56 results about "Knowledge level" patented technology

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.

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 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 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.

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 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.

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.

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%.

The invention discloses an unmanned aerial vehicle technical data construction method and device, equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: extracting target cognitive behavior data of unmanned aerial vehicle aviation support personnel in a task execution process, so as to construct a target triple, and building a cognitive process model through employing a target BERT model; clustering knowledge elements used and maintained by the unmanned aerial vehicle to obtain a knowledge hierarchical framework, extracting unstructured data in the field of the unmanned aerial vehicle, and constructing a target knowledge model by using a knowledge graph technology; determining a cognitive vector of each stage in the cognitive process model; and determining a matching relationship between the cognitive process and the knowledge based on the similarity between the knowledge vector corresponding to the target knowledge model and the cognitive vector, and constructing technical data of the unmanned aerial vehicle in combination with the cognitive process model and the target knowledge model. Cognitive characteristics and knowledge requirements of a user in different stages are considered while the integrity of unmanned aerial vehicle technical data construction is ensured.

The utility model provides a progressive network engineering comprehensive experiment box, comprising a main control unit and an expansion circuit board, the main control unit is integrated with a main power supply, a core board, an Ethernet port, a USB interface and a GPIO button; the expansion circuit board comprises an expansion board power supply and an expansion board unit. The core board is further connected with a USB interface, a GPIO key, an Ethernet port and the like. The expansion board unit adopts a 2 * 12 female header design and integrates an I2C interface, an SPI interface, a UART interface, an I2S interface, an RTC circuit, a FLASH module, a remote I / O expansion module, an I2C Switch module and an I2C expansion interface, and through stable power supply management, reliable connection design, rich integration and expansion functions and a portable and flexible core module, comprehensive experiment content is provided, and the experiment efficiency is improved. And the operational ability and theoretical knowledge level of students can be effectively improved.

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 belongs to the technical field of power system employee training, and provides a learning path planning and learning effect evaluation method and system based on a neural network, electronic equipment and a storage medium. The method comprises the steps of data acquisition, coding feature matrix construction, knowledge graph entity embedding generation, vector splicing, coding processing, matching score matrix calculation, curriculum personalized screening, knowledge level evaluation, learning demand analysis, optimal learning path screening and learning effect evaluation. According to the invention, through a two-stage learning path planning process combining actual demands and employee historical data, a learning demand list and a recommended course sequence are enabled to accurately fit employee knowledge shortages and business demands, and the path can be adjusted in real time according to the employee's own learning progress; in addition, due to the integration of the power grid business data, the training content hysteresis is avoided, and the training quality is improved.

The application discloses a learner intelligent diagnosis method based on a Bayesian network, and belongs to the technical field of intelligent education. Knowledge points are modeled as binary random variables, a Bayesian network with a conditional probability table is constructed, and a bidirectional weighted graph is constructed through forward sampling and mutual information; a triple encoding Transformer that fuses node centrality, the shortest weighted path and edge encoding is used to generate node embedding; from the perspective of information theory, intelligent diagnosis is equivalent to a maximum decoding information path search problem, and the optimal diagnosis path is searched based on node embedding; through offline construction of a K-neighbor graph index and online greedy search, efficient diagnosis is realized with one training and multiple inferences. The application can accurately model knowledge level dependency, provide an interpretable diagnosis conclusion with causal tracing capability, and significantly improve the computational efficiency in multiple diagnosis scenarios, and can be used for personalized learning diagnosis and teaching intervention in an intelligent education system.

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.

Systems and methods are described that may include an adaptive language learning system including at least one processor; and memory storing instructions that when executed by the at least one processor cause the at least one processor to perform operations including: receiving an input from a user in a user interface, the input comprising a topic and a language; generating, based on the topic and a repository of language data having rules and collections of words and phrases associated with the language, interactive content pertaining to at least one lesson for learning the language; receiving interactions from the user with at least a portion of the interactive content; determining, based on the interactions, a knowledge level of the user with respect to language skills associated with the language; and generating, based on the determined knowledge level, one or more selectable indications comprising a suggested difficulty level.

The invention discloses a multi-role agent teaching plan generation method based on a large language model, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating a structured first draft of a teaching plan through a writing agent configured to have a primary teaching and research function and employing a fine-tuned large language model; a knowledge agent configured to have a knowledge management function retrieves and integrates knowledge points and teaching strategies related to teaching plan content from an external knowledge source, supplements and enhances a first draft of the teaching plan in a knowledge level, and obtains the teaching plan subjected to knowledge enhancement; an expert agent configured to have an advanced teaching and research review function is used for checking the teaching plan subjected to knowledge enhancement, identifying potential errors and providing proofreading suggestions or directly correcting the teaching plan to generate a teaching plan draft subjected to proofreading and review; and the writing agent iteratively optimizes the teaching plan draft after verification through feedback and knowledge integration of the expert agent, and outputs a final teaching plan. According to the invention, a set of comprehensive, efficient and practical solution is provided for automatic and intelligent generation of teaching plans.

A knowledge learning management method and device based on data analysis, wherein the method comprises: acquiring a scenario-based behavior data set, the scenario-based behavior data set comprising behavior data of each drugstore employee in different business scenarios, the business scenarios comprising at least a drug recommendation scenario, a drug contraindication reminder scenario, and a special drug consultation scenario; constructing a behavior model according to the scenario-based behavior data set, and extracting behavior characteristic information of the drugstore employee in different business scenarios through the behavior model; determining a knowledge point set and a knowledge blind spot that the drugstore employee needs to master in each business scenario based on the behavior characteristic information; constructing a knowledge graph according to the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the association relationship between the knowledge point set and the business scenario; and determining professional learning content of the drugstore employee and a priority corresponding to the professional learning content based on the knowledge graph. The application can comprehensively improve the professional knowledge level of the drugstore employee.

This invention relates to a retrieval enhancement generation method based on multi-level knowledge fusion, belonging to the field of natural language processing technology. It aims to solve two core problems in existing retrieval enhancement generation technologies: fragmented knowledge levels and interference from noise information. The invention includes the following steps: Graph index construction: Segmenting documents into fragments and extracting entities and relations to generate a knowledge graph with key-value pairs, followed by deduplication and optimization; Multi-level knowledge retrieval: Acquiring local and global knowledge separately, and constructing cross-level semantic associations by connecting knowledge modules through an intermediate layer; Dual-dimensional filtering: Filtering core knowledge based on a weighted score of semantic relevance and knowledge importance, combined with a maximum boundary relevance strategy to remove redundant noise; Response generation: Inputting the filtered knowledge into a large-scale language model to generate accurate and coherent responses. This invention significantly improves the generation quality of knowledge-intensive tasks through cross-level knowledge bridging and precise noise filtering, possessing significant theoretical and practical application value.

The application discloses an AI-based medical imaging teaching auxiliary method and system, which comprises the following steps: collecting learning background information of all students and evaluating the knowledge level of the students; intelligently planning course content according to the evaluation result of the knowledge level of the students; evaluating the mastering degree of the students on the course knowledge points to realize 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 according to the feedback of the students on the course learning condition; and establishing a teaching resource sharing platform to share teaching resources and information among different teachers, realize the penetration of the teaching content, and understand the real-time learning degree of the students on the course based on the AI-based learning condition evaluation model, so that the teachers can dynamically adjust the teaching content, improve the flexibility and adaptability of the teaching, and improve the learning effect of the students.

A system is provided.SOLUTION: A system comprising: means for obtaining text data from a user; means for obtaining knowledge level information of the user from a database; means for using generative artificial intelligence to convert the text data into a simplified expression or a summary according to the knowledge level of the user; means for obtaining a question from the user and generating an answer to the question using the generative artificial intelligence; and means for transmitting the converted simplified expression or summary text and the generated answer to the user.SELECTED DRAWING: Figure 1

The invention discloses a lightweight AI training method and system based on multi-modal knowledge distillation, and the method comprises the steps: firstly constructing a multi-layer teacher network with three processing paths of vision, text and sensor, and explicitly describing the hierarchical expression of each modal from low-level features to high-level relationships; then designing a lightweight student network with a simplified structure, and establishing a multi-granularity knowledge migration path covering an output layer, a feature layer, a relation layer and a structure layer; a dynamic weight distribution mechanism is introduced, and key modals and knowledge levels are adaptively focused according to sample characteristics and a training process; implementing layered distillation, and matching knowledge transmission modes of different abstract hierarchies according to depth differences; the model is optimized by combining parameter sharing, structure re-parameterization and dynamic path selection during operation; modal balance, hierarchical consistency and cross-modal alignment are guaranteed through multi-dimensional verification and closed-loop compensation; and finally, resource awareness deployment and self-adaptive operation on the edge equipment are supported.

The application belongs to the technical field of data processing, and provides a spatial domain identification method in spatial transcriptomics based on hyperbolic graph neural networks, which comprises the following steps: step S1, constructing a spatial graph; step S2, introducing the operation of multiple manifold encoders in Euclidean space and hyperbolic space for a spatial transcriptome dataset; step S3, fusing the features of multiple manifolds using an attention mechanism; step S4, balancing the difference between the original features in step S3 and the reconstructed features decoded out to construct multiple loss functions; and step S5, identifying the spatial domain using a clustering algorithm based on a Gaussian mixture model. The application can not only replace the time-consuming manual division and annotation of the spatial domain by biomedical experts, shorten the identification time, but also does not depend on the knowledge level of experts, can control errors to a certain extent, ensures real-time performance and accuracy, and the accuracy of the application is better than that of existing spatial domain identification models.

This invention relates to a personalized learning recommendation method for e-commerce, belonging to the field of information recommendation technology. The method integrates user basic attributes and historical behavior data, deeply integrating multi-dimensional user profiles through adaptive weighting; it utilizes the association mining between products and knowledge points, establishing a product knowledge graph based on graph neural networks to achieve product topic clustering and multi-level knowledge point summarization; based on user profiles and product knowledge associations, it leverages deep reinforcement learning to dynamically plan personalized learning paths, intelligently recommending optimal resource sequences according to the user's current interests and knowledge level. The system collects user feedback information in real time and completes real-time adaptive adjustment of the recommendation strategy through local model updates and federated learning, achieving highly accurate and privacy-preserving personalized learning resource recommendations under interest shifts and behavioral changes.

This invention discloses a method, apparatus, device, and medium for generating a building knowledge graph. The method includes: acquiring a first file; determining a first knowledge level based on building instance types, preset attribute types of building instances, and connection relationships between building instances in the first file; acquiring a second file; determining a second knowledge level based on building instances, connection relationships between building instances, and attribute values ​​corresponding to preset attribute categories in the second file; receiving a third file; determining event nodes, edges between event nodes and nodes in the second knowledge level, and target temporal state information based on the third file; and updating the third knowledge level based on event nodes and edges, where the attributes of the edges include the target temporal state information. The technical solution provided by this invention can improve knowledge storage efficiency and indirectly improve retrieval efficiency when using the knowledge graph.

The invention belongs to the technical field of digital reading services, and discloses an all-media digital reading service system, which unifies each modal format and adds knowledge anchor points through an acquisition and feature extraction module, and constructs a double-layer knowledge graph of basic static knowledge and difference real-time knowledge through a cleaning and semantic fusion module. A structured system is formed according to knowledge levels of basic concepts, principle derivation, application cases and expansion and extension, and semantic coding and knowledge point entity association is completed in combination with a BERT model; when a user inquires knowledge points, the system can automatically match multi-mode resources such as text analysis, experiment demonstration videos and formula derivation audios, manual switching retrieval is not needed, the stepped knowledge acquisition requirement is perfectly met, and the problems that cross-media knowledge is loose in association and low in acquisition efficiency are solved; the modeling and behavior analysis module collects user full-scene data, constructs three layers of user portraits including basic attributes, dynamic demands and cross-modal preferences, and can also mine behavior implicit demands.

The application provides a control method and device of a vehicle, the vehicle and a storage medium, and belongs to the technical field of vehicle service. The method comprises the following steps: obtaining historical behavior information of a user in the process of interacting with the vehicle, extracting target feature data reflecting the cognitive level of the user for vehicle knowledge from the historical behavior information, determining the target knowledge level grade of the user based on the target feature data, and finally controlling the vehicle to provide the user with services corresponding to the target knowledge level grade. The method can accurately evaluate the cognitive level of the user for vehicle knowledge, thereby providing the user with services matching the cognitive level.