434 results about "Minimum spanning tree" patented technology

The invention relates to a power distribution network double layer planning method considering the time sequence and the reliability. The method comprises: according to the meteorological files and the load power statistical data, obtaining the typical daily power time sequence curves of the wind electricity, the photovoltaic output and the load in different seasons; based on the opportunistic constraint planning method, creating a power distribution network framework and a distributed power capacity double layer planning mathematical model, including the objective function and the constraint condition; using the particle swarm optimization algorithm to solve the model and using the minimum spanning tree algorithm to ensure the radiation and connectivity structure of the distribution network during the iterative process; and obtaining the target network framework and the Pareto optimal solution set of the distributed power capacity so as to generate the best planning scheme. The invention solves the problems that unnecessary investment into the a power distribution network incurred by the fact that a traditional power distribution network planning method containing a distributed power supply cannot reflect the typical output characteristic of a distribution type new energy; and 2) that by incorporating the power distribution network power supply reliability into a model target function, the reliability target can be realized at the planning stage.

The invention relates to a method suitable for multi-view-angle automatic global registration of ground laser point cloud data. The method involves two key modules: a module for extracting semantic feature points and a module for feature matching. The method comprises: step 1, extracting the semantic feature points: obtaining the semantic feature points in a series of modes of data slicing, distance clustering, geometric element fitting and the like; step 2, matching the semantic feature points: matching the semantic feature points by constructing a triangular geometric constraint condition and a semantic constraint condition, and removing error matching in a geometric consistency clustering mode; and finally, constructing a weighted undirected graph by taking a reciprocal of a feature point number as a weight, and finally obtaining global registration parameters of all stations by taking a minimum spanning tree of the weighted undirected graph as a registration path, thereby realizing global optimal registration. According to the invention, the method suitable for multi-view-angle automatic global registration of the ground laser point cloud data is constructed; the method can effectively resist influences of noise, point density and coverage; and the method improves the laser scanning operation efficiency, thereby having very high practical values.

A method, system and computer program product for simplifying a plurality of correlation rules of a graph. The method includes the steps of: receiving correlation rules; creating an undirected graph; removing redundant edges from the undirected graph; splitting nodes in the undirected graph; replacing a probability that an edge that connects two nodes to a seed value; modifying the seed value by adding a first value to said seed value and adding a second value to the first value; determining a maximum modified seed value; adding the maximum modified seed value to a probability that the uncertain edge connects two nodes; removing any temporary certain edge; and running a minimum spanning tree algorithm on said modified undirected graph.

The invention discloses a data aggregation method based on a minimum spanning tree. The data aggregation method mainly includes the steps of wireless sensor arrangement, cluster head selection, clustering, simple graph model formation through nodes in clusters, calculation of weight in the clusters, minimum spanning tree building through the nodes in the clusters, aggregation of data in the clusters, cluster head weight calculation, simple graph model formation through nodes in cluster heads, minimum spanning tree building through the nodes in the cluster heads, cluster head data aggregation, node energy consumption balancing and cluster maintaining. According to the data aggregation method, by means of reasonable arrangement of the sensor nodes and clustering of the nodes, energy consumption of the sensor nodes is distributed evenly, the life cycle of a whole wireless sensor network is prolonged, and energy of all the nodes can be used efficiently.

The invention provides an inhomogeneous clustering multihop wireless sensor network routing protocol based on energy balance. A wireless sensor network data transmission method comprises the followingsteps: voting a cluster head according to residual energy of the nodes, the distances from the nodes to a base station and the distances from the nodes to the cluster head, separately adding the nodes not becoming the cluster head to the cluster where the cluster head having the shortest distances with the nodes is located; then, constructing, by the cluster head, an optimal transmission path based on a minimum spanning tree according to the residual energy and the distance to the base station so as to find the optima next hop forwarding cluster head; and finally transmitting data to the basestation by the nodes in a single hop in the cluster and a multihop communication mode between the clusters. By adoption of the method, the energy of the nodes can be effectively saved, the energy consumption of the nodes can be balanced, and thus the life cycle of the wireless sensor network is prolonged.

The invention belongs to the field of computer vision and image processing, and provides a rapid non-local super-resolution rebuilding method based on a minimum spanning tree (MST). Time complexity of an algorithm is reduced, meanwhile the quality of a rebuilding result is maintained, and a good balance between the time complexity and the quality is achieved. According to the technical scheme, the rapid non-local super-resolution rebuilding method based on the MST comprises the following steps: (1) obtaining an initial low-resolution depth image required to be rebuilt, then carrying out further simple pre-processing on the initial depth image, (2) obtaining a standard 4-connected planar graph, (3) obtaining the MST corresponding to an undirected connected graph in the step (2), and (4) rebuilding the coarse depth image after pre-processed according to the MST. The non-local super-resolution rebuilding method based on the MST is mainly applied in image processing.

The invention discloses a low-voltage distribution network user topology estimation method and system based on mutual information, wherein, the method comprises the following steps: step S1, obtaininguser voltage time series data and transformer voltage time series data; step S2, obtaining user voltage time series data and transformer voltage time series data. 2, calculate mutual information between user voltage and mutual information between user and transformer accord to time series data of user voltage and time series data of transformer voltage; Step S3, according to the mutual information between the user and the transformer, the user phase identification is carried out according to the K-means clustering algorithm; 4, construct a topology model; Step S5, the topology estimation is completed according to the mutual information between the user voltages by using the minimum spanning tree algorithm for the user after the phase identification. Compared with the prior art, the invention does not rely on the known topology, does not need additional carrier wave, is not affected by the load of the distribution station area, has no field test wiring problem, does not exist securityhidden trouble, and improves the speed of topology estimation.

The invention provides a complex terrain wind power plant integration optimization method comprising: a first step of micro-siting optimization: with the maximization of the generating capacity of a wind power plant as the target, calculating the position coordinates of each wind turbine iteratively by using the genetic algorithm; a second step of electricity collection line optimization: calculating a scheme with the minimum total path weight by using the Dijkstra algorithm and the minimum spanning tree algorithm and determining the connecting loops between the wind turbines; a third step of maintenance road optimization: based on the connecting paths between the wind turbines obtained in the last step of electricity collection line optimization, calculating the fill and cut generated by the distance of the paths, and, by using the minimum spanning tree algorithm, calculating the scheme with the lowest cost and determining the road connection between the wind turbines. The method has the advantages of high convergence rate, stable generalization performance and high prediction accuracy. Through micro-siting optimization, the method optimizes the layout of wind turbines and can increase the generating capacity of wind power plants and reduce the economic cost; through electricity collection line optimization, the method is economical and reasonable; through maintenance road optimization, the working efficiency is increased and the cost is reduced.

The invention relates to an island dividing method for a power system and an island dividing method for a power distribution network having distributed power sources. The method includes steps of (1) modeling aiming at the island dividing problem; (2) undirected graph weight setting for the power distribution network; (3) formation of a radiation power distribution network based on a minimum spanning tree algorithm; (4) island division, based on an improved genetic algorithm, for the power distribution network having the distributed power sources; (5) island division for the power distribution power network having the plurality of distributed power sources. According to the invention, island division problem of the power distribution network is solved by the combination of the minimum spanning tree algorithm and the improved genetic algorithm. The power distribution network model is simplified based on features of the power distribution network. The power distribution network with a looped network structure is simplified to a radiation power distribution network with the optimal load restoration sequence and then the optical island is obtained by adopting the improved genetic algorithm. The searching efficiency is improved and the optimal solution can be obtained quickly and effectively. The method provided by the invention has good practicability.

The invention provides a multi-scale multi-level image segmentation method based on a minimum spanning tree. Multi-scale multi-level image segmentation is expressed and realized by a graph model. The segmentation method is suitable for initial segmentation results obtained by various images and various rules to effectively combine over-segmented regions in high level segmentation, so an over-segmentation phenomenon is avoided; moreover, multi-scale segmentation results on different levels provides characteristic description information on different levels for analysis of target structural components; and the method is very important for target recognition.

The invention provides a content route managing method of a fusion type content distribution network, belonging to the technical field of internet. The invention aims to provide a two-layered content node deployment mechanism aiming at ISP-CDN (Internet Service Provider-Content Distribution Network) and design an indexing and synchronization mechanism to improve a content route policy under a CDN environment; a flow settlement cost and data transmission quality are considered. The network data is singly processed, namely the flow cost consumed by primary transmission is only related with a passed route hop count and the size of the data is not considered; an inverse ratio relation is formed between the node quantity selected from a core route node set F and the repeated flow; a synchronous cost is in direct proportion to the node quantity; a synchronous initial source is an inter-domain router node; with regard to an inter-domain route node t, a breadth first search algorithm is used for generating k content nodes covered by a minimum spanning tree TR which takes the t as a root; the k notes are found and an RC+SC minimum is realized by adopting an algorithm based on a greedy strategy.

A method and apparatus for generating a degree-constrained minimum spanning tree (MST) may include a plurality of point-to-point (P2P) packet switching nodes for receiving and sending data packets; a plurality of point-to-multi-point (P2MP) packet switching nodes for sending and receiving the data packets; and a plurality of transmission links coupling pairs of the plurality of packet switching nodes, where each of the plurality of transmission links has a distance. A path computation module may determine a degree-constrained MST such that each of the plurality of P2P packet switching nodes has a degree of two. An extended Bellman-Ford algorithm successively analyzes each of the plurality of packet switching nodes as a present node and calculates the degree-constrained MST as a function of the distance of each of the plurality of transmission links and whether a previous node from the present node is a P2P packet switching node or a P2MP packet switching node.

The invention discloses a compression method for a screen image set, and aims to obtain higher compression efficiency. The method comprises the following steps: (1) computing the Hash value of each image block of each image; (2) balancing the correlation between images by extracting the Hash characteristics of the image blocks; (3) generating a minimum spanning tree based on a directed graph for all the images in order to determine a prediction structure of the image set; (4) re-sequencing the image set to determine four optimal prediction reference images of each image; and (5) replacing four default reference frames in an HEVC (High Efficiency Video Coding) standard with the four optimal prediction reference images in the step (4) to predict a current image.

The invention discloses a wireless network optimization method for an intelligent meter reading system. The method is an improved dynamic routing allocation optimization algorithm. The method mainly solves the routing optimization problem of a wireless network layer, DSR is preliminarily optimized, the unrelated routing number in the DSR is increased, and the enhanced DSR optimization algorithm is provided. In order to solve the route selection problem in the enhanced DSR optimization algorithm, the minimum spanning tree thought is introduced into the DSR, a DSR optimization protocol based on a minimum spanning tree is provided, factors in multiple aspects of bandwidth, transmission congestion, network throughput and the like are comprehensively considered, simulation software NS-2 is used for simulation, and the result shows that the method can effectively shorten delay and improve data transmission efficiency and other indexes.

The invention discloses a cluster tree hierarchical wireless sensor network routing method with the optimized energy efficiency. The method includes the first step of automatically and dynamically organiing clustering during a cluster construction period, the second step of carrying out random selection on cluster head nodes according to the energy balance principle through the consideration on node energy factors, the third step of utilizing a minimum spanning tree Prim algorithm to construct a cluster tree hierarchical network route, namely, points in the Prim algorithm correspond to the cluster head nodes selected in a wireless sensor network, and the weights of sides correspond to energy weighting distances between the cluster head nodes, and the fourth step of constructing the cluster head nodes to a multi-hop routing network structure. Through the protocol, the cluster heads are reasonably selected in the application of the wireless sensor network which is powered through the energy harvesting technology, and consequently effective utilization of energy can be achieved, the communication cost between the nodes is reduced, the problem of the premature death of the cluster head nodes due to long-distance communication is solved, and the prolonging of the lifetime of the whole network is facilitated; in addition, the application of the spanning tree algorithm enhances the expandability of the protocol.

The invention discloses an improved k-means clustering method based on a distributed computing platform, and introduces a distributed computing platform Spark for the problem of slow processing of mass data, the Kruskal's algorithm for the problem of too many iterations and the Tanimoto distance for the problem of giving no consideration to the correlation among features of a vector. First, a minimum spanning tree is constructed for the randomly selected k points by using the Kruskal's algorithm, the corresponding weight sum is obtained, and the process is repeated for n times. Then, according to weight sums obtained within the n times, the maximum weight is selected thereform and that distance values between edges composed of the k points are not much different is ensured. In this way, the relatively uniform distribution of cluster centers can be guaranteed. Finally, a clustering algorithm is performed using k-means algorithm improved by using the Tanimoto distance.

The embodiment of the invention provides a topographic feature line extraction method and device, and solves a problem that a characteristic line extraction method in the prior art cannot achieve the convenient screening of extraction results according to the characteristic significance degree. The method comprises the steps: carrying out the preprocessing of collected topographic DEM data; extracting a plurality of feature points and calculating characteristic confidence degree of all feature points; carrying out the feature extension in a feature direction of each feature point or in a direction adjacent to the feature direction of each feature point; obtaining a feature map, and generating a feature forest according to a minimum spanning tree algorithm; carrying out the branch and trunk decomposition of the feature forest, and extracting all feature lines; and selecting a preset number of feature lines with the maximum significance from all feature lines as a topographic feature line set. The method and device can achieve the convenient screening of the extraction results according to the feature significance, and improve the anti-noise capability of feature extraction and the connection of the feature points.

The invention discloses a distributed multi-robot dynamic network connectivity control method, and belongs to the technical field of multi-robot dynamic network control. The method comprises minimum spanning tree distributed dynamic network topology control based on event drive and optimal neighbor communication link connectivity maintaining motion control based on a local artificial potential field. Each robot node periodically broadcasts the location thereof and updates neighbor information, the local minimum spanning trees of the neighbor networks of the nodes are established in a distributed manner, an optimal neighbor is selected, and the communication link with the optimal neighbor is maintained through a local artificial potential field method, so that global information connectivity is optimized and optimal network communication cost is achieved on the basis of maintaining the connectivity of the multi-robot dynamic network. There is no need for a center node to perform calculation in a centralized way, and distributed and parallel implementation can be carried out, so that the communication load is reduced, and the scalability and robustness of the multi-robot network are enhanced. The problem of communication link interrupt caused by robot motion can be overcome.

The invention discloses a binocular stereo matching method based on edge information. Firstly, performing color conversion on a left image and a right image acquired by Gaussian sampling through a Gaussian color model, introducing gradient information, using a neighborhood median as a threshold to replace a central pixel of a window, and counting the number of different corresponding bits in a Census transform value by using a Hamming distance so as to obtain a matching cost amount; secondly, converting the acquired image into an undirected graph, performing gradient operation by using a weighting function, constructing a minimum spanning tree, and performing cost aggregation by combining a cross-scale cost aggregation method to obtain a parallax value; generating a disparity map accordingto the obtained disparity value by using a winner-king strategy; and finally, carrying out region division on the pixel points by utilizing a super-pixel segmentation algorithm, and carrying out optimization processing on the disparity map by combining weighted median filtering, so that disparity information with relatively high precision can be obtained, and particularly, a relatively accurate disparity map can be obtained in an occlusion region and an edge information discontinuous region.

The invention relates to a multi-target point path planning method based on a fast random search tree, and belongs to the field of robot path planning. The method of the invention uses a two-layer tree structure. The bottom layer tree is composed of multiple trees extending from multiple target points, wherein each tree has a weight determined by the surrounding environment, and each tree searchesthe free space by using a fast random search tree algorithm. An effective collision-free path is generated when two trees are close enough, and the effective path and nodes constituting the effectivepath will be transferred to the top layer tree. The top layer tree carries out re-planning on the paths and the nodes by using an improved minimum spanning tree algorithm and finally acquires the shortest path capable of traversing multiple target points. The multi-target point path planning method provided by the invention can be effectively implemented in various obstacle environments. In addition, the calculation speed is high, and the path can be directly navigated by a mobile robot.

The invention discloses a complex network community mining method based on an improved genetic algorithm and belongs to the technical field of complex network community mining method research. The complex network community mining method based on the improved genetic algorithm uses the improved genetic algorithm based on clustering and double population thought fusion to mine communities in a complex network. The complex network community mining method based on the improved genetic algorithm uses a normalization common information similarity standard as the standard for measuring the similarity between individuals in the population and fuses the clustering and double population thought. The complex network community mining method based on the improved genetic algorithm includes that introducing the clustering thought, using a minimum spanning tree clustering method to classify the population, introducing the double population thought, and determining the main type and auxiliary type for the clustering. The main type maintains the population evolution direction to get close to the optimal solution of an objective function; the auxiliary type is mainly used for duly providing diversity for the main type so as to enable the main type to be capable of coming out to search the other solution space to realize the complex network community mining when the main type is located at the local optimum.

The invention discloses a heuristic power distribution network reconstructing method based on a minimum spanning tree and is aimed to realize the minimum active power loss and solve the power distribution network reconstructing problem through combining the minimum spanning tree algorithm with a heuristic method. The solving process includes that using the relationship between voltages of two sides of a branch as a weight, and performing preliminary optimization through the minimum spanning tree algorithm to obtain an initial solution to enable the subsequent optimization to be independent of the initial structure of a network; the secondary optimization includes that using a new candidate switch evaluation strategy to evaluate candidate switches, obtaining an optimized reconstructing structure through a little of candidate switches to be evaluated, and correcting to obtain the optimal solution. The heuristic power distribution network reconstructing method based on the minimum spanning tree does not depend on the initial structure of the network, has high reconstructing efficiency and is suitable for different sizes of power distribution networks.