66 results about "Shortest path search" patented technology

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The embodiment of the invention provides a cutover event hierarchical judgment method and device based on a graph database, and the method comprises the steps: taking network elements, ports and channels as nodes, inputting the nodes into the graph database, and constructing a directed incidence relation between the nodes according to hierarchical routing data; according to a cutover object of the cutover event, searching a target hierarchy channel associated with the cutover object in a graph database; constructing a routing topological graph of the target level channel, carrying out shortest path search based on the routing topological graph, judging whether a line is interrupted or not and a minimum cutover event combination causing interruption, and obtaining a judgment result of the target level channel; for the target hierarchy channel with the cutover event conflict, upwards searching a corresponding top layer business channel in a graph database; and based on the punching judgment result of the target level channel, judging whether the line of the top layer service channel is interrupted and the minimum cutover event combination causing the interruption, and obtaining the punching judgment result of the top layer service channel.

The invention relates to a cloud computing-based enterprise digital tool chain dynamic collaboration and deployment method, which comprises the following steps of: according to a data mapping relationship change list, analyzing change contents of an interface protocol and a data field by adopting a rule-based reasoning method, and according to a pre-configured interface protocol adaptation rule, establishing a data field according to the change contents of the interface protocol and the data field; generating data mapping scripts among different versions of tools; after the discarded interface is deleted, a graph-based shortest path search algorithm is adopted, the deleted discarded interface is bypassed, a data transmission path between tools is re-planned, and a complete tool chain data transmission path is re-formed; and adjusting the size of the data fragments and transmission batch parameters, optimizing the circulation efficiency of the data in the tool chain, and dynamically adjusting the execution sequence and the concurrency of each tool in the tool chain according to the optimized data circulation scheme.

The invention provides an optimized endorsement method and system for business process forms, and the method comprises the steps: obtaining a corresponding endorsement process for each business process form; according to the endorsement process, providing a corresponding endorsement optimization strategy to endorsement the business process form; wherein the corresponding endorsement optimization strategy is provided, which comprises the following steps of: formulating a parallel endorsement link for countersigning and / or signing according to an endorsement process, and constructing an endorsement path containing the parallel endorsement link; de-duplication processing is carried out on other repeated sign-off links and / or low-order sign-off links in the sign-off path; and carrying out shortest path search on the signature path after de-duplication processing, and taking the obtained shortest path as a final signature path. According to the method, the operation efficiency of the endorsement process is greatly improved, the problem of low efficiency in the traditional approval process is effectively solved, the response speed of business operation is remarkably improved, and the method has wide application prospects and popularization value.

The invention discloses a high-frequency vehicle track map matching method, which comprises the following steps of: (1) dividing a track T to be matched into a plurality of sub-tracks based on a reference line distance, and extracting key track points in the sub-tracks; (2) extracting a road where the key track point is located and a road type of the road, filtering the extracted road based on the road type, and storing filtered road data by adopting an R-Tree structure; and (3) matching an optimal matching path of the track T to be matched from the road data stored in the R-Tree structure through a hidden Markov probability matching model. From the perspective of reducing the scale of original data, trajectory and road network data are simplified. Then, space and direction analysis is carried out on the simplified trajectory, a micro-region splicing strategy is adopted to accelerate shortest path search, and a path deviation function is designed to improve the topology of a matching result; in addition, the extraction process of track motion key points is refined by fusing more road semantic information, so that a better matching result is obtained.

The invention provides a multi-unmanned aerial vehicle communication sensing cooperation method in a power grid fault scene, and the method combines unmanned aerial vehicle dynamic topology reconstruction and a multi-stage optimization strategy, and achieves efficient information acquisition for emergency scenes of damaged ground base stations and large-area power failure in disaster areas after a power grid fault. Power flow entropy is introduced to analyze power grid node vulnerability, communication unmanned aerial vehicle deployment is optimized, and an optimal communication coverage strategy is solved based on a multi-target grey wolf optimization algorithm; meanwhile, a graph theory method and a shortest path search algorithm Dijkstra are used for optimizing the inspection path of the unmanned aerial vehicle, so that the fault diagnosis efficiency is improved; under a two-stage optimization framework, in the first stage, important load nodes are preferentially covered and routing inspection is executed, in the second stage, supplementary optimization is carried out on an uncovered area, and finally a global optimal solution considering unmanned aerial vehicle resource efficiency, communication quality and information acquisition is obtained. The method improves the recovery efficiency of a post-disaster power grid, and is suitable for the emergency response of a power system in an extreme disaster environment.

The present invention belongs to the technical field of communication networks. Disclosed is an adaptive split federated learning method for a distributed AI training service in a 6G computing power network. The method comprises: establishing a 6G computing power network model, and constructing an adaptive split federated learning model for a distributed AI training service; analyzing the delays of each intelligent terminal in a model training stage and a model aggregation stage; constructing a total service delay minimization optimization problem; and using a shortest-path search algorithm to perform joint optimization on model segmentation modes, cooperative node selection modes and multi-domain resource scheduling modes in the model training stage and the model aggregation stage. In the present invention, by means of joint optimization of a model segmentation mode, node selection and multi-dimensional resource allocation in a multi-base-station scenario, the problem of network congestion caused by a single-base-station learning framework processing the aggregation of a large number of intelligent terminal models is alleviated, and the present invention adapts to the heterogeneous computing power of intelligent terminals and the differentiated channel quality in an actual network, thereby improving the efficiency and generalization capability of model training.

Disclosed in the present invention are an FPGA acceleration-based graph neural network explanation method and device, which use FPGA hardware to perform parallel acceleration on the explanation process of a node classification-oriented graph neural network and improve the node traversing and shortest path searching process of BFS, thereby optimizing the requirements of algorithm calculation and storage and accelerating the generation of an explanation result. According to the present invention, in the HN value calculation process, matrix characteristics are used to perform optimization processing on multiplication operation to convert dense matrix multiplication into sparse-dense matrix multiplication, and multi-PE parallel processing is used to optimize resource occupation, thereby greatly improving the performance of graph neural network explanation acceleration. In addition, an overall architecture that calculates and distributes tasks on the basis of FIFO storage is designed to reduce calculation differences between nodes, solving the problem of high time complexity of a node classification-based graph neural network explanation method in actual data application, and improving the time efficiency of explanations.

The invention discloses a wavefront restoration method based on multi-path integration, and relates to the technical field of wavefront measurement and restoration, and the method comprises the following specific steps: data collection and initialization: carrying out the preliminary detection of wavefront through a Hartmann sensor microlens array, dividing the space where the wavefront is located into a plurality of rectangular small regions, according to the method, a multi-path calculation phase evaluation value set is introduced, all shortest paths from an initial point to each to-be-estimated point are found by using a shortest path search algorithm, it is ensured that multiple possibilities of wavefront propagation can be considered in the calculation process, and by introducing a path weight distribution mechanism, the estimation accuracy of the wavefront propagation is improved. According to the method, geometric characteristics, optical characteristics and correlation with surrounding paths of paths are comprehensively considered, each path is endowed with different weights, forward integration is carried out on discrete data results by adopting an innovative mixed integration algorithm, local detail loss or error amplification is effectively avoided, and thus a more accurate wavefront phase value is obtained.

The invention discloses a translation memory bank retrieval and matching method based on semantic similarity, which relates to the technical field of computer-aided translation, and comprises the following steps: generating an internal edge and a cross-graph edge, calculating a one-time jump success probability, calculating edge and cross-graph edge cost based on cleanliness, correcting by using the one-time jump success probability to obtain an edge weight; generating a weighted graph, constructing an initial priority queue, searching by using a shortest path to obtain a distance matrix, calculating density based on a fuzzy similarity matrix, screening the density to obtain a genealogy center, calculating proximity, and generating candidate translations in combination with the distance matrix; static and dynamic fragment sets are constructed through a dependency syntax and BERT-NER, semantic matching precision and noise robustness are improved, a weighted graph is constructed in combination with cleanliness and transfer factors, shortest path matching is carried out, and the retrieval recall rate and quality of technical translation are improved.

The invention provides a path planning method, device and equipment and a readable storage medium, and the method is applied to a mobile device, and comprises the steps: obtaining a target map, dividing the target map into a plurality of sub-regions, and enabling the plurality of sub-regions to comprise a starting point region and an ending point region; determining an access point in the starting point area, and determining an outgoing point in the ending point area; a hybrid obstacle avoidance algorithm is utilized to determine an access path between the starting point and the access point and determine an outgoing path between the outgoing point and the terminal point, and the hybrid obstacle avoidance algorithm is a path planning algorithm combining a heuristic search algorithm and vehicle dynamics constraints; determining a main road path between the access point and the outgoing point by using a shortest path search algorithm; and determining a target path of the movable device based on the access path, the main road path and the access-out path. The driving requirements of the movable device can be met while the driving path is optimized.

The invention discloses a collaborative operation optimization method for a bus and subway integrated network. The method comprises the following steps: S1, carrying out collection and standardization processing on topological structure data, train operation timetable data and passenger travel demand data; s2, completing the construction of a multi-layer coupling space-time network; s3, K short path searching is executed, and a passenger reachable path set is generated; s4, carrying out reachability verification on the passenger path selection set under a train operation rule; s5, a scheduling scheme is formed based on the distribution probability output by the Logit model; and S6, inputting the time sequence operation data into the improved Mamba network, performing constraint check and correction on the line departure logarithm and the departure moment of the first station, and forming an evaluation index for optimizing the objective function. According to the method, collaborative optimization of the train operation time sequence and the passenger travel demand under the bus and subway integrated line network is achieved, and the method is suitable for intelligent scheduling and dynamic passenger flow regulation and control scenes of a large-scale urban rail transit system.

The invention discloses an automatic cleaning robot for a land-based culture pond and a path planning method thereof in the technical field of path planning of cleaning robots, the automatic cleaning robot for the land-based culture pond comprises an X-axis guide rail, a Y-axis guide rail, an X-axis moving mechanism, a Y-axis moving mechanism, a rotary telescopic assembly and a Z-axis cleaning structure; the rotary telescopic assembly comprises a rotary disc, a rotary disc driving mechanism and a sliding rod. The automatic cleaning robot can automatically clean the pond bottoms, pond walls and corners of the culture ponds, is high in reliability, easy to operate and good in cleaning effect, can complete full-coverage cleaning of the multiple culture ponds in the whole group, and is particularly suitable for automatic cleaning operation of the aquaculture ponds; according to the path planning method of the automatic cleaning robot, covering and cleaning of the culture pond can be better completed, and meanwhile, obstacle avoidance and automatic shortest path searching of the cleaning robot can be better completed.

The application discloses a kind of polishing robot curved surface path automatic planning method and system, comprising the following steps: S1, the three-dimensional curved surface model and triangular mesh model of to be processed are constructed, involve the robot automatic control technical field of manufacturing industry surface processing, the three-dimensional surface covering path planning problem of complex workpiece is converted into two-dimensional path planning domain key point sampling and three-dimensional shortest path search two low-complexity sub-problems in the present application, only a small amount of sampling point is sampled on two-dimensional path planning domain, sampling process relies on spatial index framework, avoid global traversal of unordered point cloud, the nearest neighbor search positioning three-dimensional coordinates using KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected by graph search algorithm, form continuous processing path, to greatly reduce the amount of calculation, it is favorable for the real-time response of path planning, alleviate the problem that traditional online planning is time-consuming and difficult to respond in real time.

The invention discloses an automatic control method, system and equipment based on industrial big data analysis, and relates to the technical field of industrial big data analysis. The automatic control method based on industrial big data analysis comprises the steps of receiving order data in real time, and generating first data corresponding to a target product based on the order data; executing shortest path search in a preset process database based on the first data; collecting multi-dimensional historical data, and inputting the multi-dimensional historical data into the trained time sequence prediction model to obtain a load prediction value of each station in a future time period; performing rolling scheduling on the process chain by adopting a reinforcement learning algorithm to generate a work order sequence with a timestamp; and issuing the work order sequence with the timestamp to an edge controller, and carrying out adaptive adjustment on field equipment. According to the technical scheme, automatic control and self-adaptive scheduling based on industrial big data can be realized in a multi-variety and small-batch mixed flow production scene, and the production flexibility and the resource utilization rate are improved.

The invention discloses a shortest path searching method and a social network relation analysis method, and belongs to the technical field of network information, the social network relation analysis method comprises the steps that a knowledge graph of a social network is constructed, vertexes in the knowledge graph comprise users or entities, and edges in the knowledge graph represent the interaction relation between the vertexes; calculating a shortest path from the source point to the target point based on a shortest path searching method; wherein the source point and the target point belong to a vertex set of the knowledge graph; and obtaining a relation analysis result of the source point and the target point according to the interaction relation on the shortest path from the source point to the target point. According to the method, large-scale social network graph data with a complex structure can be efficiently and stably processed.

The application discloses a kind of grain map neural network's weld ultrasonic beam path correction method, it is related to the field of welding structure nondestructive testing, by obtaining the EBSD data of weld area, extract grain orientation, grain boundary type and spatial distribution characteristics, construct grain map structure, and calculate initial propagation time weight based on anisotropic sound velocity model;Using graph neural network to learn end to end for grain node and grain boundary edge features, output corrected edge propagation time weight;The time of flight from element to imaging pixel is obtained by shortest path search, and the time of flight predicted by neural network surrogate model is fused and corrected;Finally, the corrected time of flight is used to perform full focus imaging, and the weld defect image after optimizing the beam path is obtained.The method combines material microstructure characteristics and machine learning model, solves the problem that the beam path description is not fine enough in existing weld ultrasonic testing, significantly improves the accuracy and reliability of defect detection.

The invention discloses an intelligent process generation method and device based on knowledge graph embedding similarity matching, and belongs to the technical field of intelligent manufacturing process planning, and the method comprises the steps: constructing an assembly process knowledge graph; an assembly process sub-graph is extracted with the process as the center and serialized; the method comprises the following steps of: obtaining node-level embedding through a twin Transform encoder with shared parameters; obtaining graph-level embedding through batch embedding splicing and a pooling layer; performing bilinear interaction on an assembly subject, a relationship and an object by using an assembly element interaction neural tensor network, and extracting deep semantic features; outputting a similarity score of the two sub-graphs; retrieving the most similar process node based on the score; and executing shortest path search on the directed acyclic graph to complement missing nodes, and generating a target assembly process path. According to the method, rapid matching and automatic generation of different and semantically similar processes are realized, and the process reuse rate and the generation efficiency in a multi-variety, small-batch and rapid iterative manufacturing environment are remarkably improved.

The present invention provides an improved pedestrian block emergency simulation method, comprising: preprocessing the road network vectors of the pedestrian block to generate an undirected graph; classifying the individual characteristics of people corresponding to the pedestrian block based on a preset theory; determining the neural response strength of different individuals based on the distance between people in the pedestrian block and the classified individual characteristics; constructing an intelligent individual speed function based on the neural response strength and the undirected graph; defining panic ratios for the individual characteristics of different classifications, and constructing corresponding path evacuation models based on the intelligent individual speed function; constructing the path evacuation models corresponding to the different individual characteristics, performing a shortest path search and obtaining a shortest time state sequence, and conducting an emergency evacuation simulation in a virtual scene constructed in the pedestrian block based on the search results and the obtained results. This improves the efficiency of emergency evacuation.

A method and device for interpreting a graph neural network based on FPGA acceleration propose to use FPGA hardware to accelerate interpretation process of the graph neural network oriented to node classification in parallel, and improve node traversal and shortest path search of BFS, thereby optimizing requirements of algorithm calculation and storage, and accelerating generation of interpretation results. During calculating HN values, the present disclosure optimizes multiplication operation using the matrix characteristics, transforms the dense matrix multiplication into sparse-dense matrix multiplication, and optimizes the resource occupation using multi-PE parallel processing, greatly improving performance of graph neural network interpretation acceleration. Moreover, an overall architecture based on FIFO storage calculation task distribution is designed to reduce calculation difference between nodes, solving the difficulty of high time complexity of the graph neural network interpretation method based on node classification in actual data applications and improving the time efficiency of interpretation.

Embodiments of the present disclosure provide a method and apparatus for hierarchical conflict judgment of cutover events based on a graph database. The method includes: entering network elements, ports, and channels as nodes into the graph database, and constructing a directed association relationship between the nodes according to hierarchical routing data; searching in the graph database for a target-level channel associated with the cutover object according to the cutover object of the cutover event; constructing a routing topology graph of the target-level channel, performing a shortest path search based on the routing topology graph, judging whether the line is interrupted and the minimum cutover event combination causing the interruption, and obtaining a conflict judgment result of the target-level channel; for the target-level channel with cutover event conflicts, searching upward in the graph database for the corresponding top-level service channel; and, based on the conflict judgment result of the target-level channel, judging whether the line of the top-level service channel is interrupted and the minimum cutover event combination causing the interruption, and obtaining a conflict judgment result of the top-level service channel.

The application discloses a land surveying method and device based on multi-source data fusion, and relates to the technical field of surveying, which comprises the following steps: collecting three-dimensional geodetic coordinate data and three-dimensional space point cloud data, obtaining starting three-dimensional coordinate and ending three-dimensional coordinate of surveying, constructing a triangulated irregular network (TIN) terrain model and a shortest path search model on the ground, searching for a starting node and a target node in a weighted undirected graph, calculating the shortest path on the ground and the ground distance, rendering the TIN terrain model to draw the shortest path on the ground, and setting through points and forbidden areas to update the shortest path on the ground and the ground distance. The application breaks through the dependence of traditional land surveying on the line-of-sight condition by fusing multi-source data and constructing continuous digital terrain, intelligently calculates and visualizes the shortest path on the ground, avoids the error accumulation of manual segmented operation, provides interactive path replanning capability, and significantly improves the accuracy, efficiency and scientific nature of planning and decision-making of ground surveying under complex terrain.

The application relates to a ground object fused surface vector path planning method suitable for special vehicles, which comprises the following steps: reading and fusing a ground object classification layer according to the driving capability of a user-selected special vehicle; loading a layer fused vector file; obtaining a user-specified starting point A and ending point B; traversing each vector block in the fused vector; finding a vector block containing both A and B and gridizing the vector block; constructing a dijkstra dictionary for the grid points; finding the nearest starting and ending points in the vector block; and performing single-source dijkstra shortest path searching between the starting and ending points. The ground object fused surface vector path planning method suitable for special vehicles can intelligently adapt to the specific requirements of different vehicles, so that precise and efficient path planning can be realized in a complex ground object environment.

The application discloses a shortest path divide-and-conquer search method based on a condensation level, improves the operation efficiency of an algorithm by introducing network layering, limiting a search level, divide-and-conquer search and the like, wherein the network layering is adopted to divide network nodes according to a condensation level clustering method, a plurality of level subgraphs are obtained, and the complexity of the search is reduced; the search range is limited within a certain level by limiting the search level, and invalid calculation in the search process is avoided; meanwhile, in order to further improve the search efficiency, the divide-and-conquer search is adopted to divide the whole search process into a plurality of subtasks and perform simultaneously, and the whole search method can accurately and efficiently perform the shortest path search in an edge computing network.

This invention discloses a collaborative operation optimization method for an integrated bus and metro network, comprising the following steps: S1, collecting and standardizing topology data, train timetable data, and passenger travel demand data; S2, constructing a multi-layer coupled spatiotemporal network; S3, performing K-shortest path search to generate a set of passenger reachable paths; S4, verifying the reachability of the passenger path selection set under train operation rules; S5, constructing a scheduling scheme based on the allocation probabilities output by the Logit model; S6, inputting the time-series operation data into an improved Mamba network to constrain and correct the number of train departure pairs and the first station departure time, forming an evaluation index for the optimization objective function. This invention achieves collaborative optimization of train operation sequence and passenger travel demand in an integrated bus and metro network, and is applicable to intelligent scheduling and dynamic passenger flow control scenarios in large-scale urban rail transit systems.

The invention provides a multi-robot path searching method for an optical communication scene, and belongs to the field of robot path planning, and the method comprises the steps: obtaining an optical communication scene road network containing optical communication sites and non-optical communication sites, and obtaining a simplified road network and road network nodes through the fusion of the non-optical communication sites; on the basis of neglecting conflicts among robot paths, forming an initial path scheme through a shortest path search algorithm, judging whether space-time conflicts exist or not, generating corresponding space-time constraints according to a space-time conflict judgment result, and recalculating to form a new path scheme through the shortest path search algorithm with the space-time constraints; and repeatedly executing the shortest path search algorithm with the space-time constraint until an optimal path scheme is obtained. The problems that in the prior art, due to the fact that continuous real-time wireless communication is relied on, effective conflict detection and avoidance cannot be carried out in an optical communication discontinuous communication scene, the robot collision risk is high, and the planning efficiency is low are solved.

The invention discloses a cross-city trajectory generation method combining deep learning and shortest path search, and relates to the technical field of deep learning and trajectory generation. The method specifically comprises the steps of extracting topological features by using a spatial syntax method, learning invariant topological representation and predicting travel cost by adopting a decoupling adversarial domain adaptation algorithm, and finally learning urban invariant human travel preferences to generate a trajectory. Compared with an existing technical method, the generalization ability of track generation is remarkably improved.

A multi-server parallel computing static encrypted graph shortest path search method comprises a preprocessing algorithm, a graph encryption algorithm and a shortest distance / path query algorithm. An encrypted graph adjacency linked list is stored in a data encryption algorithm, an application-driven graph segmentation algorithm is adopted for the designed encryption algorithm, a logarithmic factor is added for a graph segmentation device according to theoretical analysis of the algorithm to further improve the segmentation device, overhead parameters are obtained by using deep learning, and the graph adjacency linked list is obtained. And finally, a higher-efficiency segmentation scheme for the graph under the shortest path is realized. The method has the beneficial effects that a Dijkstra loss function in a ciphertext state is obtained through analysis by using a deep neural network and a graph calculation theory, and the function is used for carrying out hybrid segmentation processing on a graph; by utilizing a multi-server parallel technology, the execution speed of the shortest distance query on the encrypted graph is improved, and the pre-calculation overhead is reduced compared with the prior art while the efficient query is ensured.

This invention discloses a method for obtaining K shortest paths in a city that considers real-time traffic conditions, comprising: 1. Constructing a city road network based on traffic information at the initial time; 2. Defining and initializing parameters; 3. Searching for the k-th shortest path from the starting intersection node to the ending intersection node at the current time; 4. Searching for the (k+1)-th shortest path from the starting intersection node to the ending intersection node at the current time; 5. Obtaining the K shortest paths from the starting intersection node to the ending intersection node at the next time after updating traffic conditions, based on real-time traffic conditions. This invention can obtain K optimal travel paths that better reflect actual traffic conditions while improving computational efficiency, thereby ensuring the stable and efficient operation of social traffic.

The application discloses a multi-path video real-time splicing processing method based on an embedded platform and particularly relates to the technical field of image processing, which comprises the following steps: obtaining multiple images with consistent time stamps through hardware frame synchronization, calculating the light flow module length of the overlapping area after pre-alignment through cylindrical projection to generate a binary mask, establishing a control grid in the overlapping area to perform sparse disparity estimation and correction to obtain a sparse disparity map, up-sampling the disparity map to a dense disparity map, constructing a composite energy map containing a spatial color gradient cost and a time penalty cost, modulating the spatial cost by using the binary mask and smoothing the time penalty map, obtaining a dynamic avoidance seam through shortest path search, and forming an adaptive feathering band with the seam as the center. The application effectively eliminates static disparity ghosting and motion object half-cut artifacts under limited computing resources and realizes high-quality real-time panoramic splicing.