945 results about "Graph generation" patented technology

A computer system and method for interrogating and analyzing data. The system may recognize the software application containing data and will search for visible and hidden data including Headers and Labels; and may further interrogate and analyze visible data. The system will preferably create graphical representations based on the analyzed data instantaneously. The graphical representation is configured to be generated dynamically as the selection of data changes; hence it will eliminate the need of selecting data and changing graph types while moving from one data set to other data sets. The data set may be a single point data, a block of data, a highlighted area, a screen grab, tabular, graphical, or textual formatted data. The system and method may analyze the data from various formats and applications such as spreadsheet, PDF file, remote data sources and web based databases etc.

A system for mitigation of cyberattacks employing an advanced cyber decision platform comprising a time series data store, a directed computational graph module, an action outcome simulation module, and observation and state estimation module, wherein the state of a network is monitored and used to produce a cyber-physical graph representing network resources, simulated network events are produced and monitored, and the network events and their effects are analyzed to produce security recommendations.

Computer-implemented processes are disclosed for creating predictive recommendations based on large scale analysis of users tastes. One process involves detecting users tastes based on online activity and organizing these users into groups of users with similar tastes by applying graph manipulation algorithms and applying a clustering method on these graphs. Another process is disclosed for generating from these sub-graphs of similar groups of users a list of items users are most likely to show interest in based on groups' interests. A large scale solution is disclosed capable of processing large volumes of data in parallel generated from the activities of users online to create these recommendations. A system is described that takes all these artifacts to create a large scale recommendation system and collaborative filtering system. Yet another process is disclosed on how to target these groups of users with promotions through advertising networks.

Embodiments presented herein provide techniques for creating and simplifying a cell and portal graph. The simplified cell and portal graph may be used to make a conservative determination of whether an element of geometry is visible for a given view frustum (and therefore needs to be rendered). That is, the simplified cell and portal graph retains the encoded visibility for given set of geometry. The simplified cell and portal graph provides a “conservative” determination of visibility as it may indicate that some objects are visible that are not (resulting in unneeded rendering), but not the other way around. Further, this approach allows cell and portal graphs to be generated dynamically, allowing the cell and portal graphs to be used for scenes where the geometry can change (e.g., as 3D world of a video game).

A method of routing data from a source node to a destination node in a multi-hop network of nodes interconnected by links comprises: (a) determining that a link-flow vector satisfies one or more necessary scheduling conditions for achievability, wherein the link-flow vector represents a set of flows to be routed on one or more links from the source node to the destination node; (b) generating a scheduling multi-graph for the network, wherein the scheduling multi-graph comprises a graph having at least one pair of nodes with multiple edges therebetween; (c) deriving one or more sufficient scheduling conditions for achievability of the link-flow vector by edge-coloring the scheduling multi-graph; (d) solving a linear optimization problem over the one or more necessary scheduling conditions to obtain an upper bound on the achievability of the link-flow vector; (e) generating, based on the scheduling multi-graph, a solution comprising a set of routes and an associated schedule for achieving the link-flow vector, the solution being a lower bound on the achievability of the link-flow vector; and (f) implementing a routing method using the set of routes and the associated schedule to route the link-flow vector from the source node to the destination node. At least one node v of the network is adapted to receive transmissions from a specified plurality Ω(v) of other nodes, and at least one of the scheduling conditions depends on Ω(v).

Provided is a process including: obtaining a graph comprising nodes and edges, each of the edges having a value indicating an amount of similarity between objects corresponding to the two linked nodes; selecting a parameter for influencing the graph; assessing each of the nodes based on the selected influencing parameter, wherein assessing comprises, with respect to each adjacent node in the graph sharing an edge with the node: determining the value indicating the amount of similarity between the object corresponding to the node and the object corresponding to the adjacent node; and determining a score related to the edge shared with the node, the score determined based on the similarity-amount value and a value of the selected influencing parameter for the node, such that edges are removed, weakened, added, or strengthened; and preparing, based on the graph, instructions to display at least part of the graph.

The invention belongs to the technical field of power systems and discloses a system for monitoring a secondary circuit model on line in real time based on a protection device. The system comprises a network communication module, a model resolution module, a graph generation module, a model storage module and a real-time comparison module, wherein the model resolution model is connected with the graph generation module and the model storage module and outputs a substation configuration description (SCD) static model and a resolved configured IED description (CID) on-line model respectively; the real-time comparison module is connected with the network communication module and the model storage module, receives a generic object oriented substation event (GOOSE) real-time model, a verified SCD static model and a resolved CID on-line model, compares and outputs a comparison result to the network communication module, and the comparison result is uploaded to a background system to realize real-time on-line monitoring. By the system, a phenomenon that a virtual terminal and a secondary circuit of the protection device make mistakes due to SCD file modification, CID configuration errors, secondary circuit real-time connection obstacles corresponding to GOOSE messages and the like can be timely found, so that operational risks of a substation are reduced.

The invention provides a three-dimensional terrain and radar terrain generating method based on S-57 electronic chart data. The three-dimensional terrain and radar terrain generating method comprises the steps that firstly, an elevation vector graph based on an electronic chart is generated based on the S-57 electronic chart data and through the combination of GDEM data, and the elevation vector graph serves as an uniform data source; secondly, the elevation vector graph undergoes conversion, simplification and texture mapping to generate a scene three-dimensional terrain model; at last, based on the elevation vector graph and through cutting of the data, radar terrain data generation and water depth data and navigation target data extraction are respectively completed at different sampling intervals according to needs. The three-dimensional terrain and radar terrain generating method based on the S-57 electronic chart data overcomes the defects that an existing terrain model is not uniform in data matching, not high in precision and poor in running efficiency; the three-dimensional terrain data generating technology, the GPS positioning technology and the GIS geological information technology are integrated, so that the three-dimensional scene running efficiency and the three-dimensional scene vividness are guaranteed, and meanwhile, accurate matching between scene three-dimensional terrain data, radar terrain data, water depth data and navigation target data and the electronic chart is realized.

The invention relates to an electricity storage management technology, in particular to a team and group integrated operation management system based on storage ERP (enterprise resource planning). According to the system, second-level warehouses of an electric system are subjected to intelligent operation management on the basis of a team and group storage ERP system; operating data such as ex-warehouse, warehouse entry, allocating, checking and the like of equipment are correlated by taking data input by RFID (radio frequency identification) as basis and a unique identification code of the equipment as a correlation point, and effective storage management master data are formed finally; various data in the master data are processed by conventional graph generation software to generate a required analysis report or data report which is visually displayed to a manager, the manager masters warehouse materials, machine accounts and the like conveniently, effectively and timely, so that the warehouse materials can be supplemented and adjusted, the management efficiency is greatly improved, account and material coincidence is guaranteed, material balance is achieved, and fund waste is avoided.

An event processing system for processing events in an event stream is disclosed. The system receives information identifying an application and generates a common application runtime model of the application based on the information identifying the application. The system converts the common application runtime model of the application into a first generic representation of the application. The first generic representation of the application is executed in a first target event processing system of a plurality of target event processing systems. The first generic representation of the application comprises a runtime Directed Acyclic Graph (DAG) of components of the application. The system then transmits the first generic representation of the application to the first target event processing system for execution by the first target event processing system.

Systems and methods are provided for providing improved generation of textual directions based on positional information. In an implementation, textual directions for traversing a path are generated based on positional information associated with the path. According to a method, positional information that specifies a longitude and latitude at a plurality of times is received and processed to generate a routing graph. The generated routing graph includes nodes and route links that connect the plurality of nodes. Textual directions are generated for traversing a path associated with the positional data, based in part on link information associated with the links of the generated routing graph.

The invention discloses an art stylization image processing method. The art stylization image processing method comprises the steps of: performing simple graph expression generation processing on an input real image; performing and-or graph expression generation processing on the result of the simple graph expression generation processing; performing stroke distribution graph generation processing on the result of the and-or graph expression generation processing to obtain a needed image. According to the invention, the drawbacks such as a bad layering effect, inflexible rendering and complex man-machine interaction operation in the prior art can be overcome, and the advantages of a good layering effect, flexible rendering and simple man-machine interaction operation are realized.

A specific information processing function, which assumes circuit implementation, is described in a programming language, and from this description, an RTL description that can be logic synthesized is automatically generated. A logic circuit generation device includes: a control flow graph generation unit that generates a control flow graph; a control flow degenerate conversion unit that generates a control flow degenerate program by removing all condition branch instructions from the control flow graph; a data flow graph generation unit that generates a data flow graph from the control flow degenerate program; and a logic circuit description output unit that generates logic circuit description indicating a sequential circuit in which a rooted branch of the data flow graph corresponds to the wiring of the logic circuit and a node of the data flow graph corresponds to a computing element of the logic circuit.

The invention provides a lightning activity data statistics method based on a modified Apriori algorithm. The method includes: 1, calculating weighted support and weighted confidence; 2, performing vertical bit vector format conversion; 3, generating frequent bipartite graphs; 4, mining candidate sets. Items are imparted with proper weights according to actual needs, and the original support and the original confidence are modified into weighted support and weighted confidence which are more practical. In addition, according to the algorithm, item information is stored in the bit vector vertical data format, storage space is saved, and I / O efficiency is improved; according to the modified algorithm, based on the top-down concept, longest frequent item sets meeting the support and confidence requirements are located through frequent bipartite digraphs, and all frequent items meeting the requirements are generated according to properties of the frequent time sets. Through the application of the algorithm, the efficiency of the Apriori algorithm is improved in terms of both space and time, and the algorithm better meets the actual needs.

The invention discloses a question and answer method and device based on a knowledge graph, electronic equipment and a storage medium. The question and answer method based on knowledge graph comprisesthe steps of obtaining a question sentence in response to a triggered user input request; performing entity linking on the entities in the questions obtained through named entity identification in aconstructed knowledge graph to obtain candidate entities; determining a search subgraph containing the candidate entities in the knowledge graph, and generating a plurality of candidate paths pointingto candidate answers by the candidate entities according to the search subgraph; and according to the context information of the question sentence, screening from the plurality of candidate paths toobtain a target path, and pushing a candidate answer pointed by the candidate entity in the target path as an answer of the question sentence to the user. By adopting the knowledge graph-based question and answer method and device, the electronic equipment and the storage medium provided by the invention, the problem of low accuracy of knowledge graph-based question and answer in the prior art issolved.

A data visualization system comprising: a data retrieval module arranged to retrieve data from a data storage module in communication with the data visualization system, wherein the retrieved data includes data sets for representation in a tree map; a tree map generation module arranged to generate a tree map based on the retrieved data, wherein the tree map generation module is further arranged to: i) sort the retrieved data sets according to the size of the data sets; ii) define an area for generating multiple rectangles, each rectangle representing one of the data sets, wherein the area is defined to allow the data sets to be spatially arranged within the area; iii) accumulate data points for data within the data sets to generate a rectangle that has dimensions that fall within pre-defined parameters; iv) generate a rectangle for each data set; and v) orientate the rectangle such that its orientation is only changed if the rectangle does not fit in the available area.

Data of a directed graph is input (S2001), and is divided into subgraphs (S2002). Data of node key assignment tables are generated for respective divided subgraphs (step S2003). The respective node key assignment tables are merged (S2004).

A static-analysis-assisted symbolic execution vulnerability detection method includes the light-weight symbolic execution stage, the graph generation stage, the static analysis stage, the symbolic execution stage and the like. At the light-weight symbolic execution stage, symbolic execution is conducted on a target program byte code file under the small time expenditure through a simple symbolic strategy by means of a KLEE symbolic execution tool so as to obtain a bite code of a complete program after the linking process is completed; at the graph generation stage, on the basis of the bite code of the complete program, function calling graphs of the complete program and control flow graphs of the functions are generated through a concentrated opt tool of an LLVM3.1 tool; at the static analysis stage, the function calling graphs of the complete program, the control flow graphs of the functions and configuration files are used as input; at the symbolic execution stage, an effective skip table and a branch marking table are used for assisting in vulnerability detection symbolic execution.

The invention discloses a knowledge reasoning method based on a multi-modal knowledge graph, and aims to enable knowledge reasoning reliability and accuracy to be higher and enable the knowledge reasoning method to have stronger modeling and reasoning capabilities. The method is realized through the following technical scheme: different information is fused based on multi-hop reasoning of a large-scale knowledge base; attribute completion is performed on the attribute missing graph through attribute graph embedding, structured information is extracted from unstructured and semi-structured documents or sentences, and a dynamic heterogeneous graph embedding model is constructed for multi-type characteristics of the multi-modal knowledge graph through heterogeneous graph embedding; feature learning of semi-structured knowledge, structured knowledge and different types of non-structured knowledge is achieved, and multi-modal knowledge graph features are obtained and serve as input for knowledge reasoning based on a graph neural network GNN; an inference path is generated, and a plurality of types of inference paths are constructed; and classification, edge prediction and frequent subgraphs of node types are calculated on the graph, a knowledge reasoning task is generated, and multi-step complex knowledge reasoning is completed.

The invention relates to a 3D MIMO channel modeling method. The method includes the following steps that: (1) simulated scenes and network layout are determined, large-scale parameters are calculated according to the scenes and calculated correlations; (2) small-scale parameters are generated sequentially based on the large-scale parameters, a probability density function and the scenes; (3) a channel coefficient is calculated; (4) the small-scale parameters are updated according to a calculation result, and a drift model is built; and (5) time evolution is carried out according to the drift model, and then, modeling is carried out. According to the 3D MIMO channel modeling method of the invention, short-term time evolution of the channel coefficient is realized through updating time delay, a departure angle, an arrival angle, polarization, shadow fading and a K factor; smooth transition between adjacent channel segments is supported; a visual-range scene and a non-visual-range scene are simulated jointly by a common framework structure, and therefore, the complexity of a model can be reduced, and multi-unit scenes can be configured freely; and an algorithm of position graph generation is expanded, and diagonal angle movement directions are considered, and smoother output is created.

The invention relates to the field of artificial intelligence, discloses a medical field intention recognition method, device and equipment and a storage medium, is applied to the field of smart medical treatment, and improves the accuracy of a medical field intention recognition result. The method comprises the steps of obtaining an initial question statement from a terminal; calling a preset identification model to perform entity identification on the initial question statement to obtain an entity identification result; performing entity linking on the plurality of coarse-grained entity tagsaccording to a preset medical entity synonym table to obtain linked entity tags; performing intention recognition on the initial problem statement according to a preset intention recognition model, an entity recognition result and the linked entity label to obtain candidate medical intentions; generating knowledge graph query statements according to the candidate medical intentions; and performing knowledge graph query in a preset medical knowledge graph based on the knowledge graph query statement to obtain a knowledge graph query result, generating a corresponding target verbal skill according to the knowledge graph query result, and sending the target verbal skill to the terminal.

The invention provides a house type vector graph generation method and apparatus for a building and a terminal, and relates to the technical field of building image processing. The method includes the steps of obtaining original images of the house type of the building, conducting pretreatment for the original images to enhance the original images, de-noising the original images to obtain target images, determining the house type integral contour of the target images, segmenting the target images into wall areas and non-wall areas based on the house type integral contour, conducting vector processing for the wall areas to fit wall lines, and generating the vector graph of the house type of the building based on the wall lines. Corresponding vector graphs can be generated based on the original images of the house type of the building without manual drawing, labor cost can be saved, and vector graph drafting efficiency can be improved.

A method of noise analysis and correction of noise violations for an integrated circuit design includes steps of (a) receiving as input a standard parasitic exchange file for an integrated circuit design; (b) parsing the standard parasitic exchange file to generate a resistance graph; (c) generating a representation of the resistance graph to determine noise critical nets; (d) generating a list of only noise critical nets from the representation of the resistance graph; (e) selecting a net from the list of only noise critical nets; (f) calculating a value of total crosstalk noise in the selected net from all aggressor nets relative to the selected net; and (g) generating as output the value of total crosstalk noise in the selected net for correcting a noise violation.

The invention discloses a method for evaluating a vulnerability node of an electric cyber-physical system based on an undetectable information attack pre-warning technology. A partial model of the electric cyber-physical system is generated according to a physical topological graph of an electric power system in the electric cyber-physical system, pre-warning of an undetectable information attack under a general information attack and topology condition is analyzed and the damage of the attack is evaluated, and the potential risk of the ECPS is perceived in advance to obtain a more accurate analysis result, so that a grid company can evaluate the running state of the ECPS from a global perspective and the protection for a vulnerability link is enhanced timely to protect the ECPS to run safely and stably. Pre-warning of a multi-node information attack is realized based on analysis of a single-node information attack. In addition, the information attack in the real system often has continuity, the method adds prediction of a continuity information attack on the basis of analysis of an instantaneous information attack, and the overall vulnerability of nodes and the ECPS is evaluated. A technical framework of the invention is more comprehensive.

An event processing system for processing events in an event stream is disclosed. The system receives information identifying an application and generates a common application runtime model of the application based on the information identifying the application. The system converts the common application runtime model of the application into a first generic representation of the application. The first generic representation of the application is executed in a first target event processing system of a plurality of target event processing systems. The first generic representation of the application comprises a runtime Directed Acyclic Graph (DAG) of components of the application. The system then transmits the first generic representation of the application to the first target event processing system for execution by the first target event processing system.

The invention discloses a medical knowledge graph generation method, a storage medium and a server. The method includes the steps that input medical information is received, and named entity identification is conducted on the input medical information to obtain entities in the medical information; entity relation extraction is conducted on the identified entities to obtain the corresponding relation between the entities; a medical knowledge graph corresponding to the medical information is generated according to the obtained entities and the corresponding relation between the entities. The method has the advantage of automatically generating the corresponding medical knowledge graph according to the input medical information; labor is saved, and convenience is provided for users.

A computer-implemented method for learning structural relationships between nodes of a graph includes the steps: generating a knowledge graph comprising nodes representing a system and applying a graph-based convolutional neural network (GCNN) to the knowledge graph to generate feature vectors describing structural relationships between the nodes. The GCNN comprises: (i) a graph feature compression layer configured to learn subgraphs representing embeddings of the nodes of the knowledge graph into a vector space, (ii) a neighbor nodes aggregation layer configured to derive neighbor node feature vectors for each subgraph and aggregate the neighbor node feature vectors with their corresponding subgraphs to yield aggregated subgraphs, and (iii) a subgraph convolution layer configured to generate the feature vectors based on the aggregated subgraphs. Functional groups of components included in the system may then be identified based on the plurality of feature vectors.

The invention discloses a collaborative filtering recommendation method and device based on a knowledge graph and deep learning, and the method comprises the steps: obtaining the relation data of a user and a project, and building a user-project interaction graph and a knowledge graph; generating an embedded matrix according to the user-project interaction graph to obtain original embedded vectors of the user and the project, and extracting a triple in which entities associated with the project in the knowledge graph are located to obtain embedded vectors corresponding to the entities in the triple; recursively propagating and embedding the user embedded vectors in an L-layer Light-GCN network to obtain user embedded vectors of each layer, and combining the user embedded vectors to obtain user high-order embedded vectors; spreading and embedding the knowledge graph by means of ripple networks, and obtaining a project high-order embedded vector through propagation of L ripple networks and high-order interaction of a cross compression unit; and transmitting the user high-order embedded vector and the project high-order embedded vector into a dot product prediction function, and recommending an interested project to the user according to a prediction result. The method overcomes the defects of cold start and sparsity of a traditional recommendation method.

The invention provides a point cloud based indoor dynamic scene SLAM (Simultaneous Location and Mapping) method, which comprises the steps of carrying out multi-scale based laser frame and probability graph registration; updating easy layer of historical probability graph by combining information of the current frame, wherein the implementation mode of the step comprises the sub-steps of converting the current frame to a coordinate system where the historical probability graph is located according to a registration result so as to acquire a converted laser frame, carrying out probability graph generation so as to generate a corresponding probability graph, and updating the historical probability graph by combining the current frame probability graph and the historical probability graph so as to acquire a current historical probability graph; terminating iteration until all of the laser frames are processed, and acquiring a moving trajectory of a laser and a two-dimensional map of the indoor dynamic environment. The technical scheme provided by the invention can carry out composition on a dynamic scene quickly and accurately and accomplish self-locating.

The invention belongs to the technical field of deep learning and computer visualization, and discloses an attention-based CNN category activation graph generation method, which comprises the steps of1, calculating a gradient value of each pixel point of a feature graph M = (M0, M1,..., MK-1) to serve as a spatial attention weight related to a neuron category; step 2, obtaining a connection weight corresponding to each type of neurons as a channel attention weight; and step 3, generating a CNN category activation graph according to the spatial attention weight and the channel attention weight. According to the method, the category activation weight acts on the attention weight, meanwhile, the channel-space position importance of the feature graph is utilized, compared with CAM and Grad-CAM methods, the provided method is better in visualization effect of the generated category activation graph, and the method is not limited by a network structure and is more flexible to use.