12 results about "Knowledge representation and reasoning" patented technology

The invention discloses a power grid fault rapid diagnosis method based on an artificial intelligence technology, and relates to the field of power grid fault diagnosis, and the method comprises the steps: S1, carrying out the feature extraction of a power grid fault based on quantum calculation; s2, constructing a power grid knowledge graph; s3, mapping entity nodes in the power grid knowledge graph to a quantum state according to key attribute information of the entity nodes, and designing quantum gate operation for encoding according to a relationship among the entity nodes in the power grid knowledge graph; s4, performing fault diagnosis; and S5, performing feedback and optimization based on the power grid knowledge graph. According to the rapid power grid fault diagnosis method based on the artificial intelligence technology, the high efficiency of the quantum calculation in the aspects of data processing and feature extraction and the advantages of the knowledge graph in the aspects of knowledge representation and reasoning are brought into full play through the power grid fault diagnosis method based on fusion of the quantum calculation and the knowledge graph; rapid and accurate diagnosis of power grid faults can be realized, and a guarantee is provided for safe and stable operation of a power grid.

The invention provides a virtual classroom interaction method and system based on knowledge representation and reasoning, and the method comprises the steps: obtaining a student interaction data flow in a power system training virtual classroom, carrying out the knowledge granularity decomposition of the student interaction data flow, and generating a knowledge tuple set containing concept nodes and relation edges, and inputting the knowledge tuple set into an inference engine to execute multi-dimensional inference processing, generating a student knowledge mastering state vector and a learning demand prediction vector, executing interaction content generation processing based on the knowledge mastering state vector and the learning demand prediction vector, generating classroom interaction response data with adaptability, and sending the classroom interaction response data to the student. And feeding back the classroom interaction response data to a virtual classroom interaction interface to form a dynamic interaction process of the student learning state and classroom feedback. According to the invention, the pertinence and practicability of virtual classroom interaction are effectively enhanced, so that the learning effect of students in power system training is improved.

The method comprises the steps of obtaining a to-be-uploaded file and a corresponding file type, preprocessing the to-be-uploaded file, generating a to-be-audited file, calling a corresponding auditing model in an AI knowledge base for auditing according to the type of the to-be-audited file, and generating a corresponding auditing result; the multi-hop query request of the user is obtained according to the auditing result, a modification suggestion is generated, and finally the modification suggestion is returned to the user and is encrypted and stored, and more intelligent and accurate auditing is achieved by means of the powerful knowledge representation and reasoning ability of the knowledge base. The modification suggestions provided when the auditing is not passed can help the user to quickly understand the problem and perform targeted modification, so that the efficiency of the content creator is improved, the times of repeated submission and auditing are reduced, the auditing cost is reduced, and the quality and efficiency of the whole auditing process are improved.

The invention relates to the field of knowledge maps, and provides a method, a system, equipment and a medium for dynamically generating a knowledge map, and the method comprises the following steps: obtaining a plurality of data sources and interest words of a user, and filling the interest words into a preset list template to obtain an entity type list; performing cross-domain reasoning classification on the entity type list in the plurality of data sources through a preset integrated large model to obtain entities corresponding to the entity type list; combining the interest words in the entity type list, and constructing a vector list of nodes and edges based on the combined entity type list and the corresponding entities; and generating the knowledge graph according to the vector list of the nodes and the edges through a preset increment and an interactive knowledge graph template. According to the method, the large language model is finely adjusted, so that the large language model is more suitable for knowledge representation and reasoning tasks in a specific field, and a user can obtain required information from different dimensions.

The invention belongs to the technical field of artificial intelligence and education crossing, and relates to an information security professional education system and method based on big language model agency, and the system comprises a data collection and storage module which is used for collecting and storing information security professional student data and teaching data; the user modeling and state management module is used for constructing multi-dimensional individual portraits and performing state management on the multi-dimensional individual portraits according to the characteristics of students professional for information security; the intelligent agent module is used for constructing a multi-agent large language model agent for an information security professional teaching scene; and the information security field professional knowledge representation and reasoning module is used for constructing a time-sequenced and reasonable knowledge system oriented to information security education so as to provide knowledge support for a multi-agent large language model agent. According to the method, a sequential and reasonable knowledge system oriented to information safety education is innovatively constructed, and accurate, time-efficient and reasonable knowledge support can be provided for teaching tasks through a retrieval enhancement generation mechanism.

An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

The invention provides a soft knowledge-oriented entity relationship reasoning method, which is based on a traffic knowledge representation learning model of a multi-gating mechanism and entity object attribute information, and is specially used for soft knowledge representation and reasoning problems. Compared with the prior art, the method has the advantages that external noise interference is avoided only by utilizing own attributes, attribute values and neighborhood subgraph information of the entities; key semantics are dynamically perceived through multiple gating mechanisms such as an input gate, an output gate and an attribute value perception gate and an attention mechanism, and the robustness and interpretability of the model are enhanced; key attributes are pre-selected by adopting an analytic hierarchy process (AHP), so that the training efficiency is remarkably improved; according to the method, neighborhood subgraph structure information is fused, the knowledge reasoning ability is enhanced, and the method is suitable for real-time decision-making scenes in the fields of traffic and the like and is superior to a traditional method in the aspects of field applicability, noise robustness, calculation efficiency and the like.

The application discloses a kind of city resilience dynamic early warning method and system based on knowledge graph, belong to knowledge representation and reasoning technical field, specifically as follows: obtaining the crowd distribution data of carrying multiple personnel identity tags during target activity, and the operation state data of target facility;The above data is respectively loaded to the service layer and the physical layer of the preset knowledge graph, obtain the initial state value of each node, and determine the dependence intensity coefficient of mapping relationship from the personnel identity tag of load node in the support scheme;Receive target disturbance event and determine disturbance node;Disturbance node, initial state value and dependence intensity coefficient are input into pre-trained graph neural network model, with dependence intensity coefficient as cross-layer edge weight, perform alternating state update between physical layer and service layer until the preset convergence condition is met, to generate city resilience early warning signal.Therefore, by implementing the present application, the accuracy of city resilience early warning under major activity support scenario can be improved.

The present application relates to the technical field of artificial intelligence, and provides a large language model retrieval augmentation solution. In the present solution, text data of a target vertical domain is acquired, and a domain knowledge graph containing entities, attributes and entity relationships is constructed, so as to implement professional knowledge structured representation; and a large model auxiliary component is trained by means of a deep learning model, so as to assign a domain entity recognition capability and a question intent recognition capability to the large model auxiliary component. The trained large model auxiliary component is integrated into a target large language model to enable a vertical domain semantic parsing capability. The domain knowledge graph provides domain fine-tuning information to perform domain fine-tuning on the target large language model to generate a retrieval augmented large language model. The retrieval augmented large language model performs vertical domain professional question parsing, key entity identification and knowledge retrieval within the range of artificial intelligence technologies such as semantic understanding, knowledge representation and reasoning, natural language processing, probabilistic reasoning and cluster analysis, and supports multiple types of professional knowledge question answering and intelligent retrieval scenarios.

An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

The application provides a virtual classroom interaction method and system based on knowledge representation and reasoning, wherein student interaction data flow in a power system training virtual classroom is acquired, knowledge granularity decomposition processing is performed on the student interaction data flow, a knowledge tuple set containing concept nodes and relationship edges is generated, the knowledge tuple set is input into a reasoning engine to perform multi-dimensional reasoning processing, a student knowledge mastery state vector and a learning demand prediction vector are generated, interaction content generation processing is performed based on the knowledge mastery state vector and the learning demand prediction vector, classroom interaction response data with adaptability is generated, the classroom interaction response data is fed back to a virtual classroom interaction interface, and a dynamic interaction process of student learning state and classroom feedback is formed. The application effectively enhances the pertinence and practicality of virtual classroom interaction, thereby improving the learning effect of students in power system training.

The invention provides a safety production knowledge graph construction system and method based on a large language model, and belongs to the technical field of knowledge representation and reasoning, and the system comprises a data collection module which is used for receiving data in the safety production field and constructing a basic data set; the data extraction module is used for calling the large language model and outputting a to-be-extracted data set based on the basic data set; the knowledge extraction module is used for extracting knowledge elements from the to-be-extracted data based on the extraction template; the knowledge fusion module is used for performing conflict resolution and screening on the knowledge elements based on a risk level priority rule to obtain a standardized knowledge set; the storage module is used for receiving the standardized knowledge set and constructing and storing three-dimensional diagram structure data; and the graph generation module is used for generating a risk visualization knowledge graph based on the three-dimensional graph structure data and the large language model. According to the method, knowledge graph optimization can be realized by integrating multi-source data and enabling a large model.