85 results about "Adaptive topology" patented technology

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 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 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 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 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 a hoisting equipment path planning method suitable for a limited space of a hydropower station, and belongs to the technical field of path planning. Key geometric parameters of static obstacles in the hydropower station are acquired, and a three-dimensional model of the hydropower station is constructed; calculating the obstacle probability of the dynamic obstacle at any position based on the historical motion trail of the dynamic obstacle; fusing the three-dimensional model of the hydropower station with the obstacle probability, and constructing a self-adaptive spatial topological graph of the hydropower station; defining a physical boundary and a motion constraint of the hoisting equipment so as to carry out multi-modal positioning data acquisition on the equipment; generating a global path based on the self-adaptive topological graph, predicting a motion track of the equipment within target time according to the positioning information of the equipment, and generating an equipment candidate track; and performing collision risk assessment on the equipment candidate trajectory, selecting a local path through a dynamic weight comprehensive cost function, and generating an equipment planning path. The device can adapt to a light reflecting and shielding environment in a limited space, and is high in working efficiency and safety.

A method for common signal transmission in a system comprising plurality of base stations (200) where base stations (200) are capable of transmitting common signal and information signal, plurality of user equipment (300) which are capable of communicate communicating with the base stations (200) and a network controller (100) for controlling the operation of base stations (200) characterized in that comprising the steps of: receiving, by user equipment, information signals comprising instructions on how and when to trigger the base stations; selecting, by the network controller (100), operation mode of base stations (200) among an inactive mode where the base station (200) hold operation of transmitting common signals until a user equipment (300) or the network controller (100) triggers common signaling; an active mode where common signaling is realized and where base stations' (200) transmitting and receiving capabilities are adjusted for covering the coverage area of the base stations (200) that operate in inactive mode; monitoring, by the network controller (100), network conditions; performing selection of operation mode based on monitoring results.

An embodiment of the present invention discloses a traffic state prediction method based on multimodal data and adaptive topology modeling. The method includes: dynamically determining the adjacency matrix between roads based on the type and historical traffic flow of each road, and inputting the adjacency matrix into a GCN model and a GAT model respectively based on the dynamic traffic network diagram; the GCN model extracts global spatial features based on the weight of each edge and the current feature representation of each node; the GAT model strengthens the role of key nodes to obtain local attention spatial features; short-term time series modeling and long-term time series modeling are performed on the time series of multimodal traffic data of each node in the most recent period, respectively, to obtain short-term features and long-term features; and predicting future traffic states based on the global spatial features, local attention spatial features, short-term features, and long-term features. This embodiment improves the accuracy of traffic state prediction.

The present application relates to big data processing and intelligent analysis technical field, specifically to a kind of electronic pricing scale big data acquisition processing and intelligent analysis method based on NFC and AI;Its core is: through pre-training evaluation model to original transaction data stream is inferred, according to the preset value and risk threshold value division processing level;Based on the level, in combination with topological strategy library and system load, according to utility and cost weight selection optimal processing topology and execute;Loss function is constructed by collecting processing delay, resource consumption and analysis efficiency, and threshold value and weight parameter are reversely dynamically updated;The present application realizes the change from rigid processing to dynamic stratification and adaptive topology optimization based on data instantaneous value and risk.

The invention provides a network control method, device, equipment, medium and product, which are applied to the technical field of wireless ad hoc networks, and the method comprises the following steps: determining a current mobile node and a leadership index of a neighbor node of the current mobile node by obtaining a neighbor table and based on the neighbor table; carrying out leader node election according to the leader indexes of the current mobile node and the neighbor nodes of the current mobile node so as to determine the identity of the current mobile node; and under the condition that the current mobile node is the leader node, sending a guiding instruction to a following node in the current subnet corresponding to the leader node so as to guide the following node in the current subnet. According to the method, the leadership index is calculated according to the neighbor table, the leader node election is performed, and the guide instruction is sent to the following node in the subnet through the leader node, so that the distributed adaptive topology management of the wireless ad hoc network in the high dynamic environment is realized, and the risk of single node failure is avoided.

The application discloses a pipe network prediction and intelligent decision system based on physical spectrum sensing and dynamic scanning, which deeply mines the correlation characteristics among multi-source data through dynamic physical spectrum decomposition and frequency domain adaptive topology evolution technology, effectively overcomes the problems of sensor noise interference and data deviation, simultaneously introduces a HydroScan-SSM model, effectively captures the long-term dependence relationship in the time sequence, accurately captures the coupling evolution law of the water quality parameters and the pipe network structure, finally, in view of the pipe network intercepting valve control scene, combines the pipe network data characteristics and intelligent decision demand, uses an intelligent decision framework based on dynamic data driving, realizes the dynamic optimization of the intercepting valve control strategy through real-time data fusion and machine learning algorithm, and effectively avoids the problems of misdischarge, leakage and untimely discharge.

The application discloses a function configuration method and electronic equipment, relates to the technical field of servers, and comprises the following steps: recording the topological association between the device and the root port thereof before restarting, and generating a bitmap persistence; and repositioning the device according to the stable and invariable bus number after restarting, and realizing the VMD function, so that the technical problem that the conventional hardware firmware scheme cannot correctly and accurately realize the one-key VMD function is solved, the one-key VMD function is realized without manual intervention and self-adaptation to topological changes, and the technical effects of resource waste and potential conflicts caused by global opening are avoided.

This invention relates to artificial intelligence and big data processing technologies, aiming to provide a method for constructing multimodal intelligent knowledge cards and self-organizing links by topic. The method includes: collecting data from multiple sources, preprocessing it to extract multimodal information, and constructing several standard structured data objects as knowledge cards; encapsulating the data in a unified format to build a knowledge card database; constructing multidimensional hybrid embedding vectors based on the attributes of the data in the knowledge card database using different knowledge cards; establishing a multimodal semantic adaptive topology network under spatiotemporal constraints to achieve self-organizing links by topic; storing the knowledge cards as nodes in a backend knowledge graph and establishing connections with the frontend display / interactive interface. This invention can be used for intelligent processing of data extraction, structured encapsulation, and dynamic association. Through vector space alignment, it can discover implicit knowledge and potential connections, achieving dynamic evolution and adaptability.

The invention relates to a self-adaptive topology real-time updating method and a self-adaptive topology real-time updating system for dealing with a fault sequence of opening and closing signals of a spare power automatic switching device of a power distribution station house. The method comprises the following steps: S1, switch marking: accurately marking switches related to spare power automatic switching in a power distribution master station system; s2, performing switching identification: maintaining the switching state of the spare power automatic switching device in the power distribution master station system; step S3, signal identification: when the power distribution main station receives the switch signal, automatically judging whether the switch is a spare power automatic switching switch, and identifying the current switching state of a spare power automatic switching device; and S4, topology updating: based on a judgment result of the step S3, a differentiation strategy is adopted to update the distribution line topology. According to the method, the problem of wrong sequence of the spare power automatic switching-on and switching-off signals can be intelligently solved, the real-time and accurate updating of the line topology at the power distribution main station side is realized, and the reliable and correct action of a centralized feeder automation system is ensured.

The invention provides a dynamic trapping network deployment method and system based on a Markov decision process, and relates to the technical field of network security. The method provided by the invention comprises the following steps: extracting TTP features from multi-source attack behavior data, mapping the TTP features into TTP feature vectors corresponding to attack steps, and constructing a TTP feature vector sequence according to an attack sequence; based on a Markov decision process model combined with multi-source threat intelligence, performing threat degree quantitative evaluation and sorting on the TTP feature vector sequence to obtain a sorted TTP feature vector sequence, and mapping the TTP feature vector sequence to a corresponding CVE number; based on the CVE serial number, selecting a honey point mirror image containing the corresponding vulnerability from a honey point warehouse; and based on the selected honey spot mirror image and the network defense demand, dynamically adjusting a honey spot deployment strategy and network topology configuration, and constructing a dynamic trapping network. According to the method, adaptive topological optimization from threat assessment to honey spot deployment is realized through the Markov decision process model.

The present invention discloses an adaptive subdivision grid topology optimization method and system based on a balanced quadtree. The steps of the method include establishing a sparse grid model and defining loads and boundaries; establishing a data structure for storing unit and node information; initializing design variables, calculating the objective function and constraint function, and the sensitivity of the function to the design variables; calculating the subdivision factor of each unit and determining whether it is greater than a threshold; for grids greater than the subdivision factor, using a recursive method to decompose the grid units and update the unit and node data structures; establishing a stiffness matrix, a constraint matrix and a calculation matrix for the updated model, and updating the design variables; repeating the above steps until convergence. Based on the above method, the corresponding modules are constructed and a system is formed. The method of the present invention ensures high computational efficiency while achieving extremely high boundary accuracy, greatly broadening the scope of application of the adaptive topology optimization method.

The invention relates to the technical field of geometric modeling, in particular to a geometric modeling method of a cable assembly parameterized model. The method comprises the following steps: deploying a multi-source sensor array to collect cable space trajectory data, constructing a feature matrix, and generating a cable joint feature matrix; key parameter extraction is carried out on the cable joint feature matrix, and node feature vectors are constructed; performing adaptive graph construction by using the node feature vectors, and constructing a topological structure to obtain cable topological coding data; therefore, by introducing multi-source heterogeneous data fusion, curvature-torsion sensitivity analysis, adaptive topology construction and a time sequence deformation mapping mechanism based on LSTM, the problems of incomplete multi-scale deformation response identification and insufficient physical structure expression ability in the traditional cable geometric modeling process are solved; and the accurate modeling and dynamic regulation and control capabilities of the cable geometric model in a complex environment are improved.

The invention discloses a ceramic membrane internal defect detection method based on parallel stripe convolution and topological convolution. The method comprises the following steps: 1) constructing a ceramic membrane plate internal defect data set; 2) in a feature extraction module, providing a parallel multi-branch strip convolution feature extraction module, and better extracting features while realizing light weight; in a detection head module, a self-adaptive topological convolution detection head is provided, and the module reduces calculation of redundant features; and 3) model training and optimization: carrying out multiple rounds of training on the self-built data set. According to the method, for the problem of YOLO model lightweight, parallel strip convolution is used in a feature extraction part, and features can be better extracted on the premise of reducing the network parameter quantity; in addition, adaptive topological convolution is used in a detection head part, so that redundant calculation of a large-area non-key background area can be effectively reduced.

The application discloses a hoisting equipment path planning method suitable for a limited space of a hydropower station and belongs to the technical field of path planning. Key geometric parameters of static obstacles in the hydropower station are acquired, and a three-dimensional model of the hydropower station is constructed. Based on historical motion trajectories of dynamic obstacles, obstacle probabilities of the dynamic obstacles at any positions are calculated. The three-dimensional model of the hydropower station and the obstacle probabilities are fused to construct an adaptive space topology graph of the hydropower station. Physical boundaries and motion constraints of hoisting equipment are defined to collect multi-modal positioning data of the equipment. A global path is generated based on the adaptive topology graph, the motion trajectory of the equipment in a target time is predicted according to positioning information of the equipment, and a candidate trajectory of the equipment is generated. Collision risk assessment is performed on the candidate trajectory of the equipment, a local path is selected through a dynamic weight comprehensive cost function, and a planned path of the equipment is generated. The application can adapt to a reflection and shielding environment in a limited space, and has high operation efficiency and safety.