19 results about "Configuration graph" patented technology

The invention relates to a territorial space purpose control data processing method based on a space-time knowledge graph. The method comprises the steps that map entities and semantic relations between the map entities are configured in a mode layer, and the categories of the map entities comprise space-time entities used for representing space-time slice states and event entities used for representing human activities generating actual or potential influences on land states; establishing a grid system comprising a plurality of grids based on Geohash coding, associating administrative division and multi-source heterogeneous data with the grid system respectively, and constructing a grid layer; extracting territorial space purpose control data, carrying out entity and entity relationship initialization according to the mode layer and the grid layer, and constructing a graph layer; and establishing an application layer based on the map layer and the grid layer. Compared with the prior art, the method can support reasoning and prediction of geographic phenomena, introduces multi-dimensional information features, and effectively supports business application.

The application provides an AI-based low-code assisted decision-making method and system, which comprises the following steps: collecting the event flow of a user on a low-code platform, constructing an event vector sequence and a behavior component mapping table; obtaining a low-code configuration graph to construct a behavior-structure fusion graph, generate a component complexity score table and determine a high complexity component set; according to the component complexity score table and the high complexity component set, construct a local causal subgraph, generate a path semantic nested matrix, generate a high-risk causal path set and give a path-level risk score, and generate a subgraph edge score matrix; taking the minimization of the consistency loss inside the path as the objective function, determining an optimization suggestion draft set and a suggestion positioning node set; mapping the optimization suggestion draft set and the suggestion positioning node set to the low-code configuration graph for visual labeling, automatically executing configuration modification after structure constraint verification, and recording change logs to support rollback and version tracing.

A method of formulating a desired network configuration graph database representative of a network can include receiving network information regarding multiple devices; generating an existing network configuration graph database representative of the multiple devices; and determining whether each interface description for each device correctly describes the device by comparing each interface description to an actual state of the device. The method can include, in response to the interface description being inaccurate, generating an entry in an interface description database that includes identification information of the device, the incorrect interface description for the device, and a correct interface description for the device for each incorrect interface description; formulating the desired network configuration graph database representative of the multiple devices; providing the desired network configuration graph database and a first prompt to a large language model; and determining an output regarding the network and dependent upon the desired network configuration graph database.

1. The name of the present design product: experimental template configuration graphical user interface of electronic equipment. 2. The use of the present design product: an electronic device. 3. The design points of the present design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: for configuring experimental step information and experimental template flow. 7. The human-computer interaction mode of the graphical user interface: design 1 front view is an experimental step template interface, clicking "+" add new" in the upper left of the interface enters design 1 change state diagram 1, the experimental step template addition interface, the basic information of the experimental step template can be filled in, template classification and permission setting operation can be performed in the interface, after the related operation is completed, click "confirm" at the bottom to enter design 1 change state diagram 2, the experimental step template configuration interface, clicking "+" add component" on the right side of the interface can select the required components corresponding to the experimental step, after the component configuration is completed, data recording and simple mode preset operation can be performed in the middle module of the interface, clicking "experimental step details" in the upper right of design 1 change state diagram 2 enters design 1 change state diagram 3, the experimental step details interface, the basic information of the experimental step template can be modified, template classification and permission setting. Design 2 front view is an experimental template list interface, clicking "+" add new" in the upper left of the interface enters design 2 change state diagram 1, the new template interface, the template name can be filled in, the business scope can be selected, and the permission setting operation can be performed, after the related operation is completed, click "confirm" at the bottom to enter design 2 change state diagram 2, the experimental step template flow configuration interface, the left area of the interface is the experimental step template library, which can query and select the required experimental step template corresponding to the experimental template, the right side of the experimental step template library is the experimental flow drawing area, in which area the hierarchical order of experimental steps can be controlled by drag and drop operation, clicking any experimental step of the experimental flow drawing area can enter the experimental step editing interface on the right side of design 2 change state diagram 2 for component editing operation. 8. Other circumstances that need to be explained: the yellow covered area in each view is the content picture, and the appearance design to be protected does not include color.

The application discloses a kind of graph data query statement determination method, device, equipment and medium, it is related to computer technical field, including: obtaining graph data query information, and determine whether the graph data query information is legal;If the graph data query information is legal, then based on the graph data query information from local preset graph data query statement library filtering out graph data query statement;Obtain statement query configuration information, and based on the statement query configuration information from the graph data query statement determines out to be configured graph data query statement, and according to the statement query configuration information to the to be configured graph data query statement is configured, to obtain target graph data query statement.Through the above technical scheme of the application, the efficiency of graph data query statement determination can be improved, the diversity of business requirements is increased, and the security of graph data query statement is improved.

The application discloses a communication product configuration method based on multi-modal analysis and vector matching, and particularly relates to the technical field of intelligent configuration of communication products, and is used for solving the problem that the efficiency of manually reading documents and manually selecting and configuring is low, errors are easy to occur, and it is difficult to trace back when the customer demand is proposed in the form of multi-source and multi-format documents, the field caliber is complex, and the rule constraints are numerous in the group and government and enterprise customer scenarios of operators. By taking the field dictionary and structured constraints as the basis, the multi-source and multi-format communication product demand document is sequentially converted into demand parameters, demand configuration graphs and multi-channel feature vectors, and vector matching of rule perception such as field mandatory, mutual exclusion of rights and interests, region, contract period and cost is performed on the hierarchical near neighbor graph index in combination with the template graph, so that the communication product configuration scheme meeting the business constraints is automatically output under the unified caliber, the workload of manually reading documents and selecting by experience is significantly reduced, and the accuracy and consistency of the configuration result in the complex scenario are improved.

This invention discloses an intelligent testing method for FPGA interconnect resources at the billion-gate level, belonging to the field of FPGA testing. The method involves identifying the module under test (DUT) of the internal interconnect resources of the FPGA; extracting its physical connection point information from the XDLRC resource file of the DUT; modeling and parameterizing the physical connection points, presenting the results in a graph dictionary; searching for the optimal routing path based on the graph dictionary, generating a configuration graph, and performing ATE testing to determine if the tested path has any faults. This invention extracts the FPGA's XDLRC resource file, analyzes the structural characteristics of the interconnects, and optimizes the modeling process based on the determinacy of the interconnect metal lines' positions, transforming the interconnect metal lines into points in a mathematical graph model. Compared to the previous modeling optimization, the modeling space is compressed by nearly 50%, and the routing search time is also reduced accordingly. An improved depth-first search algorithm is used, enabling the search function to be implemented with concise code, and parameterized information is added to the search dictionary object.

The invention relates to the technical field of artificial intelligence, in particular to an AI agent joint optimization method and system based on adaptive circulation, intermediate verification and multi-dimensional feedback, and the method comprises the following steps: initializing agents and optimization parameters, including calculation graph and configuration initialization, target and constraint setting, and rule and feedback definition; multi-stage self-adaptive circulation is carried out, and a configuration optimization stage, a first intermediate verification and decision stage, a graph structure optimization stage and a second intermediate verification and circulation adjustment stage are circularly executed until an optimization target is met or the maximum number of circulation times is reached; the method has the beneficial effects that the optimization efficiency is greatly improved: an intermediate verification mechanism can timely terminate invalid iteration and match a configuration / graph structure hot start strategy, so that the dependence on a large number of test samples is greatly reduced, the sample utilization efficiency is remarkably improved, meanwhile, the optimization period of an intelligent agent system can be shortened, the token consumption scale of the test samples is reduced, and the optimization efficiency is improved. And unnecessary computing resource waste is reduced.

In some aspects, the techniques described herein relate to a method including: providing a graph neural network; and configuring the graph neural network to predict a competitor, the method comprising: receiving at least one dataset of nodes of supply chain companies, competitor companies, and customers and associated attached nodes' attributes; applying a first-order proximity to denote a local connection structure of some supply chain companies, competitor companies, and customers; applying a Laplacian Eigenmap to the first-order proximity to identify at least two positive pairs and at least two negative pairs; applying a pairwise ranking loss function that reduces the distance between the at least two positive pairs and increasing the distance between the at least two negative pairs; and based on an input identification of one company, ranking competitor companies of the company based on their Euclidean distances in the graph.

The invention provides a configurable artificial intelligence application system and method for industrial automation. Comprising a visual configuration module used for performing consistency verification and versioning storage on a configuration graph to form a configuration snapshot; the algorithm library module is used for being selected and instantiated into algorithm nodes according to the configuration graph; the data processing module is used for generating a data processing snapshot associated with the configuration snapshot; the training engine module is used for receiving the parameter adjustment instruction from the visual configuration module, updating adjustable parameters in a training process, recording associated information of the parameters, data and a model according to training rounds, and exporting a model package; and the inference engine module is used for loading the model package and the associated data processing snapshot, performing parameterized configuration on multi-source data access according to the configuration graph, and executing online inference to generate an inference result. According to the method, configurable integration of an algorithm and a data flow can be realized, a training deployment threshold is reduced, and consistency and traceability of training and reasoning are guaranteed.

The invention provides a configuration editing management method based on a background interface of a power system monitoring system. According to the method, an interaction channel between a configuration editing platform and a system database is built, a special primitive library for electric power and a self-defined configuration module are built, monitoring picture editing and layout are built, and automatic association and manual calibration of primitives and equipment point locations and picture preview are achieved. Configuration editing management of a background interface of the monitoring system of the power system is realized through testing, publishing and picture maintenance and updating; when the method is used, convenient configuration editing management of the background interface of the power system monitoring system is realized by opening a database configuration tool, newly adding or configuring primitives, dynamically configuring pictures, editing primitive attributes, topology pictures, associating data and storing and publishing. The method is simple and easy to operate, is deeply combined with a monitoring system, supports direct association with field equipment data and states, and improves the working efficiency; the problem of inaccurate binding of the picture and the equipment point location is effectively avoided, and the risk of wrong operation is reduced.

Configuring a graph attention network, which is arranged to perform an operation in dependence on a weight matrix and an attention vector; computing an attention weight vector in dependence on the weight matrix and the attention vector; and configuring the graph attention network such that the graph attention network layer is configured to perform an operation in dependence on the attention weight vector. A feature embedding matrix may be multiplied with the weight matrix to form an intermediate matrix. This allows a compressed graph attention network comprising a compressed graph attention network layer arranged to perform a compressed operation in dependence on the one or more adjacency sub-matrix masks to be output. This neural network may be used for image processing, traffic forecasting, recommendation, protein function prediction of autonomous vehicles, with reduced data requirements and reduced latency.

Configuring a graph attention network, where the graph attention network layer performs operations in dependence on a feature embedding matrix and a weight matrix; assessing at least one dimension of one or both of the feature embedding matrix and the weight matrix; and in dependence on the dimensions of one or both of the feature embedding matrix and the weight matrix. The graph attention network layer is configured to, either: (i) perform an operation in dependence on the feature embedding matrix and the weight matrix so as to form a first intermediate output, and subsequently perform an operation in dependence on a first intermediate matrix and the first intermediate output; or (ii) perform an operation in dependence on the first intermediate matrix and the feature embedding matrix so as to form a second intermediate output, and subsequently perform an operation in dependence on the second intermediate output and the weight matrix. This neural network may be used for image processing, traffic forecasting, recommendation, protein function prediction of autonomous vehicles, with reduced data requirements and reduced latency.

The application relates to the electrical technical field and discloses an AI generation method and system from a drawing to a configuration based on a CV large model. First, an original sample set is constructed based on a single cabinet drawing, and a complex graph element sample set is screened out in the original sample set. Then, basic graph elements and non-detachable combination graph elements in each sample graph of the original sample set and complex graph elements in each sample graph of the complex graph element sample set are labeled. The CV visual large model is trained based on the labeled original sample set and the complex graph element sample set respectively, so that a basic graph element recognition model and a complex graph element recognition model are obtained. Finally, the complex graph element recognition model and the basic graph element recognition model are used in sequence to recognize a target system graph, the type and coordinates of each graph element and the topological relationship between the graph elements are obtained, and a configuration graph in a vector format is generated. In a complex circuit design scene, the method and system disclosed by the application can significantly improve the accuracy and reliability of electrical drawing recognition.

In some aspects, the techniques described herein relate to a method including: providing a graph neural network; and configuring the graph neural network to predict a competitor, the method comprising: receiving at least one dataset of nodes of supply chain companies, competitor companies, and customers and associated attached nodes' attributes; applying a first-order proximity to denote a local connection structure of some supply chain companies, competitor companies, and customers; applying a Laplacian Eigenmap to the first-order proximity to identify at least two positive pairs and at least two negative pairs; applying a pairwise ranking loss function that reduces the distance between the at least two positive pairs and increasing the distance between the at least two negative pairs; and based on an input identification of one company, ranking competitor companies of the company based on their Euclidean distances in the graph.

The present disclosure provides an information configuration method and device, electronic equipment, storage medium and program product, and relates to the technical field of communication. The method comprises the following steps: obtaining a configuration graph structure of to-be-configured information; converting the configuration graph structure into configuration text based on a preset graph structure semantic rule, wherein the graph structure semantic rule is used to indicate a conversion relationship between the configuration graph structure and the configuration text; performing vectorization on the configuration text to obtain a configuration text vector; querying a text vector with the highest similarity to the configuration text vector in a graph structure vector library as a target text vector; obtaining configured information of the target text vector according to the target text vector; and performing information configuration on the to-be-configured information based on the configured information of the target text vector. The present disclosure performs information configuration on the to-be-configured information based on the configured information of the target text vector with the highest similarity, which can improve the configuration speed and accuracy of information, thereby reducing the configuration time consumption.

The present application relates to the technical field of computer virtualization and cloud computing management, and discloses a virtual machine snapshot simplification and safe rollback method based on configuration atlas and differential storage. The method obtains virtual machine static configuration information and runtime dynamic device state through a virtual machine monitoring protocol at a preset silent opportunity, forms a state set and cleanses and serializes the state set into a state document, constructs a version node and manages version derivation relationships in a directed acyclic graph, generates a configuration incremental package through differential comparison and stores the configuration incremental package differentially, and realizes lightweight saving and rapid retrieval of configuration versions. When rolling back, external resource dependencies are verified first, consistency risk assessment is performed based on a rule set, the target version static configuration is restored according to an atomized process and the dynamic device state is injected, and the virtual machine is started, thereby improving rollback reliability and safety.

The present invention corrects errors made by a machining tool having a discretionary machine configuration, by using a graph in which constituent elements serve as nodes. This control device comprises: a control point coordinate system insertion unit that inserts, as nodes, a control point and a coordinate system, for each node of a machine configuration graph; an identifier allocation unit that allocates an identifier to the inserted control points and coordinate systems; an error information storage unit that stores information relating to a machine error on a control subject, and the identifier allocated to the coordinate system in which the machine error was observed; an error node generation unit that converts the machine error to an equivalent error node; and an error node addition unit that adds the error node to the machine configuration graph.

The invention discloses a DRC verification graph generation method and device, a storage medium and electronic equipment, and the DRC verification graph generation method comprises the steps: building a mapping relation between the layer name of each layout layer and a graph data format number according to a design rule of a semiconductor technology; classifying the design rules to form a rule type set; for each rule type in the rule type set, constructing a corresponding parameterization unit, each parameterization unit comprising a to-be-configured layer item and a to-be-configured numerical item; arranging the plurality of design rule terms into a rule list according to a preset format; calling a corresponding target parameterization unit according to the rule type of each design rule term in the rule list; and configuring the to-be-configured layer item and the to-be-configured numerical item of the target parameterization unit to generate a DRC verification graph. According to the embodiment of the invention, the generation efficiency of the DRC verification graph can be improved.