159 results about "Graph query" patented technology

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses a new energy equipment troubleshooting method based on a knowledge graph and a large model. The method is used for solving the problems of fault positioning and troubleshooting of photovoltaic and wind power new energy equipment and the like. The method mainly comprises the following steps: performing data cleaning and preprocessing on a new energy equipment troubleshooting professional field document, and constructing a structured knowledge graph of equipment types, fault types, fault information and solution nodes in a graph database through Cypher; the big language model analyzes the user fault description, extracts and maps node attributes in the knowledge graph, and generates a graph query statement; the question-tracing type multi-round interaction is realized, the missing information is dynamically complemented, and the basic fault is accurately positioned in a step-by-step reasoning mode; according to the method, multi-modal resources such as texts, schematic diagrams and videos stored in the knowledge graph are combined, a step-by-step reasoning path and a solution with illustrative features are generated through a preset template, the whole reasoning process is visualized in a branch thinking graph form, and the accuracy, interpretability and user experience of troubleshooting are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

Described are examples for creating a graph model for data obtained from multiple data sources and stored in a common data format. A selection of columns of data stored in the common data format that are to be associated with one another in the graph model can be obtained. Based on the selection, a graph model can be created including metadata that defines a list of vertices corresponding to at least a portion of the columns and a list of edges between two or more of the vertices identifying a relationship between corresponding columns. A graph query or graph algorithm can be performed over the graph model of the data as stored in the common data format without requiring copying of the data to a separate graph database.

The invention relates to the technical field of equipment control, and provides an equipment control method and system based on a natural language and electronic equipment. According to the application, the machine language vector is generated through the intention recognition result of the natural language instruction, and the graph query statement is generated according to the semantic concept after the machine language vector is deconstructed, so that the equipment knowledge graph of the original equipment is queried through the graph query statement to obtain the equipment operation path meeting the semantic concept; the knowledge graph has a mesh topology structure, the equipment knowledge graph is used for replacing a rigid relational database structure, deep requirements in natural language instructions are mined through intention recognition, machine language vectors are obtained, the machine language vectors are deconstructed, and the control of the original equipment is achieved. And generating a graph query statement corresponding to the equipment knowledge graph according to the deconstructed semantic concept, thereby accurately and comprehensively querying the complex context semantic relationship between the nodes, and improving the understanding accuracy of the natural language instruction.

The invention belongs to the technical field of map construction, and discloses an online vectorization map construction method based on dynamic query management. Extracting perspective image features; generating aerial view view angle features based on the perspective image features; initializing map element query; inputting the initialized map element query and the aerial view view angle features into a map decoder based on dynamic query management, and optimizing the map element query; taking the output result of the Lth layer of the map decoder based on the dynamic query management as the final prediction of the attribute of the map element; and carrying out loss calculation and training. By optimizing and dynamically managing map queries in a map decoder, the computational consumption of low confidence map queries is effectively reduced. Under the same computing resources, the model is allowed to process a larger number of map queries, so that the construction precision of the vectorized map is improved. The research of automatic driving on-line map construction is further promoted, and the deployment of an on-line vectorization map model is facilitated.

The invention discloses a dynamic knowledge graph construction and application method, system and device for energy policy influence conduction analysis and a medium, and the method comprises the steps: carrying out the multi-dimensional knowledge extraction of an energy policy text through employing a first adaptive extraction model, and outputting a structured knowledge triple; constructing a dynamic multi-subject knowledge graph; performing deduction search on the potential association path through a first influence search algorithm, and identifying a key policy influence conduction chain; an event-driven knowledge graph closed-loop self-evolution mechanism is established, policy change events are captured in real time through automatic monitoring, and incremental updating and structure correction of the dynamic multi-subject knowledge graph are triggered; and constructing a scene-oriented personalized application service interface according to the dynamic multi-subject knowledge graph, converting a user natural language query into a graph query instruction, and generating a scene response in combination with a retrieval result. According to the invention, the cost of manual arrangement and maintenance is obviously reduced, and the working efficiency and expandability of the system are improved.

The invention discloses an airport apron operation intelligent question answering system and method based on knowledge graph reasoning, and belongs to the technical field of knowledge graphs and artificial intelligence. The method comprises the following steps: constructing an airport apron operation knowledge graph by fusing multi-modal data sources; analyzing a user query intention by using a large language model, realizing alignment of the oral entity and the map standard entity, and generating a standardized query; decomposing the standardized query into an ordered subtask sequence based on thinking chain reasoning, and generating a tool calling plan; and executing the tool calling plan, obtaining a fact basis by sequentially calling a graph query tool, and generating an interpretable answer in combination with a reasoning path. According to the method, multi-modal source data can be deeply fused, a high-precision domain knowledge graph is automatically constructed, and on the basis, an interactive, reasonable, safe and credible intelligent question and answer system is provided.

In general, this disclosure describes techniques for analyzing anomalies in a network. In an example, a method comprises obtaining, by a system, a graph query and a network graph for a network, wherein the network graph includes one or more nodes having one or more properties that indicate a plurality of anomalies for the network; executing, by the system, the graph query on the network graph for the network to determine a matching subgraph of the network graph, wherein the graph query matches on the one or more nodes and the one or more properties; and based on the determination of the matching subgraph, outputting an indication of an association of the plurality of anomalies.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.

The invention belongs to the technical field of data retrieval, and particularly discloses an inquiry method and system based on a vector database and graph query, a terminal and a medium, and the method comprises the following steps: importing structured data into a graph database, and constructing a knowledge graph with entity nodes and edge relationships; generating a first semantic vector through the semantic coding model, storing the first semantic vector into a vector database, and establishing an index; a user inquiry text is received, and after sensitive information detection and context analysis are executed, a second semantic vector is generated through the semantic coding model; performing similarity retrieval in a vector database based on the second semantic vector to obtain a plurality of candidate node identifiers; executing query in a limited range by taking the candidate node as a starting point in the graph database, and extracting associated nodes and relationships to form a sub-graph; performing joint scoring on the sub-graph structures, and sorting the paths based on a scoring result; and generating and outputting an answer text according to the sorting result. The problem that in the prior art, semantic understanding and relation reasoning are disjointed and difficult to co-process is solved.

The invention provides a civil aircraft fault information retrieval method and system based on a knowledge graph and vector retrieval. The method comprises the following steps: firstly, carrying out batch vectorization processing on knowledge in a knowledge graph library, and constructing a vector index library; after a user query is received, the system preprocesses the user query and extracts information by adopting a hybrid strategy: special codes are accurately matched and recognized through a dictionary, and meanwhile, natural language description is converted into query vectors by utilizing a deep semantic model. Then, the system executes two kinds of retrieval in parallel: one is structured graph query based on accurate information, and the other is semantic similarity search in a vector index database; and finally, performing intelligent fusion and sorting on results returned by the two kinds of retrieval. By combining the accuracy of structured query and the recall capability of vector retrieval, the problems of various expressions, fuzzy semantics and the like in civil aircraft fault information query are effectively solved, the accuracy and comprehensiveness of fault information retrieval are remarkably improved, and more accurate and efficient decision support is provided for aircraft maintenance personnel.

The invention belongs to the technical field of computer data processing, and particularly relates to a dynamic RAG-NL2SQL system based on a real-time data blood relationship map. Comprising the steps that a cost-delay sensing intelligent routing module receives a user question, carries out macroscopic element decision making and calls related modules for execution; the real-time data blood relationship map construction and maintenance module constructs and dynamically maintains a real-time data blood relationship map; the intention-blood relationship dynamic retrieval enhancement module queries a data source sub-graph matched with a user question from a real-time data blood relationship graph and serializes the data source sub-graph into text description; the traceable SQL generation and closed-loop verification module generates an SQL according to the text description and the original problem of the user, executes and verifies an execution result, and starts a closed-loop verification and repair process if verification fails; the zero sample memory bank multiplexing module constructs the verified query into a memory unit, and dynamically evaluates the memory unit; semantic multiplexing and intelligent elimination are carried out according to an evaluation result; the automation level and the overall reliability of the system are improved.

The invention discloses a medical data storage method based on semantic recognition, and belongs to the technical field of data processing, and the method comprises the steps: receiving medical data, carrying out the parallel processing of an unstructured medical text in the medical data through a large language model, and generating a structured semantic triple and a semantic vector representing the overall semantics; constructing a semantic graph index and a vector index to form a mixed index structure; the method comprises the following steps: receiving a natural language query, analyzing the query into a structured query condition and a query vector, forming an executable multi-modal query instruction, executing atlas query and a vector similarity search process in parallel, and mapping the structured query condition to a semantic atlas execution path for matching. In the implementation process of the technical scheme, natural language processing and a large language model are introduced, entity recognition, relation extraction and semantic role labeling are conducted on medical unstructured texts, and structured conversion of key information such as illness state description, diagnosis conclusions and treatment schemes is achieved.

The invention provides a method and device for medical question answering, electronic equipment and a medium, and relates to the field of artificial intelligence, in particular to the field of knowledge maps and large language models. The method can comprise the steps of obtaining an intention label and an intention confidence coefficient through a first large language model based on a query text from a user; in response to determining that the intention label is not matched with the intention in the intention list or the intention confidence does not meet a confidence threshold, executing a first query process to obtain a query result; and obtaining a reply text for the query text based on the query result, the first query process comprising: executing vector retrieval based on the query text to obtain a first sub-query result; generating a graph query statement by using a second large language model based on the query text; and obtaining a query result at least based on the first sub-query result.

Systems and methods are directed to incorporating large language model (LLM) prompts into graph queries using graph query language and executing these graph queries. The system receives a graph query that includes a LLM prompt in the graph query language. The LLM prompt comprises an input context and a query to be answered. Based on the graph query, the system traverses a knowledge graph to obtain context data associated with the input context. The context data may be constrained by access control policies of an enterprise providing the knowledge graph. A context table may be generated using the context data based on the input context indicated in the LLM prompt. The system then invokes an LLM to determine a result by applying the query of the LLM prompt to the context data obtained from the traversal of the knowledge graph. The result is then presented on a user interface of a client device.

The application provides a knowledge graph-based information query method and device, equipment and a storage medium. The method comprises: constructing a target entity vector of a to-be-queried text according to an entity word in the to-be-queried text and an entity type corresponding to the entity word; determining a target query mode corresponding to the target entity vector, and obtaining a graph feature item corresponding to the target query mode; wherein the target query mode is used to indicate a query process of the to-be-queried text in a knowledge graph; associating the entity word to the corresponding graph feature item by using the target query mode; generating a corresponding graph query statement according to the target query mode and the graph feature item associated with the entity word; and querying the knowledge graph according to the graph query statement to obtain a corresponding first query result. The method provides the accuracy of the query result.

Two modes of result set rebalancing are provided to cover the needs of distributed graph engines: unordered and ordered rebalancing. Unordered rebalancing allows moving any combination of chunks from one machine to another with the only goal being to reach a target balance. Ordered rebalancing maintains the order of a result set, dividing and rebuilding new chunks as they are moved between consecutive machines in the order of the result set to reach the target balance. The rebalancing solutions capitalize on the internal structure of result sets commonly present distributed graph processing systems.

The application discloses a kind of based on graph structure's power grid voltage stability margin online prediction method, comprising the following steps: constructing power grid attribute graph model in graph database, and establishing event-driven mechanism to realize the incremental update of topological change;Based on parameterized graph query template, dynamically generate graph database query statement, obtain the node feature matrix and adjacency matrix of prediction time, and convert to sparse tensor format;Constructing spatio-temporal graph convolution network prediction model;Through the incremental update of topological change driven by event-driven mechanism, to drive spatio-temporal graph convolution network prediction model;The prediction result and model intermediate layer node embedding vector are written back to graph database as attribute.This application realizes the millisecond level online prediction of power grid voltage stability margin, meets the real-time, accuracy and adaptability requirements of power system online safety analysis.

The invention relates to identifying associated anomalies of critical anomalies in a network. In general, this disclosure describes techniques for analyzing anomalies in a network. In an example, a method includes obtaining, by a system, a graph query for a network and a network graph, where the network graph includes one or more nodes having one or more attributes indicative of a plurality of anomalies of the network; performing, by the system, a graph query on a network graph of the network to determine a matching sub-graph of the network graph, where the graph query matches the one or more nodes and the one or more attributes; and outputting an indication of association between the plurality of anomalies based on the determination of the matching sub-graph.

The invention discloses a knowledge graph-based government affair retrieval enhancement generation framework and method, and relates to the technical field of natural language processing. The method specifically comprises the steps that fine adjustment is conducted on a basic model through government affair corpora, and a government affair field enhanced semantic model is generated; vectorizing a government affair knowledge graph entity by using a semantic model enhanced in the government affair field and storing the vectorized government affair knowledge graph entity into a graph entity vector database; key government affair entities are extracted from user questions, semantic matching is carried out on the extracted government affair entities and graph entities, and accurate context knowledge is retrieved from the knowledge graph through automatically generated graph query statements; and fusing the retrieved context knowledge with the original question to construct an enhanced prompt, and driving a large language model to generate a high-quality natural language government affair answer. According to the method, the accuracy of government affair questions and answers, the understanding depth of complex government affair logic and the retrieval efficiency are remarkably improved by deeply fusing the structural advantages of the knowledge graph and the semantic ability of the large language model.

The invention relates to an overtaking intention decision evaluation method based on data driving, and the method comprises the steps: obtaining environment information which comprises the historical sequence information of a dynamic object, the current state information of a static object, lane information, speed limiting information and vehicle state information; inputting the environment information and the vehicle state information into a preset deep learning network; performing feature coding on the input information through a deep learning network to obtain comprehensive environment features; performing cross attention decoding on the comprehensive environment feature and a plurality of predefined overtaking intention query vectors to obtain a decoded feature; and calculating and outputting a decision probability corresponding to each overtaking intention based on the decoding features. The invention also relates to a corresponding system, device, processor and storage medium. Through multi-source feature fusion and a cross attention mechanism, the association between the environment information and the overtaking intention can be fully mined, the intention prediction precision and the scene adaptability are significantly improved, and a reliable basis is provided for the safety decision of the autonomous vehicle.

The invention belongs to the technical field of intelligent question answering, and particularly relates to an experimental question answering method and system based on a knowledge graph. The method comprises the following steps of: generating a candidate combination category grammatical analysis tree for an input experimental question statement, and when vocabularies outside a preset dictionary are encountered, assigning a temporary combination category in combination with word vectors and adjacent entity types; calculating the syntactic probability of the analysis tree and the node intensity of the entity subgraph in the tree, determining a target syntactic analysis tree, and converting the target syntactic analysis tree into a logic expression; extracting an anchor point entity and a semantic relationship, determining a search depth according to sub-graph intensity, and constructing an initial graph query statement by utilizing bidirectional breadth-first search; if the query result does not exist, differential query reconstruction is carried out according to an intensity threshold value, when the intensity is high, only the semantic relation is replaced with a parent class, otherwise, the parent class and brother class relation is considered at the same time, and a sub-graph or an entity obtained through query is output as an answer. According to the method, the suitability, accuracy and fault tolerance of the experiment question-answering system can be improved, and the query efficiency and the recall rate are balanced.

Embodiments of the present application provide a kind of based on graph's alarm event determination method, device, electronic equipment and storage medium, method includes: the security log is converted into the target graph information corresponding to the security log;Based on the target graph information and the graph rule configuration information obtained in advance, generate first graph query statement;The graph rule configuration information at least includes: the rule for generating the first graph query statement;The graph information matching operation is carried out between the first graph query statement and the graph information in graph database;The graph database is used to store at least one kind of graph information;According to the graph information matched to the first graph query statement, generate alarm event.The embodiments of the present application realize the association of security log before and after and the association between multi-source security log by graph database, can effectively reduce false alarm rate;In addition, based on the high efficient query ability of the association relationship of graph database itself, the low time and low resource consumption of the association analysis of multiple security logs can be realized.

The application discloses a natural language graph query generation method based on a knowledge hypergraph, performs semantic analysis and entity standardization on an input natural language query to obtain standard entity identifiers involved in the query; takes the standard entity identifiers as anchor points to extract a local hyper-subgraph containing semantic hyper-edges and associated entity nodes from the knowledge hypergraph, wherein the semantic hyper-edges are used to express a complex semantic association involving multiple entities, roles and constraints; performs structured decomposition on the natural language query to generate multiple sub-questions with semantic boundaries, and maps each sub-question to one or a group of the semantic hyper-edges, while constructing a directed dependency relationship between the sub-questions; and the application aims to solve the problem of how to accurately identify and express the nested dependency relationship between multiple entities, roles and constraints in the natural language query while preserving the complete semantic structure of the natural language query, and ensure that the dependency can be completely transmitted and verified in a structured manner.

The invention discloses a multi-mode three-dimensional target detection system and method based on 3D Gaussian sputtering. The system comprises a bimodal feature extraction module, a vector-matrix Gaussian representation module and a graph query detection network. The method comprises the following steps: firstly, inputting a LiDAR point cloud projection and an RGB image to a bimodal feature extraction module, and outputting a feature matrix group; transmitting the generated feature matrix to a vector-matrix Gaussian representation module, decomposing the feature matrix into a vector and a matrix, and generating a 3D Gaussian field through VMGS; transmitting the generated 3D Gaussian field to a graph query detection network, analyzing the Gaussian field, and outputting a target category probability and a 3D bounding box parameter; and finally, recording detection related data on a target detection result display, and evaluating the performance of a detection algorithm. According to the method, model parameters and calculation complexity of multi-modal three-dimensional target detection are reduced, target detection efficiency and accuracy are improved, and the method has effectiveness and robustness in various complex scenes.

Embodiments of the present application provide a map query method and an electronic device, the method comprising: receiving, by the electronic device, a first query request; displaying a first interface, the first interface comprising a first search result, the first search result comprising at least one geographic location related to the first query request within a map range indicated by a first map region; displaying a second interface, the second interface comprising a first history record, the first history record being associated with the first query request; receiving a second query request based on the first history record; and displaying a third interface, the third interface being the same as the first interface. With the embodiments of the present application, the search result obtained in previous queries can be reproduced when querying based on the history record, the user operation is reduced, and the search result is more accurate.

The present application belongs to but is not limited to the technical field of shale gas production, and particularly relates to a method and system for ontology graph construction and abnormal cause-effect chain tracing, comprising: S1, well, pry, process, pressure difference, alarm, operation event object and relationship mode; S2, cause-effect chain coding: forming a calculable path of 'upstream equipment-process-downstream abnormality'; S3, realizing bidirectional linkage of ontology graph and detection result. The ontology knowledge graph of different business scenarios in the field of shale gas is constructed, and the well, pry, process, sensor entity and their relationships are defined; when the abnormal detection model outputs the result, the result is mapped to the graph; through graph query or graph traversal algorithm, the upstream and downstream associated entities of the abnormal node are automatically traced, and natural language explanation is generated; an interface supporting semantic query is provided, and the user is allowed to ask questions in an exploratory manner.

This application discloses a frequency class graph query method and related apparatus, electronic device, and storage medium. The frequency class graph query method includes: setting a frequency field for each relation edge in a knowledge graph based on the entities at both ends of the relation edge; creating a first information database in the index backend based on the frequency field of the relation edge in the knowledge graph; wherein the first information database includes at least the frequency field of the relation edge in the knowledge graph and the index name; responding to a frequency class query request, constructing a frequency class query statement based on the frequency class query request, assembling a query instruction suitable for the index backend based on the frequency class query statement, and querying the first information database based on the query instruction to obtain the frequency query result. This solution can improve the efficiency of frequency class graph queries.

The invention discloses a rare earth industry-oriented big data semantic retrieval method and system based on a knowledge graph, and belongs to the technical field of knowledge management and intelligent retrieval. In order to solve the problems that traditional keyword retrieval is insufficient in accuracy, incapable of understanding semantics and high in use threshold, the method comprises the following steps: establishing a rare earth industry field knowledge graph, defining entities such as materials, processes, equipment and suppliers and association relationships thereof, and extracting data from a manufacturing execution platform, a resource planning platform and a supplier collaboration platform; carrying out intention recognition and entity extraction on the natural language retrieval request by adopting a deep language understanding model, and converting the intention recognition and entity extraction into a graph query statement; semantic reasoning and associated query are performed based on the knowledge graph, a related result is returned in combination with a vector retrieval technology, and intelligent recommendation is provided. According to the method, the retrieval accuracy is improved by more than six percent, a user can perform natural language retrieval without knowing a data structure, and the method is suitable for scenes such as rare earth enterprise knowledge query, data analysis assistance and intelligent question and answer.