40 results about "Computational topology" patented technology

Methods and apparatus to optimize workflow execution by the intelligent dispatching of workflow tasks against a grid computing system or infrastructure are provided. For some embodiments, a grid task dispatcher may be configured to dispatch tasks in a manner that takes into account information about an entire workflow, rather than just an individual task. Utilizing information about the tasks (task metadata), such a workflow-scoped task dispatcher may more optimally assign work to compute resources available on the grid, leading to a decrease in workflow execution time and more efficient use of grid computing resources.

The present invention relates to a hierarchical system and method for task assignment (TA), coordination and communication of multiple Unmanned Aerial Vehicles (UAV's) engaging multiple attack targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for target list routing. The TA algorithm is based on a graph-theoretic approach, in which a node locates all the detectable targets, identifies them and computes its distance to each target. The node then produces an attack plan that minimizes the sum of distances of the UAV's in the subtree of a given node to the targets.

The invention discloses a fault root cause positioning method for a micro-service system, which comprises the following steps of: acquiring call chain data in the micro-service system in real time, converting the call chain data into four business indexes and monitoring in real time, when an exception is found, constructing a service topological graph, a process topological graph and a host topological graph by using a call chain in an exception time period, calculating an abnormal score of each node on the topological graph, and finally positioning to a root cause node, namely a process node or a host node, according to a sequence from large depth to small depth and from process to host. According to the obtained call chain data, the topological relation of the host level can be dynamically constructed, the topological relation of the process level and the topological relation of the service level can also be dynamically constructed, and data guarantee is provided for more accurate root cause positioning. According to the method, the call chain data in the abnormal time period are analyzed in real time by utilizing an unsupervised algorithm, and training data and labels are not needed.

The invention discloses a drilling geological section modeling method based on topology. The drilling geological section modeling method comprises the following steps: S1, reading drilling data; S2, judging a drilling adjacency relation; S3, calculating a maximum common subsequence; S4, calculating topology connectivity; S5, connecting the topology nodes; and S6, carrying out drilling section topology visualization. Based on the drilling geological section modeling method based on topology, the drilling data is standardized; the method can be applied to automatic analysis and modeling of various complex geologic bodies; the three-dimensional coordinates of the topology control node are optimized based on the minimum potential energy principle; the geologic model precision is obviously improved; automatic visual processing of the drilling section topology and digital integration of geographic information are achieved, and compared with a traditional geological section map, the built drilling geological section topology is beneficial for further serving for information management of BIM, CIM and the like, data analysis and automation of three-dimensional geological modeling work.

The invention relates to a topology identification rate judgment method based on line loss, and belongs to the technical field of intelligent power grids. The method comprises the following steps of:obtaining an automatic topology identification result of a distribution transformer area, and dividing the topology result according to the hierarchy of a distribution transformer, an outgoing line cabinet, a branch box and a meter box; acquiring the electric quantity data of the low-voltage side of the distribution transformer, the outgoing line of the outgoing line cabinet and the incoming lineof the meter box; calculating the line loss rate from the low-voltage side of the distribution transformer to the meter box, calculating the line loss rate from the outgoing line of the outgoing linecabinet to the meter box, judging whether the current line loss is within a preset range, judging whether all branch lines are judged completely if the current line loss is within the preset range, and calculating the accuracy of topology identification if the judgment is completed; and if the line loss rate exceeds the preset range, judging the line loss rate as an abnormal condition, performingclassification judgment on the line loss rate abnormal condition, and recording a topology condition. The method is directly used for judging whether the topology of the low-voltage transformer area is accurate, and provides important basis and scientific support for judging whether the automatic topology identification of the low-voltage transformer area is accurate.

The invention provides a method and a system for determining information similarity, and the method comprises the steps: sorting the consumed information of each user based on the consumed time of the information, and obtaining a corresponding first information behavior sequence; integrating the first information behavior sequences corresponding to all the users to obtain a second information behavior sequence; calculating a first similarity between each pair of information with a co-occurrence relationship in the second information behavior sequence; constructing a topological graph among the information based on the second information behavior sequence; calculating a second similarity between each pair of nodes in the topological graph by using a graph convolution algorithm; and on the basis of the first similarity between each pair of information with the co-occurrence relationship and in combination with the second similarity between each pair of nodes in the topological graph, determining the final similarity between each pair of information in the second information behavior sequence. The final similarity between the information is calculated in combination with the two modes, the number of information covered by the mode of calculating the similarity is increased, and then the accuracy of information recommendation is improved.

The invention provides an SKA task scheduling system and method based on a genetic algorithm and a computing topology model, and the system comprises: a module M1 which builds the computing topology model according to a vector X formed by the processing time needed by each subtask of a parallel task, a vector Y formed by the data processing capability of each node, and a parallel task distributionscheme A; a module M2 which is used for obtaining a suboptimal scheduling scheme through a genetic algorithm according to the dependency relationship among the sub-tasks of the parallel tasks and thecompletion time required by each task; and a module M3 which is used for obtaining a task scheduling scheme by calculating a topological model according to the obtained suboptimal scheduling scheme,obtaining the completion time required by each task according to the task scheduling scheme, and repeatedly triggering the modules M2 to M3 until the number of iterations reaches a preset number of times, so as to obtain an optimal SKA task scheduling scheme. The system and the method are simple in input, and a user can conveniently and quickly construct a task-dependent topological graph file andthen take the task-dependent topological graph file as the input of an algorithm.

The invention provides a computing resources acquisition method and device, electronic equipment and a storage medium, and the method comprises the steps: determining topological information for model training according to scene information and training sample data; obtaining operator resources information of each operator included in the topological information according to the topological information; and obtaining target resources information for model training according to the operator resources information of each operator. Topological information used for model training is determined based on scene information and training sample data input by a user, operator resources information of each operator included in the topological information and target resource information used for model training are calculated subsequently, and the user is assisted in completing configuration of computing resources corresponding to model training. The interference caused by human factors is reduced, and the deviation between the computing resources required during model training and the computing resources configured by the user is reduced.

The invention relates to the technical field of artificial intelligence, and provides a user tag identification method and device based on artificial intelligence, equipment and a medium. The method comprises the following steps: acquiring related information (such as behavior information of an online medical platform) of a user group and constructing a topological network of the user group; inputting the topological network into a graph convolutional neural network, calculating a first-order similar matrix, a second-order similar matrix and a Dice similarity matrix of the topological network, and calculating a target similar matrix of the topological network; calculating a low-dimensional matrix of the topological network based on the target similar matrix; taking a first type of nodes corresponding to the low-dimensional matrix as a sample set, and training a graph convolutional neural network to obtain a target classification model; and identifying second type nodes of the low-dimensional matrix based on the target classification model to obtain target labels of users corresponding to the second type nodes. According to the invention, the accuracy of node label identification can be improved. The invention further relates to the technical field of block chains, and the target label can be stored in a node of a block chain.

The invention provides an ocean data assimilation method and system based on high-performance parallel optimization, and the method comprises the steps: obtaining to-be-assimilated ocean data and a mathematical model, calculating the data assimilation complexity of each ocean lattice point according to background field data and an ocean lattice point distribution diagram in the ocean data, and calculating the data assimilation complexity of each ocean lattice point; generating a calculation topological graph based on data assimilation complexity; according to a preset longitude range, grid points in the ocean grid point distribution diagram are grouped, the overall assimilation complexity corresponding to each group is counted according to the calculation topological diagram, a plurality of calculation nodes are distributed for each group in a balanced mode, and the calculation amount responsible for each calculation node is divided into calculation processes in the calculation nodes in a balanced mode; and after the calculation processes in the calculation nodes complete respective data assimilation tasks, assimilation result data is obtained, and the assimilation result data is written back to the mathematical model to serve as an ocean data assimilation result. According to the method, the load balancing of each process in the distributed computing system is realized by taking the computing topological graph as a basis for load balancing division.

The embodiment of the invention discloses a visual processing method, device and apparatus for a fund calculation engine, and a medium. The method comprises the steps of obtaining a fund calculation request; selecting a corresponding calculation topology according to settlement rules of the fund calculation request; according to a node sequence in the calculation topology, adopting at least one calculator corresponding to calculation rules of each node, carrying out calculation based on input data of the fund calculation request, and generating a calculation result; carrying out visualization processing and display on the nodes and node relations of the calculation topology; acquiring burying point acquisition information in at least one node, and performing visual display, wherein burying points are arranged in codes for realizing the node calculation rules, and the burying points are used for collecting information in the code execution process. According to the embodiment of the invention, a calculation route is rendered according to a moving track, and the detail height of each step of the whole calculation is visualized, so that the service structure is intuitively understood, the difference checking is convenient and quick, and the configuration correctness is quickly perceived.

The invention relates to a three-dimensional reconstruction method and device, electronic equipment and a computer storage medium, and the method comprises the steps: calculating the relative pose similarity transformation between all SFM models in a topological relation graph which is the spatial position priori obtained when an image set is collected, obtaining each SFM model by performing three-dimensional reconstruction on each image set of the large-scale scene blocks based on an SFM algorithm; processing the topological relation graph to obtain a maximum spanning tree, wherein the maximum spanning tree is a subset of the topological relation graph; and finding a node with the maximum degree in the maximum spanning tree as a root node, performing similarity transformation on the SFM models on other nodes by taking the root node as a reference coordinate system so as to unify coordinate systems of the plurality of SFM models and obtain a combined target three-dimensional map, and performing global optimization on the pose map to obtain an optimized target three-dimensional map. According to the invention, the problem of low efficiency of three-dimensional reconstruction of a large-scale scene is solved.

The invention provides a topological graph drawing and displaying method and system, equipment, and a storage medium. The method comprises the following steps: analyzing a to-be-displayed data structure, and determining a maximum level and a maximum width of a topological graph corresponding to the data structure; setting a distance width of a node in the first direction, and determining an actual height according to the distance width in the first direction and the maximum level; setting a distance width between the node and a second direction perpendicular to the first direction, and determining an actual width according to the distance width in the second direction and the maximum width; drawing a topological graph according to the actual height and the actual width, and displaying the topological graph corresponding to a preset level in response to the situation that the level of the topological graph is greater than a threshold value. According to the method and the system, the lengths and the widths required for drawing canvas of topological graphs can be accurately calculated, and the occupation of a memory is greatly reduced.

The invention discloses an intersection expression method based on road network data and vector calculation, and the method sequentially comprises the steps: reading the road network data; extracting topological connection data of the intersection; the topological edges of the intersections are ranked; calculating intersection points of the intersections; the intersection points of the intersection are smoothed so that the intersection points can better conform to the actual intersection contour range; intersection points of topological edges are calculated; and according to the topological point sequence of the road section topological connection data, connecting the coordinates of the topological edge intersection points in sequence to form a complete road network. The method has relatively high popularization and application values, and can provide a reference method for intersection expression or road network visualization of macroscopic traffic simulation software such as TransCAD and TransStar under a microscopic dimension under a macro-microscopic integrated background.

The invention discloses a metadata-driven end-to-end network topology dynamic generation system and a metadata-driven end-to-end network topology dynamic generation method, which belong to the technical field of network topology and comprise a configuration module, a data acquisition module, a node position calculation module and a node position calculation module. According to the invention, the unified topological data interface is defined, the corresponding routing data is provided according to the interface, the system automatically analyzes the data and presents the end-to-end topology, and the applicability is high; nodes and connecting lines in the topology can be adjusted through configuration, node position calculation can be automatically carried out according to data, the topology is basically not required to be changed in the follow-up process, only a data interface needs to be adjusted if new services and new scenes exist, the development cost is low, maintenance is convenient, and the method is worthy of application and popularization.