5 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 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.

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.