37 results about "Graph operations" patented technology

Techniques are described for applying topological graph changes and traversing the modified graph. In an implementation, a set of compile processes schedules the graph changes caused by a DML (Data Manipulation Language) statement. Based on the requested graph operation in a received query for graph, a set of graph operation processes generate extensions to the graph that capture the changes to the graph by the DML. The received graph operation(s) are then performed by traversing both the existing graph and the generated extensions.

The embodiment of the invention provides a permission topological structure construction method and device based on the lowest permission, and the method comprises the steps: carrying out the structural analysis and classification of request data from an identity management platform, carrying out the parameterized replacement, obtaining an unpublished interface path template, and storing the unpublished interface path template in a database; establishing a mapping relationship between the unpublished interface path template and a preset identity management platform permission model to obtain an unpublished interface parameter structure, and fusing the unpublished interface parameter structure with the existing interface parameter structure to obtain a centralized interface parameter structure; under the lowest permission role, starting from an initial node of the centralized interface parameter structure according to a task scheduling engine and a graph operation model, performing multi-hop breadth traversal along a member relationship, role binding and a permission operation path, determining a corresponding permission map, performing fine-grained dependency extraction on the permission map, and updating the permission map according to the dependency relationship, according to the method and the device, the security and the management efficiency of the identity and access management platform can be improved.

Autonomous management of risk transfer is provided using an automated underwriting processor that creates a contract block by compiling the request into a computational graph-based format, links the contract block to the requester, stores the contract block into memory, retrieves a plurality of available underwriting agreements from memory, and creates an offer list by perform computational graph operations on the contract block to determine viable risk-transfer agreements; and presenting the offer list to the requester.

The present application belongs to the cross field of maritime affairs, ship, shipping, data science, machine learning and artificial intelligence, and particularly relates to a ship lingering AIS trajectory rapid simplification method, which comprises: arranging and processing AIS data of a ship in time sequence to form an AIS trajectory of the ship; cutting the AIS trajectory to obtain a plurality of trajectory segments; performing self-intersection detection on each trajectory segment; constructing a trajectory directed graph for the trajectory segment with intersection points; and using a graph shortest distance solving algorithm to find out a simplified trajectory and output the same. The method provided by the present application can quickly remove and correct trajectory redundancy and disorder caused by lingering, can not change the trajectory form of other normal navigation segments, and can maximize the retention of useful information of the original trajectory; the constructed directed graph is a very sparse graph, and the operation speed is very efficient and fast, without the need of manually fine-tuning parameters, and is suitable for automatically and rapidly processing a large amount of trajectory data.

This application provides a method and apparatus for constructing a permission topology structure based on the lowest privilege level. The method includes: obtaining a non-public interface path template by structured parsing and classifying request data from an identity management platform and performing parameterized substitution; establishing a mapping relationship between the non-public interface path template and a preset identity management platform permission model to obtain a non-public interface parameter structure; merging the non-public interface parameter structure with an existing interface parameter structure to obtain a centralized interface parameter structure; under the lowest privilege role, starting from the initial node of the centralized interface parameter structure according to the task scheduling engine and graph operation model, performing a multi-hop breadth traversal along member relationships, role bindings, and permission operation paths to determine the corresponding permission graph; performing fine-grained dependency extraction on the permission graph; updating the permission graph according to the dependency relationships; and determining the corresponding permission topology structure. This application can improve the security and management efficiency of the identity and access management platform.

The invention provides a territorial space knowledge evolution model construction method, and belongs to the technical field of territorial space knowledge evolution models.The territorial space knowledge evolution model construction method includes the steps that a distributed graph storage framework is constructed, sub-graph partitions are divided through a community detection algorithm, and external event information is collected and converted into a graph operation instruction sequence; positioning a k-hop neighborhood sub-graph by using an incremental sub-graph matching algorithm and executing local updating to generate an intermediate knowledge graph, calculating node state embedding representation of a fusion time coding vector, capturing a time sequence dependence mode through a time perception graph attention aggregation algorithm, executing semantic consistency constraint detection to identify conflicting nodes, and obtaining a time perception graph attention aggregation algorithm; when the conflict degree exceeds a threshold value, the multi-version concurrency control protocol is triggered to roll back to the snapshot version, otherwise, the intermediate knowledge graph is reserved as final output, and the problem that it is difficult to give consideration to updating efficiency and semantic consistency guarantee when the territorial space knowledge graph responds to an external event for evolution updating is solved.

The invention belongs to the technical field of computer aided design, and relates to a cross-modal mapping and equivariant representation fused body-in-white parameterized grid generation system, which comprises an instruction analysis and encapsulation module for generating a graph operation instruction packet containing hash information based on a cross-modal instruction and a reference topological graph; the parameter injection and verification module is used for injecting parameters into the topological graph copy to generate an initial graph after verification is passed; the constraint guidance isovariant generation module is used for injecting an engineering constraint correction signal in the isovariant graph neural network interlayer iteration to generate isovariant representation; the auxiliary data generation module is used for generating a geometric validity mask and a local differential attribute tensor in parallel; and the guiding type deterministic decoding module is used for constructing contour surfaces by using the masks and guiding grid division and mapping according to the tensor. According to the method, the problems of long design iteration period and data incoherence caused by decoupling of design intention and geometric generation and lagging of engineering constraint verification in the prior art are solved.

Systems and methods for generating item recommendations from unstructured data using machine learning models are disclosed. One embodiment includes obtaining a dataset comprising a plurality of items, wherein each item includes at least one field containing unstructured data, preprocessing the dataset by performing filtering and text cleanup on the unstructured data, performing a coarse relatedness analysis by executing lookups on items in the dataset to identify potentially similar items and create links between items that are potentially interchangeable, performing coarse clustering by utilizing the links to organize related items into clusters using graph operations, performing fine clustering by constructing prompts for a large language model for each cluster to recluster items into subclusters and generate labels for canonical items and generating a list of interchangeable item recommendations based on the canonical items and their associated metadata.

A table data question and answer method based on a data fact knowledge graph acquires original table data to be analyzed, automatically extracts data facts, and constructs a knowledge graph containing data fact nodes and semantic relationships thereof; a large language model is used to perform semantic analysis on a natural language query of a user, identify a query intent type, and extract corresponding structured intent elements; the natural language query is mapped to an executable graph operation sequence for the knowledge graph according to the query intent, operations are performed on the graph to retrieve or dynamically generate target data facts and semantic edges thereof; and a data fact obtained is input into a generative large language model as an enhanced context to generate an answer containing a visual chart and a text description. A table data question and answer system based on a data fact knowledge graph is also provided. The present application effectively suppresses hallucinations of a large model by using structured knowledge constraints, and improves the accuracy of question and answer.

This invention proposes an autonomous control method for agricultural machinery based on graph reinforcement learning, comprising: real-time acquisition of multi-source sensing information of the farmland environment and abstraction into a graph structure, wherein the nodes of the graph structure include agricultural machinery nodes, obstacle nodes, and target operation path point nodes, and the edges include Euclidean distance between nodes and collision threat level; inputting the state of the graph structure into a graph neural network, extracting the features of each entity through graph operations and performing aggregation operations to generate a state feature vector; inputting the state feature vector into a reinforcement learning policy network based on proximal policy optimization, and outputting end-to-end control commands for the steering angle and linear velocity of the agricultural machinery at the current moment to drive the agricultural machinery to complete autonomous farmland operations. This method achieves a highly real-time, highly robust, and highly stable autonomous control method for agricultural machinery.

Advanced persistent threats (APTs) have caused significant financial losses for enterprises, making the development of effective detection systems a critical priority. While existing provenance graph-based APT defenses demonstrate high accuracy, the high complexity and cost of graph operations (e.g., construction, iteration) require extensive processing time and computational resources, making them impractical for real-time detection. To address this challenge, we introduce Madeline, a graph-free, lightweight APT detection system that leverages historical system statistics. This graph-free statistical approach enables lightweight coarse-grained detection with minimal computational resources. Through a multi-step state score calculation for a set of behavioral attributes, Madeline meticulously captures subtle, gradual system changes indicative of stealthy APT activities. Using an unsupervised LSTM autoencoder, Madeline performs anomaly detection effectively without the need for prior knowledge or manual labeling of attacks. Additionally, Madeline supports continuous monitoring, enhancing the assessment of ongoing risks.

The embodiment of the invention provides a man-machine verification method and device, a medium and a program product, and relates to the technical field of man-machine verification. The method comprises the following steps: determining that a current access event meets a preset verification condition, and generating and displaying a verification interface; wherein the verification interface comprises prompt information and a graphic operation area, and the prompt information comprises a currently generated random number; forming a corresponding graph stacking result based on a graph stacking instruction set executed by a user in the graph operation area; generating a to-be-verified number according to a preset statistical rule based on the graph stacking result; and determining whether the current access event passes verification based on the consistency of the to-be-verified number and the random number. According to the embodiment of the invention, the to-be-verified number is generated in a pattern stacking manner according to the preset statistical rule and is used as a basis for man-machine verification, so that whether a user is a robot or not can be effectively distinguished, and access of a web crawler is prevented.

Autonomous management of risk transfer is provided using an automated underwriting processor that creates a contract block by compiling the request into a computational graph-based format, links the contract block to the requester, stores the contract block into memory, retrieves a plurality of available underwriting agreements from memory, and creates an offer list by perform computational graph operations on the contract block to determine viable risk-transfer agreements; and presenting the offer list to the requester.

In response to a user invoking a graph operation on a graph in an in-memory graph algorithm (IMGA) runtime in a relational database management system (RDBMS), the RDBMS generates a set of one or more graph indexes in memory. The graph is represented as one or more vertex tables and one or more edge tables. The RDBMS generates the set of one or more graph indexes by generating a mapping of each database table vertex identifier to a corresponding internal identifier. For each edge table of the one or more edge tables, the RDBMS generates a graph index data structure representing edges of the edge table. The graph index data structure represents each edge in the edge table as a source internal identifier and a destination internal identifier. The RDBMS can then execute the graph operation in the IMGA runtime using the set of one or more graph indexes.

The invention relates to the technical field of water conservancy knowledge graph updating, and discloses a water conservancy knowledge graph dynamic real-time updating system and method based on physical-information deep coupling and semantic field intensity driving, and the system comprises a physical sensing layer which is used for carrying out the real-time collection, transmission and preprocessing of multi-source water conservancy physical data; the coupling mapping layer is used for mapping the multi-source water conservancy physical data into semantic state variables available for a knowledge graph; the field intensity calculation layer is used for calculating semantic field intensity based on semantic state variable changes and determining nodes and relations, needing to be updated, of the knowledge graph; the graph operation layer is used for executing knowledge graph updating decision making and writing operation according to a semantic field intensity triggering threshold value; and the knowledge service layer is used for providing dynamic knowledge service for an external intelligent water conservancy system. According to the invention, multi-source water conservancy data can be effectively integrated, comprehensive acquisition and preprocessing of real-time data are realized through the physical sensing layer, and the accuracy and integrity of the data are ensured.

Techniques are described for applying topological graph changes and traversing the modified graph. In an implementation, a set of compile processes schedules the graph changes caused by a DML (Data Manipulation Language) statement. Based on the requested graph operation in a received query for graph, a set of graph operation processes generate extensions to the graph that capture the changes to the graph by the DML. The received graph operation(s) are then performed by traversing both the existing graph and the generated extensions.

The invention relates to a social relation data visualization optimization method and device based on an attribute graph, and belongs to the technical field of graph data visualization. The method comprises the steps that a social relation visualization rule base is defined and updated in real time based on the attribute graph, wherein the social relation visualization rule base comprises a social relation rule, a relation screening rule, a relation rendering rule, an on-graph relation data statistical rule and an on-graph operation rule; determining relation graph data according to the unique identifier of the to-be-analyzed person, the social relation rule and the relation screening rule; analyzing the relational graph data according to a relational rendering rule, and determining a rendered relational graph; and according to the on-graph relational data statistical rule and the on-graph operation rule, checking the rendered relational graph and analyzing the social relation data of the personnel. According to the method and the system, the aim of timely, accurately, comprehensively and flexibly analyzing the social relation data of the personnel is fulfilled.

The present disclosure provides a global pooling method and a multi-core system for a neural network, the method comprising: receiving point data of the data to be processed inputted sequentially by the previous network layer; after each point data is received, performing a preset pooling operation based on the currently received point data until all the point data of the data to be processed are pooled. Based on the solution provided by the present disclosure, point operations can be used instead of graph operations in the pipeline operation of the multi-core system, that is, after each point data is received, it will be processed once to obtain an intermediate pooling result, until the final pooling result of the data to be processed is finally obtained, thereby reducing the calculation delay.

Methods, systems, and computer storage media for providing a multi-tenant system that executes graph language requests using graph operations of a graph language. A graph language request—that configures tenant data for tenants in a multi-tenant system—is executed using a graph operations engine. The graph operations engine receives and parses a graph language request that includes a list of tenants and a definition of data operations. The set of data operations of the definition are executed on a tree of data operation nodes comprising a plurality of leaf nodes and a root node. Executing the data operations is based on graph language actions (e.g., composition, transformation, and aggregation) that support asynchronously returning results data associated with configuring the Tenant data. Executing the data operations of the definition causes generation of results data (e.g., root node results or leaf node results) configuration of the tenant data in the multi-tenant system.

An elastic distributed graph processing system deployment includes an external control plane that is responsible for controlling the resources that the graph processing system uses. The control plane responds to resource information provided by the graph processing system by giving resources or taking away resources from the graph processing system. Graph operations that cannot continue due to lack of resources are paused and later resumed after the cluster grows, manifesting only an increased latency from a user perspective. Determining which cluster members will participate in the operation processing is driven by extending presence of the objects involved in the operation on a just-in-time basis before the operation starts. The objects involved in and resulting from the graph operations form a hierarchy of transitively dependent objects, which must be considered when extending their presence.

The invention belongs to the crossing field of maritime affairs, ships, shipping and data science, machine learning and artificial intelligence, and particularly relates to a ship wandering AIS track rapid simplification method, which comprises the following steps: arranging and processing AIS data of ships according to a time sequence to form an AIS track of the ships; segmenting the AIS trajectory to obtain a plurality of trajectory segments; carrying out self-intersection detection on each track section; constructing a trajectory directed graph for the trajectory segments with the intersection points; and for the constructed trajectory directed graph, finding out a simplified trajectory and outputting the simplified trajectory by using a graph shortest distance solving algorithm. According to the method provided by the invention, track redundancy and disorder caused by wandering can be quickly removed and corrected, and useful information of an original track can be reserved to the greatest extent without changing track forms of other normal navigation sections; the constructed directed graph is a very sparse graph, the operation speed is very efficient and fast, parameters do not need to be manually and finely adjusted, and the method is suitable for automatically and fast processing large-batch trajectory data.

The invention discloses an audit video generation method and system for the field of graphic operation and maintenance, and a medium. The method comprises the following steps: acquiring graphic operation and maintenance traffic, analyzing graphic messages in the graphic operation and maintenance traffic based on a protocol analyzer, and rendering the graphic messages to an RGB graphic maintenance layer; and calling a graph transcoder to convert the RGB data into corresponding YUV data and rendering the corresponding YUV data into a YUV graph maintenance layer. And according to the time dimension and the space dimension of the image change area in the screen, updating the image change hotspot map, converting the data of the YUV image maintenance layer into an H.264 standard video stream in real time based on the image transcoder, and further installing the H.264 standard video stream into a corresponding standard video format. According to the method, the usability of the auditing video is improved, the real-time performance of auditing video generation is improved, the resource loss of auditing is reduced, and the method can be flexibly configured and deployed according to an actual application scene and has relatively good practicability.

The invention relates to a power transmission line target detection method and device and computer equipment. Acquiring a power transmission line image in the power system, performing feature extraction on the power transmission line image according to the space-to-depth convolutional network in the target detection model to obtain a first feature map, performing embedding position coding processing on the first feature map to obtain a second feature map, and calculating the target detection model according to the second feature map and a map operation network in the target detection model. And performing feature extraction to obtain a third feature map, and performing target detection on the power transmission line image according to the third feature map and a feedforward neural network in the target detection model to obtain a target detection result. According to the embodiment of the invention, feature extraction is carried out through a space-to-depth convolutional network, the feature extraction capability of a low-resolution image and a small target can be enhanced, meanwhile, a feedforward neural network is introduced behind a graph operation network, the feature conversion capability of a target detection model is enhanced, the problem of large scale difference of detected targets is solved, and the detection precision is improved.

The invention relates to a dynamic graph hardware accelerator based on search space perception and a method thereof. The hardware accelerator comprises a control module, a loading module and a cache module. The control module analyzes the received graph query task to determine a task type, determines the range of vertexes and edges needing to be accessed by the graph query task by querying graph structure metadata, and generates a task descriptor; sending the task descriptor to a loading module; the loading module performs analysis and address mapping on the received task descriptor, and judges an execution path based on the cache state of the cache module; when it is detected that the cache is not hit, a data access mechanism for external storage is automatically triggered, and batch loading and cache updating of graph data are completed; sending the processed task data and the distribution logic to a target processing unit in an idle state in a computing unit cluster; the target processing unit receives the task data and the distribution logic and executes graph operation; the caching module is used for caching, address mapping and distribution of target data.

This invention discloses a simulation verification method and system for power communication network maintenance based on digital twins, relating to the field of intelligent operation and maintenance technology for power and communication networks. It solves the problems of existing technologies where maintenance approval relies on manual experience, lacks dynamic simulation methods, and cannot assess the risk of superimposed faults. The key technical points are: converting the pending power communication maintenance application form into a graph operation command; inputting the graph operation command into a pre-constructed digital twin model for topology simulation, outputting the maintenance-state network topology; wherein the digital twin model is constructed from the full topology graph of the power communication network; under the maintenance-state network topology, performing a full-network static connectivity analysis, traversing and simulating the network survivability after a single fault occurs on the remaining critical links, and calculating the maintenance risk index by combining the voltage level and type weight of the carried services; comparing the maintenance risk index with a preset safety threshold, and outputting the simulation verification result.

The application discloses a traffic travel analysis method and device based on a knowledge graph, equipment and a medium. The method comprises the following steps: acquiring a pre-constructed traffic service knowledge graph, wherein the traffic service knowledge graph is constructed based on multi-source traffic data; acquiring a travel service demand of a user, querying a travel service model corresponding to the travel service demand; querying entity information associated with the travel service model based on the traffic service knowledge graph, and calculating a travel service analysis result according to the travel service model and the associated entity information. According to the traffic travel analysis method provided in the application, the public travel data related to urban traffic travel is processed, the urban traffic travel knowledge graph is established, and the graph operation theory is used for mining and calculation, so that data support is provided for the upper research and decision model in the form of a data interface, and the calculation efficiency of subsequent traffic travel analysis services is improved.

The invention discloses an attack detection method and system based on unmanned aerial vehicle group log graph overlap calculation, and belongs to the technical field of unmanned aerial vehicle detection, and the attack detection method comprises the steps: constructing an attack sample graph according to attack logs of an unmanned aerial vehicle group in a historical time period; according to the damping coefficient of each node in the attack sample graph and the frequency among the nodes, generating an attack feature sub-graph in combination with a dynamic expansion strategy; constructing a graph structure of unmanned aerial vehicle behaviors according to the behavior log of the unmanned aerial vehicle cluster, expanding core nodes of the graph structure of the unmanned aerial vehicle by adopting a sampling algorithm, and constructing an overlapping region graph of attack behavior characteristics; determining the similarity between each attack feature sub-graph and the overlapping area graph, superposing the similarity of the overlapping area graph to obtain the attack credibility of the overlapping area graph, and determining an attack behavior according to the attack credibility; according to the method, unmanned aerial vehicle behaviors are comprehensively resumed through a graph operation technology, an attack mode is identified, and an attack path is traced, so that efficient unmanned aerial vehicle group intrusion detection is realized.

The invention discloses an inductive subgraph retrieval completion method and system for entity update knowledge graph, and belongs to the technical field of information, and the method comprises the following steps: constructing a graph operation function, initializing subgraph parameters, obtaining relation representation through attention aggregation, and calculating entity representation by combining message passing and main neighbor aggregation, and updating the representation by using gated residual connection, expanding the sub-graph and performing scoring to complement the triad. Through local sub-graph retrieval and inductive reasoning, the problem of sparsity during dynamic updating of the knowledge graph is solved, complementation efficiency and accuracy are improved, and the method is suitable for an information retrieval and recommendation system.

This application relates to the fields of software engineering and system architecture design, and provides a method, system, and computer-readable storage medium for boundary context identification based on business semantic analysis. The method includes: performing semantic layer reasoning on an acquired business requirement document to obtain business semantic elements; mapping the business semantic elements of the business requirement document to nodes and connecting edges according to preset graph construction rules to obtain a business semantic coupling graph; identifying high-cohesion node sets in the business semantic coupling graph to form communities using graph algorithms, obtaining the initial boundary context corresponding to each community; applying heuristic rules to perform dynamic graph operations on the initial boundary context to obtain the final boundary context; and generating a visual interface to display the final boundary context and the set of interaction relationships between different final boundary contexts. This method automatically discovers boundary contexts in the early design stage based on business requirement documents, without relying on existing code and text similarity, ensuring the accuracy of automatic context identification.

The invention discloses an asymmetric topological key manufacturing method based on an odd graceful topological coding graph. An asymmetric topological key is composed of two essentially different mathematical branches, namely a topological structure and a mathematical code. Wherein the theory of the topological structure is based on a graph theory and topology; the theory of mathematical coding is based on combinatorial mathematics, linear algebra, number theory, set theory, algebra, computer science and other subjects. Giving a coding mirror image from the odd topology to the odd topology, and a mirror image or inverse substitution operation from one odd topology (private key) to another odd topology (public key); establishing a odd extension graph group and a topological graph group signature thereof; a set ordered odd topology graph W-operation graph lattice is constructed by using topological coding point superposition operation and edge addition operation, and a topological graph lattice signature and infinite'one-time pad 'keys are obtained; a generic topological coding matrix and a Fibonacci key sequence are defined so as to be suitable for wider information security application scenarios.