151 results about "Minimum spanning tree" patented technology

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

The invention discloses a cross-institution financial data federated learning modeling system and a privacy compliance verification method, and relates to the technical field of financial data processing, the system comprises a federated aggregation module used for a server to align financial time sequence characteristics of each institution through a timestamp hash alignment strategy, and a privacy verification module used for verifying the privacy compliance of each institution; aggregating the aligned financial time sequence characteristics by using a federated time convolutional network, and generating time sequence model parameters for capturing cross-mechanism global time sequence dependence; the federation reasoning module is used for generating a minimum spanning tree representation of a global enterprise association relationship based on a security multi-party computing strategy by the server side on the basis of a local sub-graph of the enterprise association relationship constructed by each institution client side; the server performs cross-mechanism graph reasoning by using a federated graph attention network and an aggregation graph embedding vector and combining minimum spanning tree representation, and constructs graph model parameters for identifying enterprise associated risks; and the model joint training module is used for the server to fuse the time sequence model parameters and the graph model parameters so as to determine global model parameters.

The invention discloses a main station big data-based low-voltage transformer area topology hierarchy identification method and system, and the method comprises the steps: constructing a multi-dimensional voltage feature vector through the voltage time sequence data, collected by a main station side, of a transformer area general meter and a user ammeter, and forming an electrical feature matrix through feature extraction; and on the basis, judging the station area affiliation and the phase line affiliation of the user electric meter through similarity calculation. Then, generating a stable consensus cluster based on multi-time window clustering and the copolymerization matrix, and mapping the cluster into a virtual electric meter box node; constructing a minimum spanning tree and carrying out directionalization to obtain an initial radial topological structure; further introducing an electrical consistency criterion based on correlation of voltage drop and subtree current, performing multi-candidate structure optimization on the preliminary topology, and finally obtaining an optimal transformer area topology hierarchical structure consistent with a physical rule; high-precision automatic identification of the low-voltage transformer area topology can be realized without manual participation, and effective support is provided for digital operation and maintenance of a power distribution network.

The invention provides a multi-axis linkage engraving equipment control algorithm optimization method, which belongs to the technical field of engraving equipment, and comprises the following steps of: decomposing a model into a plurality of curved surface segmentation segments according to a curvature change rate, and performing NURBS curve parameterization processing on each segment to generate control point coordinates and weight coefficient data; a dynamic weight adjustment model is adopted to dynamically calculate a path planning weight coefficient according to a tool cutting force value, a surface roughness demand value and a machining efficiency index value, a minimum spanning tree path algorithm is utilized to carry out path connection sequence planning, and a layered interpolation operation framework combining preprocessing interpolation calculation and real-time interpolation compensation is established. And the optimized interpolation coordinate data is converted into an axis control instruction sequence containing a position instruction, a speed instruction and an acceleration instruction, and the technical problem that the interpolation calculation precision and the real-time performance are difficult to balance when the multi-axis linkage carving equipment is used for machining a complex curved surface is solved.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention provides an offshore wind plant current collection network topology optimization method based on an improved grey wolf optimization algorithm, and the method comprises the steps: S1, obtaining basic data of an offshore wind plant, initializing the grey wolf algorithm, and generating an initial solution set; s2, adopting polar coordinates and a minimum spanning tree to optimize and expand the initial solution set; s3, optimizing a connection mode in each group; s4, executing the optimal connection of each group of fan combination; s5, performing optimal solution set exploration on the grey wolf algorithm by adopting an operator; s6, repeatedly executing until a preset maximum number of iterations is reached, and generating a topology model of the current collection system of the offshore wind plant; the method can improve the initial solution quality and the convergence efficiency, gives consideration to the global search capability and the local optimization precision, and achieves the quick and effective processing of geometric constraints.

The invention belongs to the technical field of optical engineering, and discloses a tree real skeleton point extraction method and system based on a deep neural network and a minimum spanning tree algorithm, a terminal and a storage medium, and the method comprises the steps: obtaining original point cloud data; predicting the original point cloud data based on a deep neural network, and obtaining the vector, radius and category information of each point cloud; performing centralization processing on the original point cloud data according to the vector, radius and category information of each point cloud, and performing initial skeleton extraction by using a minimum spanning tree algorithm to obtain initial skeleton points; simplifying and smoothing the initial skeleton point, and updating to obtain a real skeleton point based on a real skeleton point updating method of Euclidean clustering; and outputting the real skeleton point. The tree real skeleton point extraction precision is improved, and the reconstruction precision of the tree model is improved.

The invention discloses a whole wind network wind volume deduction method based on limited actual measurement wind volume and a topological structure, and relates to the technical field of wind volume deduction. The relation between the spanning tree air volume and the residual tree string air volume is obtained; the ratio of the number of independent loops to the number of total branches in the mine ventilation network is 1 / 3; establishing a maximum air volume residual tree string model and a minimum air volume residual tree string model; a corresponding minimum air volume spanning tree and a corresponding maximum air volume spanning tree are obtained; respectively putting known air volumes into the maximum remainder tree string, the minimum remainder tree string or the maximum spanning tree and the minimum spanning tree to construct four air volume deduction models; and solving the deduction model by adopting an FLA optimization algorithm, and outputting the air volume of each branch of the whole wind network. According to the method, through air volume adjustment and global optimization deduction, the air volume of the whole mine ventilation network is deduced rapidly and accurately, the air volume monitoring defect in the prior art can be effectively overcome, and more accurate and timely mine air volume state sensing is provided.

The invention provides a power collection system topology optimization method and device based on a variable weight minimum spanning tree, and relates to the technical field of offshore wind power topology optimization. The method comprises the following steps: grouping a plurality of fans based on the angle difference among the plurality of fans according to the position information of the offshore booster station to obtain a plurality of fan sets; a Delaunay triangulation method is adopted to construct a reference grid connecting line of each fan set; and based on the reference grid connection line of each fan set, generating the optimal topology of the to-be-processed offshore current collection system based on a variable weight minimum spanning tree algorithm. According to the method, the grouping clustering method, the Delaunay triangulation method and the variable weight minimum spanning tree algorithm are combined, and the topology design of the offshore wind plant current collection system is efficiently, economically and rapidly achieved. According to the invention, topological connection and cable specification type selection can be optimized synchronously, and a global optimal solution is sought between the cable path length and the type cost difference caused by the change of the current-carrying capacity, so that the fine control of the overall cost of the current collection system is realized.

The embodiment of the application provides a kind of welding path planning method and device, method includes: by machine vision algorithm to the object to be processed is three-dimensional scanning, obtains three-dimensional point cloud data, by the segmentation algorithm based on deep learning to three-dimensional point cloud data is data analysis, and the geometry feature is identified;With the geometry feature is constructed into a undirected graph, and the optimal connected path of the weld center line is found on the graph by minimum spanning tree algorithm, and the optimal connected path is constructed to obtain the fine three-dimensional model of processing object;According to the three-dimensional coordinates of weld search site, current welding gun attitude and target weld length, determine the optimal search height, search depth, inclination angle and offset of welding gun, generate corresponding welding path planning based on search height, search depth, inclination angle and offset by welding process simulation algorithm;The application can effectively improve the precision and efficiency of welding path planning.

The invention provides a low-code and component intelligent collaborative application construction method based on a dual-drive architecture, and belongs to the technical field of computer programming. The method comprises the following steps: establishing a component dependency digraph, optimizing a component collaborative relationship by adopting topological sorting, graph coloring and a minimum spanning tree algorithm, collecting multi-dimensional performance indexes to establish a monitoring matrix, processing monitoring data by utilizing a collaborative optimization model containing a gating information propagation mechanism, and generating a dynamic scheduling decision vector; the load change of the system is judged through a change rate threshold equation, quick or slow response adjustment is executed, and finally, a low-code component library and a visual configuration interface are established to realize intelligent collaboration and dynamic resource allocation among components. The technical problems that the collaboration efficiency among low-code platform components is low and intelligent dynamic scheduling cannot be achieved according to system load changes are solved.

The invention discloses a multi-fault diagnosis method for lithium battery sample scarcity and data imbalance, and the method comprises the steps: firstly constructing a feature extraction model based on a residual neural network, introducing an improved multi-factor imbalance index (MFI), carrying out the analysis of real-time monitoring batch feature distribution through employing a minimum spanning tree, and carrying out the real-time monitoring of the real-time monitoring batch feature distribution; therefore, the loss function and the sample weight are dynamically adjusted, and the learning stability of majority classes and the recognition precision of minority classes are both considered. On the basis, a prototype vector of a normal working condition is obtained through sample feature mean value calculation, and a prototype network (ProtoNet) is constructed to serve as an anomaly detector; after features of a test sample are extracted through the ResNet-MFII module, the Euclidean distance between the test sample and a normal prototype is calculated, if the Euclidean distance exceeds a set threshold value, it is judged that the test sample is abnormal, and detection of unknown or rare faults is achieved. The system finally outputs fault types and abnormal alarms, and high-precision recognition of multiple types of faults such as short circuit and aging of the lithium ion battery is achieved.

The invention provides a three-dimensional steel structure full-coverage path planning inspection method based on topology reconstruction, and relates to the technical field of industrial robots, and the method comprises the steps: obtaining a model file of a to-be-inspected three-dimensional steel structure; constructing an original vertex set and an original edge set according to the model file; performing adaptive clustering on each original vertex in the original vertex set through a density-based spatial clustering algorithm to obtain a cluster; taking the geometric center of the original vertex in each cluster as a topological node, and establishing a mapping relation between the original vertex and the topological node; carrying out topology reconstruction on the original edge set, and constructing an undirected weighted graph; stitching non-connected sub-graphs in the undirected weighted graph through a K-dimensional tree and a minimum spanning tree algorithm to obtain a fully connected graph; generating a full-coverage continuous path sequence through a Chinese postman algorithm; the full-coverage continuous path sequence is converted into a three-dimensional space coordinate sequence, and the three-dimensional steel structure to be inspected is inspected according to the three-dimensional space coordinate sequence.

The invention relates to an IPv6 lightweight network equipment surveying and mapping method based on behavior fingerprints and hop count verification. Comprising the following steps: collecting metadata of original traffic in an IPv6 network; selecting a multi-dimensional feature vector from the metadata, processing the multi-dimensional feature vector, and generating a unique and stable fingerprint identifier for each device; sending a detection packet to the target device, calculating a forward path hop count, estimating a return path hop count of a response packet to obtain a reverse path hop count, and detecting the path by comparing the forward path hop count with the reverse path hop count; taking the fingerprint identifier as a topological node, taking a bidirectional path hop count detection value as a topological edge weight between devices, constructing an initial topological skeleton through a minimum spanning tree algorithm, and performing clustering optimization in combination with auxiliary data to obtain a network topological structure; and automatically deducing a hierarchical relationship according to the fingerprint identifier, the node hop count, the connection closeness and the behavior consistency, and generating an interactive logic topological graph for displaying abnormal nodes and associated paths.

The invention discloses a point cloud and image fused robot structure modeling method and system. Firstly, the invention provides an image boundary guided point cloud clustering method, image edge and region consistency features are fused, edge consistency loss and clustering contour score are taken as a joint optimization target, and the problems that pure point cloud clustering is fuzzy in component boundary and easy to merge in texture similar structures are solved; and the separation precision of key connecting parts is obviously enhanced. Secondly, the invention innovatively provides a motion consistency analysis mechanism driven by a time sequence and an image, carries out linkage optimization on a spatial clustering result under the guidance of the image and a multi-frame motion track, constructs a motion correlation graph, and deduces the structural topology of the robot based on a minimum spanning tree. And high-robustness component connection relation restoration is realized under the condition of no label supervision. The strategy is excellent in performance on simulation and physical data sets, and the topology accuracy is improved by 6.2%.

The invention discloses an automatic generation method of an intelligent unmanned system city simulation test scene, and relates to the field of simulation scene construction and intelligent unmanned system test.The method comprises the steps that based on terrain boundary parameters, a plurality of rectangular areas with function labels are generated through Poisson disc sampling and a distance gradient algorithm; constructing a navigation grid representing the passable area; arranging a plurality of road intersections by adopting a constrained Poisson sampling algorithm; calculating the heart weight based on each intersection, and calculating the connection strength between the intersections through a gravity model; generating a global road network topology structure by adopting an improved minimum spanning tree algorithm; performing path search by adopting an algorithm A to obtain an optimal path, and automatically generating a road model and ancillary facilities; and extracting road network, road parameters and subsidiary facility information in the generated urban scene, and generating a standardized scene description file according to an OpenDRIVE standard format. And the construction efficiency of the simulation test scene, the test effectiveness and the cross-platform compatibility are improved.

The invention provides a method for intelligently extracting key geometric features of a target object through a three-dimensional laser scanner, and belongs to the technical field of actual measurement of an internal forming surface of a wind tunnel body. Constructing an association degree matrix by using a minimum spanning tree algorithm in a graph theory, calculating three clustering center values based on K-means clustering analysis, determining a boundary threshold value, starting a scanning precision adaptive strategy, dynamically adjusting scanning parameters through a feature extraction optimization model, extracting key geometric features by using a geometric feature recognition algorithm, and carrying out feature extraction on the key geometric features; spatial context information is automatically extracted by using a Transform attention mechanism, and finally a key geometric feature extraction result of the target object is output through sequential processing of six parallel computing units, so that the technical problem of insufficient geometric feature extraction precision of three-dimensional laser scanning point cloud data in a complex environment is solved.

The invention relates to the technical field of forest fire prevention monitoring, and discloses an unmanned aerial vehicle forest fire prevention line patrol path optimization method comprising the following steps: S1, obtaining multi-source data of a forest area, the multi-source data comprising terrain elevation, vegetation coverage, historical fire danger points and real-time meteorological data; s2, constructing an initial line patrol path network based on the multi-source data, wherein the initial line patrol path network generates a tree topology structure by taking the key fire danger point as a center; and S3, optimizing the line patrol path by adopting a dynamic risk assessment model, wherein the dynamic risk assessment model is based on a tree topology structure. Key fire danger points are established through historical fire danger frequency and vegetation flammability classification, a tree topology path is generated by adopting a minimum spanning tree algorithm, the self-adaptive coverage density of a high-risk forest region is realized, the limitation of static uniform distribution of traditional grid line patrol is solved, and unmanned aerial vehicle line patrol resources dynamically incline towards the high-fire-danger region.

The application discloses a logistics transportation and distribution scheduling method, an electronic device and a storage medium, and comprises the following steps: based on the position distribution information of a distribution warehouse, taking the distribution warehouse as a node, an n-ary tree corresponding to the distribution warehouse is constructed; based on the article information of the distribution warehouse and the demand order of a guarantee object, nodes in the n-ary tree are traversed to obtain a target warehouse meeting the demand order; based on a minimum spanning tree and a reverse topology algorithm, an optimized transportation route between the guarantee object and the target warehouse is determined; and based on a tabu search algorithm, the optimized transportation route is optimized to obtain a target transportation path, and the demand order of the guarantee object is transported and distributed according to the target transportation path. The technical scheme of the application solves the technical problems of the existing logistics transportation and distribution scheduling method, such as insufficient adaptability to changes, limitation of actual application of an algorithm, trade-off difficulty in multi-objective planning, inflexibility of a system, and obstacles in resource integration, and realizes providing a comprehensive and accurate transportation scheme.

The invention provides a transformer area topology identification method and system based on a graph neural network, and relates to the technical field of power system power distribution networks, and the method comprises the steps: taking an HPLC concentrator as an edge module, carrying out the modeling of a transformer area into a graph structure, taking a transformer and an intelligent electric meter as the nodes of the graph structure, and taking the potential electrical connection between the nodes as the edge of the graph structure; the edge module collects multi-source data of each intelligent electric meter in a transformer area, and after the multi-source data is preprocessed, node feature vectors and edge feature matrixes are obtained according to the preprocessed multi-source data; the edge module learns node features and edge features by using a graph neural network based on the obtained node feature vectors and edge feature matrixes, and calculates the probability of electrical connection between any two nodes; and the edge module adopts a minimum spanning tree algorithm to generate a transformer area topological structure according with the set physical constraint according to the obtained result. According to the invention, high-precision automatic identification of the transformer area topology can be realized.

The invention relates to the technical field of high-current particle beam diagnosis, in particular to a high-energy ion beam three-dimensional reconstruction method and system based on secondary electron emission signals. According to the technical scheme, the method comprises the steps of data preprocessing and conversion, geometric space up-sampling, point assignment generation and model boundary division and output. According to the method, the spatial sparsity of original data is overcome through geometric upsampling, the fidelity of physical attribute reconstruction is remarkably improved by utilizing a topological structure based on a minimum spanning tree and a primary function frequency superposition model, automatic and accurate division of beam boundaries is realized according to a physical threshold value, an end-to-end automatic processing flow is formed, and the method has the advantages that the method is simple and convenient to operate and high in practicability. And outputting a visual three-dimensional model supporting interactive analysis, systematically solving the problems of data sparsity, interpolation distortion and boundary fuzziness in the prior art, and realizing high-precision and high-efficiency reconstruction and visual analysis of the three-dimensional form of the high-energy ion beam.

The application provides a sandwich combination structure lower hanging steel beam coordinate precision control method, and belongs to the technical field of wind tunnel construction. The application establishes an independent reference control network, sets a forced closed control point to block error linear transmission, calculates the lower hanging steel beam camber, and offsets the dead load deflection through reverse prefabricated bending processing. A buried part rigid positioning frame is installed and the position deviation is monitored. A hydraulic synchronous jacking device is used to iteratively adjust the posture of the steel beam. Pre-stressed tension is applied to realize reverse arch shaping. Finally, the least squares adjustment technology and the minimum spanning tree optimization algorithm are used to reasonably distribute the section cumulative error between the forced closed control points, thereby solving the technical problem of excessive steel beam coordinate precision caused by large-span multi-section construction cumulative error.

The invention discloses a wind power plant current collection system topology design method and system based on an improved genetic algorithm, relates to the technical field of system topology design, and aims to solve the core problems of collaborative optimization of fan grouping and submarine cable connection and accurate matching of current-carrying capacity and cost under high-dimensional constraints. According to the method, a parameter calculation module is combined with a formula to determine the maximum feeder fan number and the number range, a grouping connection module combines polar coordinate grouping with a minimum spanning tree algorithm to construct an initial scheme, a cost modeling module calculates the cost through an exclusive function, and an iterative optimization module dynamically adjusts the grouping and fan number based on an improved genetic algorithm. And the constraint screening module eliminates violation schemes through a penalty formula, and finally generates optimal and suboptimal topological graphs containing core parameters. According to the method, the high-dimensional optimization problem is layered and disassembled, the constraint satisfaction degree and the economical efficiency are considered, the submarine cable investment cost is greatly reduced, the design efficiency and the engineering feasibility are improved, and the method is suitable for topology design of various offshore wind plant current collection systems.

The application discloses a kind of wind farm power collection system topology design method and system based on improved genetic algorithm, it is related to system topology design technical field, to solve the core problem of wind turbine grouping and submarine cable connection collaborative optimization under high-dimensional constraint, accurate matching of load flow and cost.The method determines the maximum number of wind turbines and the number range of feeder by parameter calculation module simultaneous equations, grouping connection module constructs initial scheme by polar grouping combined with minimum spanning tree algorithm, cost modeling module calculates cost by exclusive function, iteration optimization module dynamically adjusts grouping and the number of wind turbines based on improved genetic algorithm, constraint screening module excludes illegal scheme by penalty formula, and finally generates optimal and suboptimal topology graph containing core parameters.The application divides high-dimensional optimization problem into layers, considers constraint satisfaction degree and economy, significantly reduces submarine cable investment cost, improves design efficiency and engineering feasibility, and is suitable for various offshore wind farm power collection system topology design.

The application discloses a conflict-aware automatic routing method and system for analog integrated circuits, and relates to the technical field of computers.The method comprises the following steps: constructing a minimum spanning tree based on layout file information of the analog integrated circuit, generating a topology of the analog integrated circuit based on the minimum spanning tree, and obtaining target topology information; performing access point planning processing based on the target topology information; performing initial path planning based on the target topology information and access point planning information, performing line network distribution on the initial path information based on a cross relationship graph, and obtaining initial path information after line network distribution; performing 3D monotonic routing processing on the initial path information after line network distribution; and performing enhanced routing processing based on 3D monotonic routing information by using an A algorithm, and obtaining target routing information of the analog integrated circuit.The application realizes reliable routing in a complex layout environment, and ensures that the routing result meets production process rules.

The application provides a heterogeneous cooperative path planning method for a marine floating object target search task. A Parcel v2 particle simulator based on a Lagrange framework is used to predict the future trajectory and position of a plurality of particles according to real sea waves, ocean currents, wind fields and other information, and then a Parzen window method based on a Gaussian kernel function is used to obtain a target probability map. In the traditional GBNN method, the regional centroid and motion constraint are added to improve the external stimulation value of the neuron to plan the cooperative search path of the plurality of unmanned ships, so that the local optimum can be effectively avoided and the search efficiency is improved. A solar unmanned aerial vehicle is used as a communication relay of the plurality of unmanned ships, an optimal communication network connection is obtained by using a minimum spanning tree, and then the path of the unmanned aerial vehicle is planned. The application can improve the method for solving the marine search target of the plurality of unmanned ships, so as to improve the efficiency of the marine search target of the unmanned ships.

The invention discloses a power distribution network topological structure construction method and system adaptive to a double-high characteristic, and relates to the field of topology, and the method comprises the steps: collecting double-high characteristic data through a sensor, building an evaluation system, and calculating a node comprehensive influence index; k-means clustering grouping is carried out based on the index and the geographic position, an intra-group minimum spanning tree initial topology is constructed with the transformer substation as the center, and dynamic adjustment is carried out after simulation verification; then, the inter-group power exchange requirement is determined according to the load and new energy characteristics, and the optimal contact topology is determined through multi-objective optimization; and finally, the intra-group topology and the inter-group topology are integrated to form a complete structure, and a verification model is established to test the performance under different scenes. The method has the advantages that the challenges of high-proportion new energy and power electronic equipment are effectively coped with and stable and efficient operation of a power distribution network is guaranteed by comprehensively collecting double-high data, scientifically clustering nodes, hierarchically optimizing intra-group and inter-group topologies and combining double-high scene simulation dynamic adjustment and performance verification.

The invention discloses an overhead line construction three-dimensional model simplification method, system, equipment and medium, and belongs to the technical field of three-dimensional models.The method comprises the following steps that an original three-dimensional model is obtained, all vertexes and edges of the original three-dimensional model are extracted, and an initial vertex set is formed; calculating a distance relationship between vertexes based on the extracted initial vertex set, and constructing a distance matrix; according to the distance matrix, performing region division on the initial vertex set through a clustering strategy to obtain a vertex cluster set; calculating representative vertexes for all vertex clusters in the vertex cluster set to form a simplified vertex set, and generating a simplified graph structure; and optimizing the simplified graph structure to generate a simplified three-dimensional model. According to the method, the vertexes and the edges are extracted from the original three-dimensional model, the model is simplified by using the clustering algorithm and the minimum spanning tree algorithm, the key structural features of the model are reserved, the real-time performance of the model is improved, and the topological relation integrity of the model is maintained.

The application relates to the technical field of chip design, and particularly discloses a routing method and system based on timing criticality, which abstracts physical layout into a node network, constructs a weighted cost function of total wiring length and critical path length, adopts a minimum spanning tree algorithm to iteratively expand a path and record a parasitic parameter, calculates a merging cost value based on the product of the length of a coincident path and the timing criticality of a node, generates a virtual node by merging a node pair with the maximum benefit, and cyclically optimizes until a convergence condition is met, so that the dual objectives of wiring resource optimization and timing critical path delay reduction are achieved. The method is based on a more accurate time delay model, defines a target function which comprehensively considers the overall routing cost and the routing cost of a timing critical load node, makes timing analysis more accurate, reduces the signal delay and the size of a clock cycle of a load node according to the criticality of the load node, and effectively increases the running speed of a chip.

The application provides a TIN network construction method based on a spatial minimum spanning tree and a convex hull polygon, relates to the field of geographic information system data processing, and is used for solving the problems of low triangular network quality, topological errors and insufficient construction efficiency in the existing TIN construction method. In order to improve the construction quality of the TIN and maintain the global optimality, the spatial minimum spanning tree and the convex hull polygon of an input scattered point set are constructed in advance in the application, and then the two geometric structures are used as a framework to construct the TIN. In the growth process of the TIN, it is ensured that each generated triangle has excellent morphological characteristics (satisfying the "empty circumscribed circle" or "minimum angle maximization" condition) and topological correctness, and finally a high-quality irregular triangular network is constructed. Meanwhile, a "skip" mechanism of edges and points is provided, invalid traversal is avoided in the triangular network construction process, and the time efficiency of the algorithm is maximally improved.