18 results about "Knowledge space" patented technology

The invention relates to the technical field of intelligent medical teaching, and discloses a medical intelligent teaching model construction method based on an ultrasonic AI technology. According to the method, ultrasonic image sequence data and corresponding operation records of a target object are integrated to generate an original teaching data set; carrying out multi-dimensional teaching feature analysis on the dynamic teaching feature parameter set, and extracting a dynamic teaching feature parameter set; constructing an anatomical structure evolution feature tensor according to a time evolution rule of the parameter set, and calculating knowledge density distribution of historical typical cases; setting a teaching anomaly discrimination boundary and generating an anomaly feature index set; capturing ultrasonic image flow and operation behavior data in teaching operation in real time, mapping the ultrasonic image flow and operation behavior data to a multi-scale teaching knowledge space, and calculating spatial distribution similarity with the abnormal feature index set to obtain a real-time teaching deviation coefficient; and constructing a teaching risk prediction network model, and generating a teaching operation quality evaluation result and a teaching strategy adjustment scheme.

An ideation platform device and method using a diagram are disclosed. An ideation platform device using a diagram, according to one embodiment of the present invention, can comprise: a C-K canvas module for providing a C-K canvas divided into a concept space and a knowledge space and connecting a concept and knowledge to each other on the C-K canvas through a chaining process so as to help a solution search for resolving a problem; and an instance management module for storing and managing, as one instance, the C-K canvas, for which a solution search is completed, including the concept, the knowledge, and information about an interconnection relationship.

The application relates to the technical field of intelligent medical teaching, and discloses a medical intelligent teaching model construction method based on ultrasonic AI technology. The method collects ultrasonic image sequence data of a target object and corresponding operation records, generates an original teaching data set, carries out multi-dimensional teaching feature analysis on the original teaching data set, extracts a dynamic teaching feature parameter set, constructs an anatomical structure evolution feature tensor according to the parameter set and the time evolution law, calculates the knowledge density distribution of historical typical cases, sets a teaching anomaly discrimination boundary and generates an abnormal feature index set according to the knowledge density distribution, captures ultrasonic image flow and operation behavior data in real time, maps the ultrasonic image flow and the operation behavior data to a multi-scale teaching knowledge space, calculates the spatial distribution similarity of the abnormal feature index set, obtains a real-time teaching deviation coefficient, constructs a teaching risk prediction network model, and generates a teaching operation quality evaluation result and a teaching strategy adjustment scheme.

A system capable of reasoning, learning, and imagination. The system includes an input / output module, a reasoning and decision agent, and a knowledge management module including a deliberation agent and a semantic knowledge space. This system uses prior knowledge stored in memory for reasoning and decision-making. It learns new domain knowledge and user behavior throughout operation, making it an evolving system that adapts to the user's needs. The system reinforces knowledge stored in its semantic memory without user intervention and imagines new relationships between existing concepts to search for novel ideas until it reaches an epiphany. Several embodiments of the disclosed system can interact in a collaborative environment for cross-domain reasoning and brainstorming new ideas.

The invention relates to a time-space dynamic evolution-oriented man-machine cooperation assembly cognitive inference method, which comprises the following steps of: extracting visual features in an assembly scene, generating a scene graph, constructing a time hyperedge, a space hyperedge and a task hyperedge, and fusing the three types of hyperedges to form a hyperedge set; time-varying non-pairwise relationships among human operators, robots, assembly operations and various assembly component nodes are represented, a hyperedge incidence matrix is defined, a man-machine cooperation assembly knowledge space-time hypergraph is constructed, and hyperedge representation among assembly components is realized; designing a stacked graph neural network with a self-excitation characteristic based on a multi-event hokes process, learning high-order task association among assembly nodes, and updating the change of the high-order task association along with time to realize space-time hypergraph representation; and modeling a self-excitation process among different sub-tasks, and capturing individual features and collective association to realize man-machine cooperation assembly. The problem that man-machine cooperation cognition in a time-varying task is difficult to infer is solved, and man-machine cooperation assembly efficiency and initiative are improved.

In one embodiment, a computer implemented method for generating personalized search results is disclosed. The method may include processing, via a processor, a search query of a user, modifying, via the processor, the search query based on educational competency data of the user, generating, via the processor, a search result based on the modified search query by performing a search operation in a knowledge space based on the modified search query to retrieve one or more content items of the knowledge space corresponding to the modified search query, generating, via the processor, a user interface configured to display the search result; and transmitting, via the processor, the user interface to a user device associated with the user.

The invention discloses an intelligent consultation method and system based on a dynamic legal knowledge graph and multi-modal recommendation, and relates to the technical field of intelligent legal consultation, and the method comprises the steps: constructing the dynamic legal knowledge graph which comprises a law provision layer, a case layer, a user consultation layer and a knowledge association layer, selecting a plurality of knowledge nodes in a legal knowledge space, the knowledge nodes comprise a law article node, a case node and a consultation demand node, and any knowledge node is selected as a coordinate origin to establish a knowledge association three-dimensional coordinate system. According to the technical scheme, external legal data are synchronized in real time through the update triggering unit, and recommendation of lagged content is avoided; the matching degree is calculated in combination with multi-modal features, the demand misjudgment rate is reduced, knowledge updating and topology reconstruction are optimized, irrelevant node calculation is reduced, calculation power consumption is reduced, a multi-modal recommendation result is provided, and information receiving habits of different users are adapted.

The application discloses a knowledge graph multi-hop reasoning method based on a Transformer deep reinforcement learning, and comprises the following steps: S1, a large number of RDF triples are accessed, a relationship is completed through relationship completion, and a knowledge graph with complete relationships is created; S2, representation learning is respectively performed on a topological structure and a triple semantic of the knowledge graph, representation tensors of the topological structure and the triple semantic are comprehensively combined in a linear combination manner, and a representation layer of the knowledge graph is mapped to a unified knowledge space; and S3, a GPT-2 model based on a Transformer decoding layer stack is used to model a reasoning layer of the knowledge graph, and a bundle search is used to generate a next time step action A T+1 The application avoids defects such as uncontrollable design of behavior strategy constraints and distribution of confidence by using artificial experience in traditional reinforcement learning.

The application provides a method and device for establishing a clinical auxiliary diagnosis and treatment professional large model, relating to the field of artificial intelligence technology of large language models, comprising: deconstructing professional literature into knowledge points according to the knowledge characteristics of clinical diagnosis and treatment, and mapping the knowledge points to a knowledge space; constructing a question paradigm, which is used to constrain the coordinate mapping relationship between the natural language expression of the question and the knowledge space; and establishing a clinical auxiliary diagnosis and treatment professional large model according to the knowledge space and the question paradigm. In this way, common clinical diagnosis and treatment questions are mapped to knowledge space coordinates, and knowledge points in professional literature are matched based on the knowledge space coordinates, thereby realizing accurate positioning retrieval of the question content and providing relevant recommendations based on the spatial position relationship between the knowledge points.

The application discloses a plot multidimensional portrait and evaluation method fusing space data and industry knowledge base, and belongs to the field of data analysis.The application comprises the following steps: collecting multi-source plot space data and industry knowledge base data, and constructing a standardized space database and an industry knowledge graph; calculating a knowledge-space coupling fusion degree based on a three-field coupling model of a semantic field, a topological field and a constraint field; decomposing a user intention into a multilevel structure, and calculating a multilayer matching degree of the intention and the knowledge base through knowledge graph penetration and propagation; automatically screening adaptive evaluation indexes and determining dynamic weights of each dimension based on a three-force field dynamic balance model of intention traction, knowledge constraint and space differentiation; performing comprehensive portrait calculation based on a three-dimensional coordination model; calculating a multi-channel knowledge iteration gain based on three-channel gain, and updating knowledge base and fusion model parameters and feeding back for closed-loop iteration optimization.

This invention relates to the field of intelligent decision-making technology in emergency management, and provides an intelligent emergency decision support method based on a hybrid agent network. The method includes: constructing a decision knowledge space containing a knowledge system of the emergency domain; storing knowledge units in vector form to create a knowledge base supporting semantic retrieval; upon receiving disaster event information, a planning agent decomposes and orchestrates the decision task, generating a task orchestration scheme containing multiple sub-tasks and their dependencies; an analysis agent performs multi-dimensional analysis of the decision task according to the task orchestration scheme; a generation agent summarizes the analysis results; and, combined with a business knowledge system, generates disposal suggestions and a decision report. This invention achieves deep integration of business knowledge and AI capabilities, collaborative decision-making among multiple types of agents, and adaptive orchestration of the decision process, thereby providing decision-makers with comprehensive, accurate, and scientific decision support during the critical time following a disaster.

The application relates to an athlete action biomechanics evaluation method, system, device and medium based on deep learning, the method comprising: obtaining a motion signal sequence by time synchronization and cycle segmentation of a multi-modal technical action signal of an athlete, generating a joint representation tensor by kinematics and dynamics feature fusion, obtaining a bottom feature map by spatiotemporal coding of fused individual morphological parameters, performing cross-attention decoding based on a pre-constructed biomechanics semantic knowledge space to generate a semantic state matrix and a concept activation sequence with a concept node as a query vector, generating an evaluation result along a causal topological structure, and outputting an interpretable biomechanics evaluation report through natural language generation. The method can improve the interpretability, causal tracing ability and training guidance value of the athlete action biomechanics evaluation.

The application provides a clinical auxiliary diagnosis and treatment professional large model evaluation method and device, relates to the artificial intelligence technical field of large language models, and comprises the following steps: constructing a test data set according to the priority order of the main dimension of a knowledge space, a question paradigm form, a question paradigm variable value, and a synonym expression of a question paradigm; inputting the test data set into the clinical auxiliary diagnosis and treatment professional large model for processing to obtain the answer content of each test question in the test data set; evaluating the correctness of the answer content by a business expert, and determining the confidence of the clinical auxiliary diagnosis and treatment professional large model according to the error rate of the evaluation result.

The application provides a document interaction processing method, device and equipment for a large language model, the method comprising: generating a plurality of target sub-tasks according to a target task input by a user; determining, for each target sub-task, a plurality of sub-queries corresponding to the target sub-task; determining a sub-knowledge space corresponding to the target sub-task according to a hybrid index pre-constructed for a document and the plurality of sub-queries corresponding to the target sub-task; and inputting the plurality of sub-knowledge spaces corresponding to the plurality of target sub-tasks into the large language model to generate a response result corresponding to the target task. The method of the application can effectively reduce the difficulty of subsequent retrieval and reasoning by task splitting, converting a large-scale and fuzzy target task into a small-scale and clear target sub-task, and can solve the problem that a complex task is directly processed and a reasoning chain is easily broken. In addition, the hybrid index is constructed based on a dense semantic vector index and a sparse keyword index for a document, which can effectively avoid the problem of low recall rate or poor precision of a traditional single index, ensure that the retrieval result is both comprehensive and accurate, solve the contradiction between retrieval precision and efficiency, enable the system to maintain global perception and local accuracy when processing long documents and heterogeneous documents, and overcome the limitation of traditional dense retrieval which only focuses on local semantics.

This invention discloses a teaching optimization method, system, device, and medium based on a knowledge space. The method specifically includes: using the National Industrial Internet Identifier Resolution Secondary Node Platform, parsing official textbooks to generate knowledge point entities with identifier codes and establishing a relationship network; based on the relationship network, collecting problem-solving steps from multi-platform question banks, authenticating them using an identifier code matching mechanism, and synchronizing the authenticated steps to the basic knowledge space and marking them as extended knowledge point databases, while simultaneously calculating the difficulty threshold of knowledge point association combinations based on error statistics; based on the relationship network, capturing learners' cross-platform problem-solving steps in real time, authenticating them using an identifier code matching mechanism, and marking learners as not having mastered the preceding knowledge points if they fail authentication, generating a learner's problem-solving knowledge space with status markings. This invention achieves comprehensive analysis of learners' knowledge point mastery, difficulty identification, and generation of personalized teaching optimization suggestions, improving teaching quality and learning outcomes.

The application discloses a kind of mobile robot adaptive navigation methods for continuous task, the method of the application organizes as task stream for continuous incoming navigation task, obtains the state observation of robot and task semantic information;Potential task variables are constructed by task encoder;Dynamic knowledge space is established based on Dirichlet process mixture model, online update is carried out using memorized variational inference, and the birth and merging of knowledge cluster are realized;Potential task variables are input as context condition into downstream soft actor critic policy network, and adaptive navigation action is generated.The application can continuously accumulate and reuse navigation knowledge in long-term task stream, effectively alleviate catastrophic forgetting, improve the learning efficiency and navigation success rate of new task, and ensure the long-term stable operation of mobile robot under continuous task conditions.

The invention provides a large language model-oriented document interaction processing method, device and equipment, and the method comprises the steps: generating a plurality of target sub-tasks according to a target task input by a user; for each target sub-task, determining a plurality of sub-queries corresponding to the target sub-task; determining a sub-knowledge space corresponding to the target sub-task according to a hybrid index pre-constructed by the document and a plurality of sub-queries corresponding to the target sub-task; and inputting the plurality of sub-knowledge spaces corresponding to the plurality of target sub-tasks into the large language model to generate response results corresponding to the target tasks. According to the method provided by the embodiment of the invention, a large-scale and fuzzy target task is converted into a small-scale and clear target sub-task through task splitting, so that the difficulty of subsequent retrieval and reasoning can be effectively reduced, and the problem that a reasoning chain is easy to break when a complex task is directly processed is solved. Besides, the hybrid index is constructed based on the dense semantic vector index and the sparse keyword index of the document, so that the problem of low recall rate or poor precision of the traditional single index can be effectively avoided, the retrieval result is ensured to be comprehensive and accurate, the contradiction between the retrieval precision and efficiency is solved, and the retrieval efficiency is improved. The system can keep global perception and local accuracy when processing long documents and heterogeneous documents, and the limitation that only local semantics are concerned in traditional dense retrieval is overcome.