103 results about "Adaptive topology" patented technology

The embodiment of the invention provides an environment monitoring method and system based on 5G. According to the method, a dynamic coupling sensing field is constructed in a 5G network coverage domain, an adaptive topology with nonlinear interaction of parameters is established, and when parameter abnormity is detected, differential geometric reconstruction of a 5G network slice control surface is triggered based on Riemannian curvature change. And constructing a space-time manifold space at the 5G edge computing node, decomposing and extracting a diffusion principal curvature direction, and iteratively solving a propagation rate of an abnormal parameter in combination with a geodesic equation to generate a diffusion leading edge envelope surface. And according to the main curvature distribution characteristics of the diffusion leading edge envelope surface, constructing a dynamic sampling optimizer, generating an exponential decay sampling function, and deploying a directional sensing wavefront. And carrying out conformal tightening processing on the sampling function and the geographic grid to generate an interactive holographic manifold, establishing an aging judgment criterion of an abnormal level based on curvature 2-form characteristics, and triggering a graded early warning pulse wave packet. According to the technical scheme provided by the embodiment of the invention, the real-time performance and precision of environment monitoring are improved.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention provides a novel power system multi-scale network construction energy storage planning optimization method based on source network load clustering. The method comprises the following steps: firstly, processing source network load data by adopting a multi-dimensional adaptive feature extraction and fusion method, and realizing accurate processing of the data through interval adaptive probability density estimation, adaptive kernel tensor decomposition and marginal information entropy maximization; then, a multi-level data clustering method is adopted, and accurate clustering is achieved through hierarchical distance measurement and boundary self-adaptive adjustment; then, a multi-scale energy storage modeling method is adopted, and accurate modeling of the energy storage system is achieved through the dynamic characteristic matrix and self-adaptive topology embedding; and finally, a multi-objective optimization solution method is adopted, and efficient solution of an optimization problem is realized through adaptive hierarchical decomposition and hybrid evolutionary optimization. According to the invention, the accuracy and reliability of energy storage planning can be obviously improved, and the system operation efficiency is improved.

The invention provides an emergency command converged communication system resource dynamic scheduling method based on artificial intelligence. The method comprises the following steps: S1, acquiring a plurality of sensors and log data of an emergency command system; constructing a dynamic graph neural network optimization communication topology, and selecting an optimal communication path; combining resource prediction and the optimized communication topology to formulate a global optimal resource scheduling strategy, and generating an optimal resource allocation scheme for each task; the resource pool comprises available computing resources and communication bandwidth; dynamically adjusting resource allocation and a communication path in a task execution process, and generating task execution feedback of a corresponding task; the system state is monitored in real time, and resource prediction, topology adjustment and scheduling strategies are optimized based on execution error feedback of task execution feedback. According to the method, efficient and intelligent dynamic resource scheduling is realized in an emergency command converged communication system through resource prediction of self-supervised learning, self-adaptive topological optimization of a variable topological graph neural network and global scheduling of reinforcement learning driving.

The invention relates to the technical field of road condition intelligent perception and application, in particular to a road condition adaptive topology perception and application system and method. A condition cluster perception module monitors road condition parameters in real time; the data transmission communication module realizes differential data transmission strategies; a road section dynamic risk grade evaluation module calculates a road section risk index and grade according to the road structural state parameters and a risk evaluation model; the node adjustment and resource allocation module adjusts the activation state and sampling frequency of each sensor according to the road section risk level, simulates and verifies the implementation effect of a resource allocation scheme under different road section risk levels by using a constructed road system digital twin model, and optimizes the resource allocation scheme by using a multi-objective optimization algorithm; the application service module converts the risk level into a specific service action; the cooperative operation of the modules realizes the flexible allocation of road condition perception, communication and computing resources, and improves the utilization efficiency of the resources and the service efficiency of the system.

The invention relates to the technical field of agricultural wireless sensor networks, and discloses an agricultural wireless sensor network topology reconstruction method based on an energy consumption balance model. The method comprises the following steps: acquiring multi-dimensional energy consumption data of each node, including node residual energy, communication load intensity and an environment interference coefficient; extracting a node energy health degree based on residual energy, generating a communication priority sequence according to communication load intensity, and analyzing an environment interference coefficient to generate an interference distribution thermodynamic diagram; inputting the result into an energy consumption balance evaluation model to generate a topology reconstruction strategy; and a self-adaptive topology adjustment map is constructed through a distributed optimization algorithm, and a node connection relation optimization scheme is output. The system is correspondingly provided with a multi-dimensional data acquisition module, an energy consumption state analysis module and the like. The problems of unbalanced energy consumption, non-uniform communication load, environmental interference and the like of the agricultural wireless sensor network can be effectively solved, the network performance is improved, the service life of the network is prolonged, and the development of precision agriculture is promoted.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

The invention discloses a security monitoring abnormal behavior image recognition method based on time sequence convolution, and the method comprises the following steps: S1, obtaining monitoring video data, and constructing a key point time sequence; s2, modeling the key point time sequence by using a manifold embedded Sheng differential equation, and performing smooth change in a manifold space by using an exponential mapping method; s3, based on a self-adaptive step size solving strategy, dynamically adjusting the time step size according to the change rate of the optimized key point time trajectory; s4, constructing an adaptive topology time sequence convolutional network, and generating enhanced key point time sequence features; s5, using local topology regularization to constrain the amplitude of the topological change of the key points, and extracting the feature representation of the human behavior; s6, carrying out abnormal behavior classification by adopting a classification network; and S7, setting an alarm threshold value, and providing early warning information through the monitoring platform. According to the method, the manifold embedding Shenchang differential equation and the adaptive topology time sequence convolution technology are utilized to accurately identify abnormal behaviors.

The invention provides a substation secondary system communication network adaptive topology reconstruction method, which comprises the following steps: modeling by adopting multi-scale time delay feature fusion, and accurately predicting protocol conversion time delay; designing a hierarchical fusion protocol conversion time delay control framework, wherein the framework comprises two-stage time delay control of feed-forward compensation and feedback adjustment based on a resource-time delay sensitivity matrix; proposing a protocol semantic dynamic cutting technology, and realizing time delay demand-based differential semantic processing through a performance-semantic tradeoff function; and constructing a topology reconstruction decision-making mechanism of end-to-end time delay perception, and comprehensively evaluating the influence of the candidate topology scheme on the key service flow time delay. According to the method, the protocol conversion time delay of key services in the multi-protocol environment of the transformer substation is reduced, and millisecond-level time delay guarantee and self-adaptive optimization of network topology are realized.

The embodiment of the invention discloses a traffic state prediction method based on multi-modal data and adaptive topology modeling. The method comprises the following steps: dynamically determining an adjacent matrix between roads according to the type and historical traffic flow of each road, and respectively inputting a GCN model and a GAT model according to an adjacent matrix dynamic traffic network diagram; extracting global spatial features by the GCN model according to the weight of each edge and the current feature representation of each node; strengthening the effect of a key node by the GAT model to obtain a local attention space feature; performing short-time timing sequence modeling and long-time timing sequence modeling on the time sequence of the multi-modal traffic data of each node in the latest period of time to obtain a short-time feature and a long-time feature respectively; and predicting a future traffic state according to the global spatial features, the local attention spatial features, the short-time features and the long-time features. According to the embodiment, the traffic state prediction accuracy is improved.

The invention discloses a power distribution network feeder line self-healing online dynamic simulation test method and device and a computer program product, and the method comprises the steps: generating a self-adaptive topology recognition and fault self-healing scheme based on the on-site test characteristics of a power distribution network feeder line self-healing protection function; constructing a power distribution network feeder line self-healing model by combining the self-adaptive topology identification and fault self-healing scheme with the goal of meeting the requirements of recovering load power, isolating load and path loss; key parameters of a northern eagle optimization NGO algorithm are optimized by adopting chaos initialization and an elite individual learning strategy; and analyzing the self-healing model of the feeder line of the power distribution network by using an improved NGO algorithm, simulating the operation process of the whole feeder line group under different fault conditions on a dynamic model test platform, and outputting an optimized self-healing processing strategy. According to the invention, the stability, reliability and operation efficiency of the power distribution network are significantly improved, and an efficient and intelligent self-healing control solution is provided for a complex and changeable power distribution network environment.

The invention discloses an ultra-low power consumption pyroelectric infrared human body detector and a networking control method thereof. The detector comprises a detection module, a control module, a power supply module, a communication module, an interaction module and a dynamic power consumption management module, human body infrared radiation is detected based on the pyroelectric effect, and ultra-low power consumption operation is achieved by dynamically switching working modes. The networking control method is based on a BLE Mesh protocol, a self-adaptive topology management algorithm is used for optimizing routing, and efficient networking with intelligent equipment is achieved. The system solves the problems of high power consumption, large networking delay, high false alarm rate and the like of a traditional detector, and is suitable for the fields of smart home, intelligent security and protection and the like.

The invention discloses a low-voltage equipment management method and system for a power distribution network, and the method comprises the steps: S1, collecting the operation parameters of all low-voltage equipment in real time through an Internet of Things terminal node disposed at the low-voltage side of the power distribution network; s2, on the basis of the real-time data in the S1, combining dynamic change characteristics of the power distribution network topology, constructing an adaptive topology network model through edge computing nodes, and dynamically updating a connection relation and a load path between equipment by the model by adopting a graph theory algorithm; s3, inputting the real-time data in the S1 into a preset fault diagnosis model, identifying abnormal equipment and positioning a fault type by the model through fusion of a deep belief network and a random forest algorithm, and predicting a health degree attenuation trend of the equipment at the same time; and S4, according to the topology model in the S2 and the fault diagnosis result in the S3, generating a resource optimization allocation strategy based on a dynamic programming algorithm, including adjusting the switching priority of standby equipment, reconstructing a load allocation path, and sending a control instruction to a specified terminal through a communication module.

The invention relates to an activation value re-segmentation communication method for a heterogeneous AI acceleration chip server. The activation value re-segmentation communication method comprises the following steps: scanning through chips, network interface cards and PCIe levels in the AI acceleration chip server, extracting network topology information of the server, calculating delay and bandwidth from each chip to each network interface card, and generating an affinity matrix; based on the affinity matrix, each chip is bound to a network interface card in an exclusive or shared mode through maximum matching; based on the server network topology information, the chip-network interface card affinity and the network interface card and PCIe single line bandwidth, activation value communication mode matching is carried out, and communication modes comprise a transmitting / receiving mode, a transmitting / receiving + collecting mode and a transmitting / receiving + broadcasting mode; and double-level multi-channel communication between the chip and the network interface card is realized. The method has the advantages of remarkably improving the bandwidth utilization rate, being highly adaptive to topology difference, being high in heterogeneous compatibility and the like.

The invention provides an aviation positioning method based on inertial navigation, and relates to the technical field of satellite navigation, acceleration data and angular velocity data of an aircraft are collected in real time through an inertial navigation system, and a virtual inertial field is constructed based on the acceleration data and the angular velocity data to represent the dynamic motion trend of the aircraft. Predicting the position and the speed of the aircraft according to the virtual inertial field, comparing the position and the speed with the position and the speed measured by the inertial navigation system, generating error distribution, analyzing a topological structure of the error distribution by using an adaptive topological filter, adaptively adjusting filtering parameters, and correcting accumulated errors of the inertial navigation system, so as to obtain the position and the speed of the aircraft. A virtual inertial field prediction result and a self-adaptive topological filter correction result are fused, real-time position and speed estimation of the aircraft is output, the method can dynamically adapt to a complex flight state, the ATF surpasses limitation of a traditional filter on linear Gaussian error hypothesis through topological data analysis, and the intelligence and accuracy of error correction are remarkably improved.

The invention relates to the technical field of power electronic conversion, and discloses a high-efficiency rectification conversion system and a control method thereof, and the system comprises the following modules: a detection module, an input compatible module, a hybrid power device module, a control module, a thermal management module and a frequency modulation module. The control method comprises the following steps: acquiring input power grid voltage, output current and load state parameters; calculating an input voltage phase difference; dynamically selecting the type and channel configuration of the silicon carbide or gallium nitride power device according to the load rate; outputting a device configuration instruction and a switching frequency control instruction; adjusting the cooling system through the evaluated thermal state of the conversion system; and the control module receives the control instruction output by the control module and is used for implementing a corresponding pulse width modulation strategy. According to the method, a multi-system power grid identification and self-adaptive topology switching strategy is adopted, the technical effect of automatically configuring the optimal rectification architecture under different input types is achieved, and the problems of response delay and poor compatibility of the method in the environment of variable power grid types are solved.

The invention relates to a quality prediction method based on spatio-temporal dynamic graph convolution and mutual attention selection, and the method comprises the steps: collecting historical observation values of a target variable and a process variable in a chemical process, combining the historical observation values into a one-dimensional vector, carrying out the dimension rising processing of the time dimension of the one-dimensional vector, and carrying out the feature embedding and grouping processing, thereby obtaining a plurality of grouping features; respectively carrying out convolution operation on each grouping feature to obtain a corresponding time feature representation; obtaining a first time-space representation corresponding to the time feature representation based on the adaptive topological structure and a gating mechanism; aggregating each time feature representation through the attention weight to obtain an aggregated feature; carrying out average pooling and convolution operation on the aggregated features in the time dimension and the node feature dimension to obtain global features, time local features and node local features, and then obtaining second space-time representation; and performing residual connection on the second spatio-temporal representation and each first spatio-temporal representation, and outputting a predicted value of the target variable through a full connection layer.

The invention discloses a power distribution network fault diagnosis method and system based on a dynamic graph attention network and cross-modal fusion, and belongs to the technical field of power system operation analysis and calculation. The method comprises the following steps: S1, data acquisition and preprocessing: acquiring multi-modal data of a power distribution network; s2, performing dynamic topology modeling, analyzing the switch state signal, and generating a dynamic adjacency matrix in real time so as to construct a dynamically changing power distribution network topological graph; s3, cross-modal feature alignment: performing feature extraction and distribution alignment on the electrical quantity, the environmental quantity and the equipment state quantity to generate fusion features; s4, blind area data completion is carried out, and virtual monitoring data are generated for monitoring blind area nodes by using a physical constraint generative adversarial network; and S5, fault diagnosis and output: inputting the fusion features and the virtual monitoring data into the dynamic graph attention network together, and outputting a fault diagnosis result. According to the method and the system provided by the invention, self-adaption to topological change can be realized, multi-source information is effectively fused, and the physical authenticity of data is guaranteed.

The invention provides a government affair work order generation alarm method and system based on domain fit topology awareness, and relates to the technical field of government affair work order early warning, and the method comprises the steps: obtaining real-time alarm data, carrying out key structured field extraction, introducing a cue word optimization algorithm, optimizing the key structured field, and generating an optimized cue word; constructing an operation and maintenance knowledge graph, extracting an event sequence, calculating and obtaining a hidden dependency or potential fault propagation path which is not described by a document among the components based on the event sequence, and generating an optimized operation and maintenance knowledge graph; generating a structured alarm feature sequence with weight based on the optimized operation and maintenance knowledge graph; and inputting the weighted structured alarm feature sequence into a work order generation model, converting the feature sequence into an attention bias matrix, gradually generating a fault abstract, performing optimization based on a topology perception optimization algorithm to obtain a final fault abstract, generating a processing suggestion based on the final abstract, and creating a work order. According to the invention, the work order automatic generation efficiency can be improved.

The invention discloses a power electronic distributed cooperative control method and system for an intelligent micro-grid. The method comprises the following steps: constructing a distributed communication architecture based on adaptive topology; based on a distributed communication architecture, executing a fusion cooperative control algorithm: dynamically coordinating a power distribution proportion based on a distributed average consistency algorithm, and introducing a dynamic weight mechanism; in combination with an LSTM prediction model, historical and real-time data are utilized to predict the output fluctuation and load change trend of the distributed power supply, and a control strategy is optimized in advance; setting an intelligent abnormal response mechanism; and based on the intelligent abnormal response mechanism, carrying out dynamic scheduling by adopting a flexible power regulation method. In the aspect of flexible power adjustment, fuzzy logic control is organically combined with maximum power point tracking and PWM pulse width modulation, and dynamic scheduling is performed according to the real-time operation state of the micro-grid, so that accurate and efficient control of the output power of the inverter is realized, and the overall performance and stability of the micro-grid are greatly improved.

The invention discloses a privacy perception graph prompt learning method based on adaptive topology recovery, which belongs to the technical field of computer network security and artificial intelligence crossing, and adopts an adaptive structure Bayesian estimation method to reconstruct privacy graph data into graph data closer to an original graph sample, and extracts an internal privacy mode from node embedding, so as to improve the privacy perception graph prompt learning efficiency. A learnable prompt of a noise mode introduced based on dynamic perception and responding to privacy disturbance is constructed, progressive privacy consistency is introduced in the optimization process, semantic alignment between multiple noise views is promoted, two loss functions are jointly optimized, multiple rounds of iterative training are performed, and the optimization efficiency is improved. According to the method, the final generalization ability of the graph prompt learning model and the balance of task adaptation can be realized, and a load balance topology is generated based on an adaptive disturbance estimation topology recovery technology so as to establish disturbance distribution; a noise suppressed reliable cue and multi-view cue optimization is designed to mitigate the effects of residual perturbations, generating robust and semantically aligned cues.

The invention relates to the technical field of three-dimensional measurement and digital twinborn assembly analysis, in particular to a part deformation analysis system based on three-dimensional measurement data driving, which comprises an anisotropic feature decoupling unit for acquiring sparse point cloud data of a flexible target object and generating a purified discrete feature set; the geometric illusion monitoring unit is used for generating an illusion entropy distribution diagram by using an implicit neural representation technology; the adaptive topology fidelity arbitration unit is used for generating a final deformation analysis report based on the illusion entropy distribution diagram; the deformation logic iteration unit is used for dynamically adjusting weight parameters of a resistance deformation decoupling model and a calculation strategy of the geometric illusion entropy according to a final deformation analysis report and downstream assembly feedback; according to the invention, the purity of input data and zero missing detection of deformation are ensured.

The invention discloses an SVG vector diagram-based primitive matching and intelligent recognition system and method, and relates to the technical field of computer graphic processing and mode recognition. The system stores a PDF file through SHA-256 Hash duplicate removal, format conversion is achieved through a tool, an 800DPI PNG image is generated, and the resolution is verified; the affine transformation matrix is analyzed, coordinate normalization and graphic element classification conversion are executed, and complex paths are split, deduplicated and merged; detecting a character area, and deleting corresponding character elements in the SVG; geometric features are extracted to construct a multi-dimensional index, and a template topological structure is analyzed; and selecting anchor points according to priorities, adapting to multiple rotation directions, matching non-anchor point elements through dynamic tolerance, verifying connection points in combination with adaptive topology tolerance, and finally outputting a matching result containing confidence. The system realizes efficient identification and accurate matching of technical drawings through standardization processing and an intelligent matching algorithm, and is suitable for scenes such as industrial drawing analysis and document digitization.

The invention relates to the technical field of graph generation, in particular to a self-adaptive topological plant station wiring diagram generation method, which comprises the following steps of: acquiring a wiring diagram image, extracting edge features, identifying an image type, positioning a line structure boundary, identifying text elements, classifying corresponding graph areas, and constructing a connection path to generate a topological wiring diagram. According to the method, clear recognition of the structure contour is realized through extraction of the edge features of the line structure of the image, the spatial position information of the graphic area is acquired in combination with a multi-scale primitive positioning technology, and then identity extraction and classification judgment are performed on the text elements contained in the image in a character recognition mode. And establishing a corresponding relationship between the equipment identifier and the graph region, constructing a complete path by combining the graph arrangement direction and the starting and ending coordinates of the connecting line, and determining a connection sequence and a relative position relationship between the equipment. Coordination and unification of spatial arrangement are realized through application of an attraction and repulsive force model between nodes, and clearness and coherence of a wiring diagram in the aspect of topological structure expression are improved.

The invention provides a data processing method which can be applied to the technical field of artificial intelligence. The data processing method comprises the following steps: acquiring original training data, wherein the types of the original training data comprise a numeric type, a text type and an image type; constructing a topological space corresponding to the original training data by using an adaptive topology measurement algorithm; recognizing topological features in the topological space by using a dynamic threshold recognition algorithm, wherein the topological features comprise holes and rings; and based on the data type and the topological features of the original training data, repairing the topological features to obtain repaired data. The invention further provides a data processing device and equipment, a storage medium and a program product.

The invention relates to the field of power grid power transmission, in particular to a power grid multi-topology section power transmission capacity rapid evaluation method and a medium, and the method comprises the steps: obtaining power grid operation data containing an operation data matrix and an adjacent matrix; judging whether the power grid topology is changed or not, and if so, updating a power transmission distribution coefficient matrix reflecting the influence of node injection power on section power flow; and inputting the correlation matrix into an off-line trained evaluation model constructed based on an improved graph attention network, and outputting a maximum power transmission capability predicted value. According to the method, through a closed-loop process, evaluation needing repeated iteration or retraining is converted into off-line training and then is taken and used, the bottleneck that topological change needs to be modeled again is eliminated, real-time evaluation and rapid decision making are achieved, and the problems that an evaluation model adapts to topological change and outputs a high-precision result at a millisecond level are solved.

The invention discloses an action sports scoring method and device based on big data analysis, and relates to the technical field of computer vision, and the method comprises the following steps: S1, constructing a human body posture tensor manifold; s2, generating aligned action track characteristics; s3, generating symmetrical positive definite manifold features; s4, generating deep manifold distribution parameters; s5, quantifying global distribution deviation characteristics; s6, calculating a residual vector based on the deep manifold distribution parameters and the standard action distribution parameters, inputting the residual vector into the improved AGCN model for processing, constructing an adaptive topology based on the residual vector, and performing aggregation analysis to obtain a joint-level physical angle error after multi-scale space-time convolution and manifold enhancement; and S7, outputting a comprehensive score vector. According to the method, the limitation that a traditional method only depends on local geometric features and ignores global topological structure constraints and statistical distribution priori is overcome, and an efficient solution is provided for intelligent scoring of sports actions.

The invention relates to a traffic flow self-adaptive prediction method based on a multi-time step diagram, and belongs to the technical field of intelligent traffic and data mining analysis. The traffic flow self-adaptive prediction method based on the multi-time step diagram obtains a self-adaptive topological graph network by fusing a distance diagram, a time-varying edge weight topological graph and the multi-time step diagram; and by introducing an attention conversion mechanism, utilizing a reinforcement learning control module and designing a prediction technology combining the attention mechanism and an adaptive topological graph network, comprehensive capture of space-time dynamic characteristics, effective alleviation of prediction errors and adaptive determination of an optimal prediction moment are realized, so that the prediction accuracy is ensured, and the prediction efficiency is improved. And the timeliness and the reliability of prediction are obviously improved.

The invention relates to the technical field of geometry chamfering, and discloses a three-dimensional geometry adaptive topological contour gradient structure generation method comprising the following steps: S1, input analysis and contour extraction; s2, calculating the maximum safety chamfering radius; s3, performing triangulation processing on the contour; s4, a chamfering path is constructed; s5, connecting the surfaces of the chamfers; s6, generating a bottom surface / top surface; and S7, data buffering and attribute binding. Smooth transition is achieved through self-adaptive processing of concave-angle arc connection and convex-angle linear connection, and sawteeth are avoided; the chamfering radius is restrained through the maximum inscribed circle radius and the depth range, and safe chamfering of the complex contour is ensured; the memory occupation is reduced and the rendering efficiency is improved through sharing vertex data and staggered storage optimization; the special chamfer for the path is supported, and the requirement for complex shapes is met.

The invention discloses an active power distribution network voltage control method and an active power distribution network voltage control device adaptive to topological change, which are used for solving the problem that a voltage control strategy cannot be output due to ineffective modeling caused by incapability of well adapting to topological change in the prior art. The active power distribution network comprises a first voltage level power distribution network and a plurality of second voltage level power distribution networks; constructing a voltage control model of the active power distribution network according to a coupling relationship between the first voltage level power distribution network and the plurality of second voltage level power distribution networks; carrying out optimization solution on the voltage control model, and taking a solved boundary variable optimal solution as a voltage control strategy when power grid dispatching is carried out on the first voltage level power distribution network and the plurality of second voltage level power distribution networks; and in the optimization solution process, performing power flow model updating based on the element matrix for the target second voltage level power distribution network with topological change to obtain a new linear power flow model, and performing power flow calculation of the target second voltage level power distribution network based on the new linear power flow model.