, and belongs to the technical field of educational question and answer. The method comprises the following steps: generating initial education question and answer content based on an obtained cognitive portrait of a learner and multi-modal knowledge data matched with a question and answer request in a multi-modal knowledge graph, and verifying the initial education question and answer content to obtain a verification result; judging whether the verification result meets the requirement or not, and if the verification result meets the requirement, collecting feedback data of the learner about the initial education question and answer content; based on feedback data of the learner about the initial education question and answer content, the multi-modal knowledge graph and the question and answer service process are adjusted through multiple agents. According to the method, the problems that a personalized adaptation mechanism for learners in different subjects, knowledge levels and professional directions in the education field is lacked, and high-quality question and answer service of an education scene is difficult to support are solved.

The present application relates to the technical field of knowledge tracking, and relates to a method for dynamically evaluating student knowledge level based on a double attention mechanism, comprising the following steps: S1. obtaining interactive information of a student learning process, and grouping the interactive information into a sequence; S2. dividing the interactive sequence into three parts, namely a dynamic problem level sequence, an average skill level sequence and an additional feature sequence; S3. inputting different sequences into corresponding modules for training, and obtaining a student knowledge state through a long short-term memory network and multiple attention mechanisms; S4. inputting the knowledge state into an interpretability module to evaluate the knowledge level; and S5. recording training model evaluation indexes, verifying the interactive sequence of the student through a model updated by parameters, and evaluating the knowledge level of the student. The present application fully mines the interactive information of the student, evaluates the knowledge state of the student from different angles, and improves the accuracy of predicting the future performance of the student.

A data processing device includes: a processor, in which the processor is configured to collect input information from a specific user, the input information being related to a specific technology, specify, by using a generation model, a knowledge level of the specific user for the specific technology based on the collected input information from the specific user, generate, when information related to the specific technology is requested from the specific user, technical information for the specific user based on the requested information related to the specific technology and the knowledge level of the specific user for the specific technology by using the generation model, and output the technical information for the specific user to the specific user.

The invention discloses a medical imaging teaching assistance method and system based on AI, and the method comprises the steps: collecting the learning background information of all students, and evaluating the knowledge level of the students; according to the knowledge level evaluation result of the student, intelligently planning course content; evaluating the mastering degree of the students on the course knowledge points to realize the dynamic adjustment of the teaching content; inputting the student data into an AI-based learning condition evaluation model, and optimizing the teaching content and method by the teacher according to the course learning condition of the student fed back by the model; a teaching resource sharing platform is established, different specialized teachers share teaching resources and information, teaching content communication is achieved, the real-time learning degree of the students on courses is known based on an AI learning condition evaluation model, the teachers dynamically adjust the teaching content, the teaching flexibility and adaptability are improved, and the learning effect of the students is improved.

The application relates to a wisdom detection system, method, equipment and medium based on robot technology, wherein the method comprises the following steps: an advanced detection module is used for acquiring detection data of a target robot according to a target detection element, so that the target robot performs a preset detection operation; a remote transmission module is used for acquiring environment information corresponding to the target robot according to real-time positioning information of the target robot, so as to determine a corresponding data transmission mode to transmit the detection data; a data wisdom management module is used for performing data processing and evaluation operation on the detection data, so as to obtain standard detection data, and based on a deep learning model, an adverse geological body identification result corresponding to the standard detection data is acquired. Therefore, the problems that the interpretation and evaluation process of the prior art excessively depends on professional knowledge level and engineering experience, the initial geophysical prospecting error is large, the adverse geological body is difficult to verify through drilling, and the stability of the detection element and the detection effect are greatly affected are solved.

To provide an information processing apparatus, a method for information processing, and an information processing program for easily providing information according to IT knowledge.SOLUTION: The present invention stores management information that has been managed related to system development, selects a knowledge level in system development, provides, to a user, a consultation input screen for entering consultation matters related to system development in accordance with the selected knowledge level, searches stored management information on the basis of the consultation matters entered in the consultation input screen and acquires search results, obtains, from a large language model, an answer generated in response to a prompt including at least the consultation matters entered in the consultation input screen and the acquired search results, and provides the obtained answer.SELECTED DRAWING: Figure 1

The invention discloses a resident energy-saving effect evolution prediction method and system, and the method comprises the steps: obtaining user preferences, user energy-saving knowledge levels and user education levels in a resident community, obtaining the number of household electric appliances of a user, and obtaining the data of a power transmission line in the resident community; on the basis of an entropy weight method, entropy values of user preferences, user energy-saving knowledge levels, user education levels and user household electric appliance numbers are calculated, and user load data are obtained through calculation according to the entropy values; performing transformation processing on the user load data and the power transmission line data based on Fourier transform, and analyzing correlation characteristics between the user load data and the power transmission line data to obtain a load correlation factor and a power transmission line correlation factor; and performing association prediction analysis on the user load data based on the load association factor and the power transmission line association factor to obtain load characteristic data. According to the resident energy-saving effect evolution prediction method provided by the embodiment of the invention, accurate detection of load prediction is realized.

A method for tracking a knowledge level of a user may include the steps of: detecting a reaction of a user consuming content; determining whether the user understands the content based on the user's reaction; inputting information about whether the user understands the content or not and information about the content into the knowledge tracking model; and updating the knowledge level of the user based on the output of the knowledge tracking model.

The invention discloses a method for question analysis, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining question information of a target question and historical learning data of a user; determining a target gear to which the historical learning condition of the user belongs according to the historical learning data, and determining a question analysis strategy matched with the target gear; based on the question information, the question analysis strategy and first prompt information, using a preset large language model and a retrieval enhancement generation module to generate a question analysis script; and generating a question analysis video by using a text video model based on the question analysis script, the reference video and the second prompt information. By utilizing the scheme disclosed by the invention, personalized question analysis can be realized, and adaptive explanation contents are provided for students with different knowledge levels.

This invention discloses a method, system, and device for recommending learning paths in online communities based on knowledge graphs. First, a knowledge graph model is constructed, consisting of three layers of entities: "course – topic – knowledge point." A multi-level text similarity function based on a corpus and core phrases is then constructed using user posts in the online community. Second, horizontally, the similarity function measures the semantic similarity between posts. Based on the user's knowledge background, a unique user with a similar knowledge level within the user group is identified, and their learning path is obtained. If a path is missing, vertically, the knowledge graph is used to recommend users at a higher level than the current topic's knowledge point. A unique user is identified based on their knowledge background, and their posts are obtained. Finally, the knowledge graph is used to recommend fine-grained and multi-context-aware learning paths for users in the online community. This invention can recommend fine-grained and multi-context-aware learning paths from both horizontal and vertical perspectives, combined with the user's knowledge background.

The invention belongs to the technical field of teaching platform resource recommendation, and discloses an intelligent teaching platform resource recommendation management method. Comprising the steps of collecting explicit data, implicit data and situation data; according to the implicit data, obtaining a long-term interest value and a short-term interest value of the user for the online courses, according to the long-term interest value and the short-term interest value, obtaining a comprehensive interest value of the user for the online courses, and according to the comprehensive interest value, recommending the corresponding online courses to the user; obtaining a concentration value of the user for the offline course and a knowledge level value of the user according to the situation data, setting a concentration value threshold range, obtaining a knowledge level grade of the user according to the knowledge level value of the user, and when the concentration value exceeds the concentration value threshold range, recommending the corresponding offline course to the user according to the explicit data. When the concentration value is within the concentration value threshold range, recommending a corresponding offline course to the user according to the knowledge level grade; and the accuracy and adaptability of teaching platform resource recommendation are further improved.

The invention discloses a curriculum commodity personalized recommendation method and device, a terminal and a storage medium, and relates to the field of commodity recommendation. The method comprises the following steps: acquiring client target data and multi-dimensional current situation data to perform state quantification to obtain a quantized state set, constructing a course logic relation graph, mapping knowledge level differences to graph effective nodes, performing topological sorting and time resource filtering to obtain candidate learning paths, and performing learning on the candidate learning paths. And calculating the comprehensive gain value of the candidate learning path, and selecting the path corresponding to the highest value as a personalized recommendation path. Meanwhile, the method also comprises a rapid recommendation step for new customers or data sparse customers. The technical effects of providing a personalized course commodity recommendation scheme according to the actual condition of the customer, improving the accuracy and effectiveness of recommendation, saving the learning time for the customer, improving the learning effect and facilitating the new customer to quickly obtain proper recommendation are achieved.

The invention discloses a multi-modal virtual teacher dynamic teaching method based on generative AI, and relates to the technical field of virtual teacher dynamic teaching, and the method comprises the steps: employing a data collection and preprocessing method to carry out the standardization processing of learning behavior data from various sources, and obtaining the preprocessed multi-modal data; carrying out fusion processing on the preprocessed multi-modal data by adopting an optimized Transform architecture or other cross-modal fusion technologies, and extracting key features by utilizing a self-attention mechanism to obtain a fused multi-modal data representation; analyzing the fused multi-modal data representation by adopting an emotion calculation model, identifying the emotion state of the student, and outputting emotion classification probability distribution; based on the understanding of the current knowledge level and emotional state of the student, adopting a reinforcement learning algorithm to plan a personalized learning path, and obtaining recommended learning resources and an activity sequence; and generating a customized learning material by adopting a generative adversarial network GANs based on a personalized learning path demand.

This application discloses a method, apparatus, device, and storage medium for constructing UAV technical data, relating to the field of computer technology. The method includes: extracting target cognitive behavior data of UAV aviation support personnel during mission execution to construct target triples and establishing a cognitive process model using a target BERT model; clustering knowledge elements related to UAV use and maintenance to obtain a knowledge hierarchy framework; extracting unstructured data from the UAV domain and constructing a target knowledge model using knowledge graph technology; determining the cognitive vectors at each stage of the cognitive process model; determining the matching relationship between the cognitive process and knowledge based on the similarity between the knowledge vectors and cognitive vectors corresponding to the target knowledge model; and constructing UAV technical data by combining the cognitive process model and the target knowledge model. While ensuring the completeness of the UAV technical data construction, the method also considers the cognitive characteristics and knowledge needs of users at different stages.

Systems, methods, and tools associated with AI knowledge boundary assessment are disclosed herein. A device may receive a question published to an AI-based smart entity, such as a robot, etc. The device may extract a first knowledge point from the question using a machine learning (ML) model, such as a large language model (LLM), and determine a first knowledge level associated with the first knowledge point. The device may also identify a second knowledge point owned by the robot corresponding to the first knowledge point, and determine a second knowledge level associated with the second knowledge point. By comparing the lifting levels of the first knowledge level and the second knowledge level, the device may determine whether the question issued to the robot exceeds the knowledge boundary of the robot.

A method for providing an automated response to user behavior comprising: receiving, by a computer system, data of user actions taken on a computer of the user, the computer of the user in communication with the computer system; analyzing the received data against the knowledge level of the user as determined by the computer system, and / or, the user's responses to simulations generated by the computer system, to determine a score for the user; and, in response to the score, making a behavior recommendation for the user and / or making a decision to take an action associated with the computer of the user.

The invention relates to the technical field of power load prediction, and discloses an intelligent power grid power load prediction system and method based on federated learning, the system comprises a plurality of user terminals, a plurality of edge nodes and a cloud server, the user terminals collect local power consumption data and train a local model; the edge nodes extract abstract knowledge representation vectors of a regional power consumption mode through knowledge distillation, the cloud server fuses knowledge of the edge nodes based on a topological knowledge graph and constructs a global prediction model, and the system further comprises an extreme weather compensation module which dynamically adjusts a prediction weight when extreme weather is detected. According to the method, cross-region fusion is carried out on a knowledge level instead of a parameter level, so that the problem of multi-region feature distribution difference is effectively solved, the prediction accuracy is improved by 8-12%, the communication overhead is reduced by about 60%, and the prediction error in extreme weather is reduced to be within 2%.