301 results about "Neighborhood search" patented technology

The invention discloses a building roof reconstruction method based on airborne LiDAR data. The method comprises the steps of 1, obtaining LiDAR point cloud data, and 2, processing the LiDAR point cloud data; the step 2 comprises the processes of 201, dividing building roof patches to obtain a plurality of roof patches from the building roof, 202, obtaining building roof point cloud data, 203, extracting outer contour points of the building roof and the roof patches, and meanwhile, extracting the outer boundary line of the building roof, 204, obtaining the vector boundaries of the roof patches, including establishing a search information set, carrying out neighborhood search, determining the types of the outer contour points and determining the vector boundaries, and 205, obtaining a vector model of the building roof, namely obtaining the vector model of the building roof after determining the vector boundaries of the plurality of roof patches. The method is simple in steps, reasonable in design and convenient to realize, good in using effect, and capable of completing the reconstruction processes of the building roof simply, conveniently and quickly with high quality.

The invention discloses an optimization method for solving an omni-channel logistics distribution problem. In a first stage, a Lagrange relaxation technology is used to solve a LAP (Location-allocation Problem) problem. In a second stage, adaptive large-scale neighborhood search is used for solving a multi-vehicle-type vehicle path problem, a feasible solution can be searched in a large range in a solution space through one group of simple destruction and reconstruction algorithms, and a situation of falling into local optimum can be effectively avoided. Meanwhile, a simulated annealing acceptance criteria realized in the adaptive large-scale neighborhood search can guarantee the quality of a solution and the convergence of the algorithm, a result is output after the algorithm is executed for an appointed iteration number, a time constraint requirement can be met, and a good vehicle distribution scheme is solved for enterprises in limiting time. The simpleness of the Lagrange relaxation technology is combined with the efficiency of the adaptive large-scale neighborhood search, so that the integral solving efficiency of a mixed algorithm is high, and the problem of omni-channel logistics distribution can be effectively solved.

The present invention discloses a three-dimensional reconstruction method and system capable of maintaining sharp features. The method includes the following steps that: 1) a two-dimensional image filtering method extended to a three-dimensional space is adopted to perform smoothing de-noising on inputted roughly-registered point cloud; 2) an improved region growth method is adopted to perform outlier removal on the smoothed roughly-registered point cloud; 3) a kd-tree (k-dimensional tree) acceleration-based ICP (iterative closest point) algorithm is adopted to perform precise registration on the outlier-removed roughly-registered point cloud; 4) a neighborhood search and boundary point detection-based fusion method is adopted to fuse the precisely-registered point cloud; and 5) a feature point detection and adaptive step size update-based method is adopted to perform surface reconstruction on the fused precisely-registered point cloud. The three-dimensional reconstruction system is composed of a point cloud preprocessing module, a point cloud combining module and a surface reconstruction module. The three-dimensional reconstruction system which is realized based on the method of the invention can maintain the sharp features of the edge of a reconstructed model, and therefore, reconstruction speed is considered with accuracy ensured.

A mass point cloud spatial management method based on octree-like encoding takes the massive point cloud data as the research object, and is a hybrid point cloud index structure which combines the spatial segmentation of KD-tree to achieve efficient management of mass point cloud. The method is characterized in that firstly, the space of point cloud is partitioned by KD tree, and the subspace is further partitioned by octree, and the octree-like index structure is established; the traditional linear octree coding is improved and the optimized coding method is used to encode the space in orderto achieve better spatial management and neighborhood search. The experimental results and comparative analysis show that octree-like structure can make the overall structure of data organization morereasonable, improve the access efficiency and reduce the occupancy of memory space. The method not only improves the speed of traditional KD tree index, but also improves the problem that traditionaloctree occupies too much space and takes too long to search neighborhood, and realizes the reasonable management of massive point cloud space.

A processing device of an information processing system is operative to obtain a plurality of records, documents, web pages or other data objects, and to construct a binary tree using a bipartition procedure in which subsets of the data objects are associated with respective nodes of the tree. Evaluation of a designated modularity for a given one of the nodes of the tree is used as a stopping criterion to prevent further partitioning of that node and to indicate designation of that node as a leaf node of the tree. The resulting leaf nodes of the tree provide a non-overlapping partitioning of the plurality of data objects. The processing device is further operative to perform a neighborhood search on the tree to identify pairs of the plurality of data objects that match the same entity, and to store an indication of the matching pairs of data objects.

The invention belongs to the technical field of robot path planning and discloses a robot optimal energy efficiency path planning method based on an improved A algorithm. According to gradient characteristics of a three-dimensional terrain environment and geometrical properties of robot motion, an energy cost consumption model is established, and a starting point and a target point are determined;by combining the basic principle and process description of an A* algorithm, a heuristic evaluation function of the A* algorithm is determined; the neighbor node searching mode of a traditional A* algorithm, namely multi-neighborhood search, is improved, traditional 8-neighborhood extension is changed into 24-neighborhood extension; the improved A* algorithm is executed to output an optimal energy efficiency path. The turning times of paths are decreased by adopting the method, the improved strategy of multi-neighborhood search is proposed, the energy consumption for finding the optimum energy efficiency paths of the improved A* algorithm is lower compared with a traditional A* algorithm, the turning times of paths are also decreased relatively, and the method has effectiveness and reliability.

The invention provides a flow shop rescheduling method which belongs to the technical field of shop scheduling. The method comprises the steps of: acquiring unexpected events, rescheduling workpieces, time available for machinery and manufacturing process completion time of a current flow shop in real time; for different unexpected events, grouping processes which are started and processes which are not started; determining rescheduling objectives and constraints of the flow shop; generating a rescheduling scheme of the flow shop using the particle swarm optimization; sending the rescheduling scheme of the flow shop to each process of the flow shop for rescheduling. The rescheduling method uses a particle swarm optimization to solve the problem of flow shop rescheduling, takes account of a variety of unexpected events, to guarantee rapid response among the unexpected events, uses inserted or exchanged neighborhood search mechanisms to effectively guarantee that the rescheduling scheme is used for processing in time between two unexpected events, constantly transmits search information to the subsequent migratory birds, and constantly swaps the lead bird, thereby ensuring the diversity and stability of scheduling schemes.

The invention discloses an electric logistics vehicle scheduling method and an electric logistics vehicle scheduling system with a time window. The method comprises the steps of acquiring a distribution parameter of an electric logistics vehicle, and establishing a mixed-integer programming model according to the distribution parameter; acquiring a programming constraint parameter of the electriclogistics vehicle, and determining a programming constraint condition of the mixed-integer programming model according to the programming constraint parameter, wherein the programming constraint condition includes a programming requirement constraint condition and a charging constraint condition; performing optimal calculation on the mixed-integer programming model by use of an adaptive neighborhood search algorithm and a simulated annealing algorithm in combination with the programming requirement constraint condition and the charging constraint condition to obtain distribution path information; and completing optimization of electric logistics vehicle scheduling according to the distribution path information. According to the electric logistics vehicle scheduling method and the electriclogistics vehicle scheduling system with the time window, the distribution path of the electric logistics vehicle can be reasonably arranged to complete scheduling, the efficiency for a cargo distribution service is improved by virtue of the electric logistics vehicle, the electric energy is saved and the pollution to an environment is further reduced.

The invention discloses a biochip analysis method based on an active contour model and a cell neural network. The method comprises the following steps that improved Hough transformation is adopted to perform slant correction on a rectangular sampling point, and improved Radon transformation is adopted for a circular sampling point; initial positioning is performed on the sampling points by using a projection method, and an optimized network is generated; then the network is adaptively adjusted on the basis of neighborhood search, and secondary precise positioning is performed on the sampling points; the active contour model is optimized by using a greedy algorithm, and a CNN (Cable News Network) is utilized to classify the sampling points in accordance with signal strength; Multiple snakes are combined with the CNN, the CNN first learns about the convergence behavior of the sampling point snake with a strong signal and then guides the convergence of the sampling point snake with a weak signal, and finally, reasonable partition of the sampling points is realized; and signal data of microarray sampling points is extracted and output. By using the method, the problems of slant correction of a biochip image, difficulty in partition of sampling points with irregular shapes and sampling points with weak signals and the like are solved, automatic identification of biochip sampling points is realized, and the method is suitable for quick analysis of large-scale biochip sampling points.

The invention discloses a decision-level fusion based bridge recognition method. The method includes following steps: reading in multispectral images, full-color images and SAR (synthetic aperture radar) images, and subjecting the multispectral images to HSV (hue, saturation and value) spatial conversion; respectively performing expansion and corrosion pretreatment to components H, S and V; respectively subjecting the H, S and V to threshold segmentation; using 8 neighborhood search to remove small-area interference area to obtain land-water segmentation result; using morphological dilation and morphological corrosion operator to put water body area in order to obtain a potential bridge area; extracting textural features of the potential bridge area from the full-color images, determining threshold, and recognizing bridge; and subjecting bridge recognition results in the full-color images and the SAR images to decision-level fusion. Bridge recognition is realized by comprehensively utilizing complementary features of the multispectral images, the full-color images and the SAR images, so that correct recognition rate of bridge targets is effectively improved.

The invention relates to a laser point cloud reduction method based on dynamic grid k neighborhood search. According to the method, a curvature of a point, an average normal included angle between a point and a neighbor point, and an average distance between a point and a neighbor point are calculated by a k neighbor of point cloud data points; on the basis of the three parameters, a feature determination parameter and a feature threshold are defined, the feature determination parameter and the feature threshold are compared, and extraction and keeping are carried out on the feature points; and then secondary reduction is carried out on non-feature points by using a bounding box method, the reduced point cloud and the feature points are spliced, and thus reduced point cloud data are obtained. Compared with the prior art, the laser point cloud reduction method has the following advantages: the geometric features of the model are kept precisely; generation of a blank area is avoided; thecalculation efficiency is improved effectively; and the method has the great practical value.

The multi-parking-lot and multi-vehicle-type vehicle path scheduling control method comprises the steps of 1, establishing an objective function by taking the lowest total cost of all delivery vehicles as an objective; Step 2, performing a coding step; 3, performing population initialization; 4, evaluating all the individuals by adopting the objective function as a fitness function; Step 5, performing selection and crossover operation; step 6, performing mutation operation; 7, performing neighborhood search on each individual in the population by using an improved extreme value optimization algorithm; Step 8, calculating fitness of all individuals in the population; Step 9, performing selecting; Step 10, performing elite retention; Step 11, completing iteration in sequence; Step 12, judging whether a termination condition is met or not, the termination condition being that the number of iterations g reaches the maximum number of iterations MaxGen or the number of iterations Nu of whichthe Gb fitness value remains unchanged reaches the specified number of iterations Kbest, if yes, continuing to execute the step 13, and if not, returning to execute the step 5; Step 13, outputting the individual Gb and the fitness value fGb thereof; And 14, interpreting the optimal individual Gb and the fitness value fGb thereof. The invention aims to improve the search efficiency and convergencespeed of the algorithm.

The embodiment of the invention provides a multi-UAV mission planning method based on minimization of maximum energy consumption and device. The method comprises steps: multiple mission points are acquired; the multiple mission points are planned to an initial path; and the initial path is optimized according to an optimization manner in a preset traversing neighborhood search algorithm to obtainflight paths of M UAVs. The step of planning the multiple mission points to an initial path comprises sub steps: a starting point is determined and M points are selected from the multiple mission points to serve as first nodes of M to-be-expanded paths; after the first nodes of the M to-be-expanded paths are determined, in view of a tail-end node of each to-be-expanded path, the expansion energy consumption is calculated, and a mission point with the minimum expansion energy consumption is used as a candidate node; and the expansion energy consumption of the rest to-be-expanded paths is returned to be calculated until each mission point belongs to one of the M to-be-expanded paths, and M initial paths are obtained. Through the scheme of the invention, the flight path of multiple UAVs working together can be further effectively adjusted.

The embodiment of the invention provides an unmanned aerial vehicle base station site selection and patrol path optimization method and device. The method includes allocating target points according to Euclidean distance between the target points reconnoitered by unmanned aerial vehicles and base stations, then according to neighboring point search, planning an unmanned aerial vehicle flight path, using unmanned aerial vehicle flight duration to constrain and define an unmanned aerial vehicle patrol path, and defining upper and lower limits of the number of unmanned aerial vehicles that each base station configures, thereby obtaining an initial scheme for unmanned aerial vehicle base station site selection and patrol flight path; performing neighborhood search on the initial scheme, so as to obtain a new scheme for unmanned aerial vehicle base station site selection and patrol path after neighborhood search adjustment; calculating the total cost of the new scheme for unmanned aerial vehicle base station site selection and patrol path; and if the total cost of the new scheme is judged to be reduced compared with the total cost of the original scheme, storing the new scheme, and performing next-step iteration on the basis of the new scheme, otherwise abandoning the new scheme. The abovementioned technical scheme has the following beneficial effects: the unmanned aerial vehicle base station site selection and patrol path cost is minimized, the unmanned aerial vehicle patrol cost is greatly reduced, and the scheme is very low in time consumption.

The invention relates to an improved particle swarm optimization method based on flow production scheduling of a fuzzy due date. The optimal solution of the existing algorithm can not be found easily. The method comprises the following steps: using the penalty function for neighborhood block design of key lines based on the needs of the flow shop scheduling of the fuzzy due date, establishing a taboo table, and adopting the neighborhood search strategy, thereby enhancing the effect of optimal solution of the improved particle swarm algorithm. Since the stagnation can easily occur to the particle swarm optimization, the concepts of exchange operators and exchange sequences are introduced to reconstruct the particle location formula and the speed optimization formula of the particle swarm optimization, and the introduction of the taboo search algorithm helps the particle swarm optimization to search the best solution in the local area. Simulation experiments on the flow production scheduling of the fuzzy due date demonstrate that the improved particle swarm optimization method facilitates the overall solution.

The invention discloses a super-resolution image reconstruction method based on sparse multi-manifold embedment. The super-resolution image reconstruction method based on sparse multi-manifold embedment comprises the steps that medium-frequency and high-frequency characteristics of a set of high-resolution training images are extracted to build a medium-frequency and high-frequency characteristic training library; clustering is carried out on the medium-frequency and high-frequency characteristic training library on the basis of the multi-manifold hypothesis, and medium-frequency and high-frequency characteristic set pairs of different classifications are obtained; medium-frequency characteristics of an input low-resolution image through the method same as the method for extracting medium-frequency characteristics of the training images, the nearest medium-frequency characteristic training center of the medium-frequency characteristics is found out, and the classification of the medium-frequency characteristic training center is appointed as a neighborhood search range of the low-resolution image; the positions of sparse neighbors, from the same manifold, of each processed medium-frequency block in the classification are determined by solving a sparse optimization problem, reconstructed high-frequency blocks are obtained through the least square solution, and after processing of all the blocks is accomplished, a high-frequency image can be formed in a composite mode; the high-frequency image is added to the amplified low-resolution image, and an initially-estimated reconstructed image is obtained; the initially-estimated reconstructed image is processed through a common post-processing method, so that the final result is obtained.

The invention relates to a mobile robot path planning method based on an improved A * algorithm. The method comprises the following steps that a three-dimensional map model of a mobile robot working environment is established by adopting a grid method; in the three-dimensional map model, neighborhood search is carried out by adopting the improved A * algorithm under the condition that part of obstacles exist in grids, so that the rationality of a generated path is improved; meanwhile, the cost value from the starting node to each neighborhood node is calculated by introducing an improved costfunction of slope information, so that the path is gentler, and the optimal path is obtained on the premise of ensuring the search efficiency. The problem that the mobile robot is prone to colliding with the edge of the obstacle is solved, and the problem that an algorithm generation path is too steep in complex terrains is solved.

The invention provides an unmanned ship global path planning method based on an improved A star algorithm, which relates to the field of path planning. According to the method, a traditional A star algorithm neighborhood search strategy is changed, a search neighborhood is expanded, a traditional heuristic function is improved, a deweighting strategy is combined, and an angle factor is added, so that a result obtained during path search is distributed near a connecting line of a starting point and a target point in a biased manner, and the search efficiency is improved. According to the method, the marine geographic information is acquired by utilizing an electronic chart, the improved A star algorithm is combined with a dynamic grid method, the grid map model is constructed through grid dynamic refinement, the improved A star algorithm is applied to search for the path, the path precision gradually meets the precision requirement, and redundant path nodes are further reduced through smooth path processing.