13 results about "Strongly connected component" patented technology

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

Aspects concern a method (800) for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations (801), generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned (802), identifying the largest strongly connected component of the routing graph (803), identifying one or more further strongly connected components of the routing graph (804), detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph (805) and amending the map data by the identified travel possibilities (806). FIG. 8.

This invention relates to a multi-agent orchestration and execution method supporting complex cyclic graph structures, comprising: S1: receiving workflow configuration and constructing a directed graph data structure; S2: performing a depth-first search traversal on the directed graph data structure, detecting and outputting strongly connected components in the directed graph data structure; S3: abstracting strongly connected components containing multiple nodes into super nodes, and keeping single-node strongly connected components as ordinary nodes, thereby constructing a directed acyclic graph containing super nodes; S4: performing topological sorting on the directed acyclic graph to generate a hierarchical execution plan containing multiple execution layers; S5: scheduling execution items according to the hierarchical execution plan, and when an execution item is the super node, starting a loop executor for iterative execution. This method, by constructing a multi-layered technical system, integrates graph algorithm innovation, unified context management, abstraction layer design, metadata-driven techniques, etc., to achieve automated orchestration and execution of multi-agent workflows supporting cyclic graph structures.

The invention provides an abnormal transaction recognition method and system, electronic equipment and a storage medium, and relates to the technical field of graph models.The method comprises the steps that transaction flow data are obtained, data preprocessing is conducted on the transaction flow data, and a standard data set is obtained; constructing an aggregation directed graph according to the standard data set; obtaining a plurality of strongly connected component graphs according to the aggregated directed graph, traversing each strongly connected component graph to enumerate a simple directed ring, and obtaining a directed ring list; performing time sorting on each edge of the simple directed rings in the directed ring list, and performing node weight assignment on the simple directed rings after time sorting to obtain a plurality of target directed rings; creating a data table based on each target directed ring, and filling fields of the data table according to node attributes of each target directed ring to obtain a directed ring node attribute table; and performing abnormal identification on the target transaction according to the directed ring node attribute table to obtain an identification result. According to the invention, abnormal transactions can be accurately identified, and effective risk control is realized.

The present application relates to the field of chemical process simulation, and particularly relates to a simultaneous equation simulation block solving method for process industry. The method comprises the following steps: extracting equations in an activated state and variables in an unfixed state from a chemical process model to form an original equation and variable set to be solved; converting the equation and variable set into a graph data structure reflecting system dependency according to a preset block strategy; using a strongly connected component identification algorithm to traverse the graph data structure to obtain a strongly connected component set, each strongly connected component in the strongly connected component set corresponding to a sub-equation block in the process equation; determining a solving order of the sub-equation blocks according to the dependency of the sub-equation blocks; solving each sub-equation block according to the solving order of the sub-equation blocks; and collecting the solving results of each sub-equation block to obtain a global solution of the complete chemical process model.

Aspects concern a method for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations, generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned, identifying the largest strongly connected component of the routing graph, identifying one or more further strongly connected components of the routing graph, detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph and amending the map data by the identified travel possibilities.

The invention provides a graph island structure interpretability analysis and risk early warning method and system. The method comprises the steps that user configuration information is acquired, and input data is used for constructing a user-defined graph structure; extracting all strong connected components or connected components in the graph structure, and carrying out island judgment; in the graph construction process, all nodes which do not meet constraint conditions and do not construct edges and constraint information violated by the nodes are synchronously recorded, the identified constraint conditions of edge construction failure between all nodes in each island and nodes outside the island are counted, statistical results are sorted according to the occurrence frequency and the influence range, and a graph is constructed according to the statistical results. Main influence constraints and corresponding node attribute features are extracted, and an island cause report is generated; continuing key structures in the graph structure are detected, corresponding risk early warning levels are evaluated, a risk early warning list is generated, and the risk level and the key structure type of each node are marked. The problems that a graph analysis method is poor in adaptability and difficult in application expansion are solved.

The application discloses a river network grading and river topological relation automatic identification method based on graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring vector river network, key points and DEM data of a target basin and cleaning; constructing an initial river network graph model based on line element connection relation; fusing DEM terrain evidence and graph theory connection characteristics, constructing and solving a global potential field equation containing terrain driving and boundary constraint, calculating the flow direction potential attribute of all network nodes to determine the flow direction relation; identifying topological abnormal structures such as strong connected components in the network based on the flow direction relation, performing breaking treatment by using direction confidence, and generating a loop-free directed network structure; performing river grade division based on topological transfer rules, and associating the key points to the river network skeleton. The application solves the problems of difficult flow direction identification in plain micro-landform areas and the problem that complex loops cannot be graded by solving and optimizing the global potential field, and realizes automatic construction of the river network topology.

The invention provides a neural network heterogeneous scheduling method based on global sorting and iterative segmentation. The neural network heterogeneous scheduling method comprises the following steps: analyzing a neural network model into a directed acyclic calculation graph; the method comprises the following steps of: marking a hardware affinity label for each operator node in a computational graph based on a hardware affinity rule, initially dividing the computational graph by adopting a connectivity-first search strategy, and aggregating continuous operator nodes which have the same hardware affinity and are connected through a data stream; forming a plurality of initial sub-graphs which respectively specify the target execution processors; constructing a sub-graph level dependency relationship graph; identifying all strongly connected components in the sub-graph level dependency graph; for each identified strong connected component, executing an iterative resolution operation; performing topological sorting on the finally obtained loop-free sub-graph level dependency relationship graph to generate a deadlock-free sub-graph execution sequence; and scheduling each sub-graph to a target execution processor for execution so as to solve the problem that a complex model cannot efficiently run on a resource-limited edge device.

The embodiment of the invention discloses an associated transaction detection method and system, and the method comprises the following steps: obtaining an original transaction side table, an equity side table and a legal person side table, and generating a vertex-side relation table according to the original transaction side table, the equity side table and the legal person side table; constructing the vertex-edge relation table into a weighted attribute graph, and performing distributed cutting on the weighted attribute graph to obtain an edge set and a vertex set which are optimally distributed; performing strong connected component detection on the weighted attribute graph according to the optimally distributed edge set and vertex set, and extracting a sub-graph set containing a closed-loop relationship; and detecting each sub-graph in the sub-graph set to determine whether the associated transaction exists or not. According to the embodiment of the invention, efficient cutting and closed-loop sub-graph classification can be realized through a distributed graph segmentation strategy and strong connected component extraction, so that the detection accuracy of associated transactions is improved, redundant calculation is reduced, and the overall calculation time consumption is reduced.

This invention discloses a personalized webpage ranking method and system based on linear algebra. It represents the graph as an adjacency matrix and performs algebraic calculations, achieving good parallelism for efficient personalized webpage ranking. Specifically, it uses the original graph data corresponding to a set of webpages as the input webpage set, with the initial webpage set being a subset of the input webpage set. A graph adjacency matrix is ​​constructed based on the original graph data. The algebraic Trim-1 method is used on the graph adjacency matrix to discover strongly connected components with one node, and the adjacency matrix is ​​reconstructed to obtain a graph represented using the reconstructed adjacency matrix. An algebraic FW-BW algorithm based on algebraic breadth-first search is used to find the largest strongly connected component in the graph. An algebraic label propagation algorithm based on matrix multiplication is used to find strongly connected components in the graph represented using the adjacency matrix. The PM algorithm is used on the reconstructed directed acyclic graph to calculate personalized webpage rankings, indicating the degree of association between the webpage and the initial webpage set.

The invention discloses a strongly connected component mining method and device, equipment and a storage medium, and belongs to the technical field of graph theory calculation. According to the method, to-be-mined target directed graph data, a corresponding to-be-mined vertex set and a strongly connected component vertex set are obtained; determining a to-be-mined target mining vertex according to the to-be-mined vertex set; determining a target strongly connected component point set corresponding to the target mining vertex according to an HRSCC algorithm, a strongly connected component vertex set and the target mining vertex; updating the strongly connected component vertex set and the to-be-mined vertex set according to the target strongly connected component vertex set, and when the to-be-mined vertex set has vertexes, returning to execute the step of determining the to-be-mined target mined vertex according to the to-be-mined vertex set; and outputting the strongly connected component vertex set. The technical effect of improving the efficiency of mining strongly connected components is achieved.

The application discloses a simulation solver for industrial system control, an electronic device and a storage medium, to solve the problems of high memory occupation and multiple redundant iterations in calculating the Jacobian matrix of the existing simulation solver, comprising: obtaining the differential algebraic equation system compiled from the physical model of the controlled object, generating a hierarchical block directed acyclic graph through strong connected component decomposition, and outputting a topological solving sequence; constructing a partial differential expression node, assigning a number to each node, and generating an ordered array; traversing the directed acyclic graph, executing corresponding automatic differentiation strategies according to the type of the equation block (explicit block, implicit block, algebraic loop), and storing the intermediate results in the topological solving sequence; calculating the partial derivative value through assignment or solving a linear equation set, and filling in the Jacobian matrix. The application eliminates irrelevant equations, distinguishes different types of equations and adopts differentiated differentiation strategies, effectively reduces the memory occupation, and improves the real-time performance and automation level of the industrial system simulation.