1408 results about "Path generation" patented technology

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention provides an unmanned aerial vehicle flight path planning method based on air-space linkage, and the method comprises the steps: carrying out the multi-time-domain air-space data fusion, double-layer asynchronous iterative planning, multi-constraint joint optimization, distributed computing resource scheduling, task perception path generation, and other innovative technologies; the problems of limited environment perception capability, high calculation complexity, low resource allocation efficiency, insufficient real-time track adjustment capability and the like in the prior art are solved, and the flight performance and task execution efficiency of the unmanned aerial vehicle in a complex environment are improved.

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention relates to a cutting path generation system based on visual positioning, cutting equipment and a cutting method. The system comprises a scanning camera control module, an image acquisition and recognition module, an image processing module, a cutting path planning module and a cutting control module. According to the system, by combining a Laplace algorithm and an LBP and GLCM texture extraction technology, the boundary recognition problem under the condition that a pattern and a background are in the same color or low contrast is solved; the path smoothness and the cutting quality are improved by adopting contour optimization and curvature compensation algorithms, the cutting sequence is optimized by combining weighting functions of the area, complexity and overlapping degree, and material deformation is reduced. And motor driving is dynamically adjusted through PID control, and stable control over the speed of the tool bit is achieved. The method has the advantages of being high in recognition precision, high in adaptability, intelligent in path planning and stable in cutting process, the automatic cutting efficiency and the finished product quality of complex patterns or same-color materials are remarkably improved, and the overall cutting precision fluctuation is controlled within + / -2%.

The invention discloses a mechanical arm path planning method based on Transform and a diffusion model, and belongs to the technical field of robots, and the method comprises the following steps: collecting environment information to generate a reference path, and constructing a training data set; a conditional diffusion Transform prediction network is constructed, features are extracted, and multi-modal path prediction is realized; gaussian noise is applied to the reference path, and denoising training is carried out on the conditional diffusion Transform prediction network; in combination with a diffusion model and a cost guidance mechanism, optimizing a noise path sampled from Gaussian distribution until a smooth collision-free path is generated; the candidate paths are evaluated, and a mechanical arm joint control instruction is generated; and the mechanical arm executes the planning track and carries out real-time sensing and online re-planning. According to the method, environment perception, Transform coding and diffusion generation are organically combined, a plurality of feasible tracks which are smooth and capable of avoiding obstacles are rapidly generated in a complex obstacle scene, and the method has good generalization ability and can adapt to different scenes and dimension changes.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

The invention relates to the technical field of structured retrieval, and discloses a retrieval enhancement generation method and system based on LLM structured index and vector hybrid retrieval, and the retrieval enhancement generation method based on LLM structured index and vector hybrid retrieval comprises the steps of processing and analyzing a non-structural document, generating a hierarchical tree data extraction with document logic, and generating a hierarchical tree data extraction with document logic. The method comprises the following steps: obtaining a query demand, analyzing and identifying the query demand, self-matching any one or a combined retrieval strategy to quickly and accurately obtain a retrieval result in the complete logic positioning information, optimizing hierarchical tree data by adopting context perception, and establishing the complete logic positioning information in the hierarchical tree data through a reasoning path generation method. According to the method, through structured indexing, accurate positioning of specific chapters of the document, improvement of retrieval accuracy and support of multi-step logical reasoning, the long tail problem which cannot be processed by traditional RAG is solved, a clear and transparent retrieval path can be provided, and the system credibility is enhanced.

The invention relates to the technical field of artificial intelligence and unmanned aerial vehicle path planning, in particular to a multi-rotor unmanned aerial vehicle adaptive obstacle avoidance strategy generation system and method, and the system comprises a multi-source environment sensing module which is used for collecting environment data; the dynamic obstacle modeling module is used for constructing a dynamic obstacle motion state model; the threat degree evaluation and analysis module is used for constructing a threat degree evaluation model and then outputting a threat evaluation result; the path generation optimization module is used for inputting the threat evaluation result into a path generation network model and generating N groups of candidate paths meeting constraints; performing simulation deduction on the candidate paths, and calculating a comprehensive score of expected energy consumption, a risk value and a task completion degree of each path based on a threat assessment result; and selecting an optimal path according to the comprehensive score, and generating a final obstacle avoidance strategy. According to the method, the defects of response lag and scene adaptability caused by dynamic obstacle prediction misalignment, layered decision delay and fixed parameter stiffness of a traditional obstacle avoidance system are overcome.

The invention discloses a surface mine transportation real-time intelligent scheduling method and system based on multi-objective optimization. The method comprises the steps that S1, an initial data loading module initializes the system; s2, the algorithm module verifies the vehicle state after receiving the scheduling request; s3, executing a path generation strategy according to states, wherein a normal vehicle generates all feasible routes based on the global path network; when a user appoints a starting point and a terminal point, a point-to-point optimal route is intelligently generated; s4, performing dynamic optimization scheduling based on the multi-source data; optimal task allocation is solved through a multi-objective optimization model, and a vehicle and route mapping relation is output; and S5, a scheduling result is issued to the vehicle-mounted terminal through the service layer, and a terminal interface displays a recommended route and operation guidance. According to the invention, a dynamic event-driven mechanism and a multi-source data collaborative optimization intelligent scheduling framework are fused, so that efficient distribution of vehicle tasks, real-time generation of paths and quick response to abnormal conditions are realized.

The invention discloses an additive manufacturing intelligent repairing and surface optimizing method for a complex special-shaped curved surface, and particularly relates to the technical field of additive manufacturing. A three-dimensional digital model is obtained, and a complex special-shaped curved surface defect area is recognized; extracting a local morphology parameter set; constructing a repair path generation model based on a graph neural network, and performing collaborative optimization of forming precision, residual stress and deposition stability on the path through a multi-objective optimization algorithm; additive manufacturing equipment is controlled to perform material deposition according to the optimized path; carrying out online error detection by adopting a structured light system, judging repair quality through a fuzzy decision tree model, and triggering path reconstruction if the repair quality exceeds a threshold value; after repairing is completed, surface micro texture factors are extracted, and functional texture regulation and control are conducted through the laser micromachining technology; according to the method, high-precision intelligent repair of the complex curved surface area and collaborative improvement of surface function performance are achieved, and the method is suitable for the fields of aerospace, energy, high-end equipment manufacturing and the like.

The invention provides a geological data driving path optimization and settlement prediction method for pipe jacking construction, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring sparse drilling point location geological data, constructing a geological data set including soil layer types, forming continuous geological feature tensors through multi-dimensional interpolation, and generating a path vector sequence by combining starting and ending points and building distribution to represent crossing tracks of different paths under geological conditions; and constructing a path graph topological structure through a graph coding neural network, performing feature propagation, obtaining a path comprehensive score vector, and screening an optimal path meeting structural integrity and settlement response constraints. Further identifying settlement trend sensitive points by utilizing the settlement risk prediction values and the accessibility scores of the nodes, constructing a settlement trend map and calculating settlement transaction coefficients. And the transaction coefficient is fed back to a path generation link and is used for iteratively optimizing path sampling density and node distribution so as to realize dynamic correction and stable convergence of a path scheme.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention provides a logistics transportation optimization method and system based on a traffic logistics large model, and the method comprises the steps: carrying out the element extraction through receiving a basic scheduling instruction of a logistics transportation task, generating core element information through semantic scene analysis, and carrying out the transportation scene adaption based on the core element information. Generating a scene influence factor set in combination with a depth feature mining result of the historical transportation scene database; then, calling a pre-trained traffic logistics large model to carry out collaborative path decision on the scene influence factor set, and outputting a candidate path scheme population containing a path topological structure and cost feature distribution through interactive iteration of a path generation unit and a cost evaluation unit in the model; and performing multi-round evolution screening on the candidate path scheme population according to a preset optimization target, determining an optimal path scheme meeting transportation requirements, and generating a transportation scheduling instruction set to be pushed to a transportation execution terminal after conversion. According to the invention, the adaptability, decision-making precision and reliability in the logistics transportation optimization process can be effectively improved.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention discloses a subway service robot real-time path planning method based on a graph neural network, and the method comprises the following steps: scanning the environment in a subway station, completing the map construction, and obtaining basic environment data; performing target detection and personnel statistics to form an environment and passenger flow fusion map; taking the fused map as input, setting a starting point and a target position, and outputting a global path search initial state; calling an improved A * algorithm to carry out node expansion, calculating a cost estimation function, and generating candidate paths; performing redundant node deletion and arc smoothing processing on the candidate path, performing step length dynamic adjustment, and outputting an optimized global path; performing local path planning by adopting an improved dynamic window method, and outputting an update path in real time; a passenger evacuation scheme is generated and is output through a display screen and voice broadcast, evacuation is guided, and passing is guaranteed. According to the invention, real-time path planning of the subway service robot is realized.

The invention relates to the technical field of network security, and provides an APT attack traceability and path restoration method and system, attack path validity is verified through a dynamic causal element path generator in combination with an anti-factual adversarial network, and fine-grained entity modeling is realized through a hierarchical multi-modal entity inference system. A causal attention mechanism is optimized to improve association analysis precision, a federal incremental learning framework is constructed to realize dynamic updating and privacy protection, and the problems of poor rule adaptability, coarse granularity of entity modeling, insufficient causal association distinguishing and low calculation efficiency in the traditional technology are effectively solved. The method has the technical effects of dynamically generating an effective attack path, improving attack traceability accuracy, reducing calculation overhead and enhancing privacy protection.

The invention relates to the technical field of personalized learning, and discloses a personalized learning path recommendation system based on a knowledge graph. The system comprises a user portrait module, a knowledge graph construction module, a path generation module and an effect evaluation module. The user portrait module obtains user learning parameters of the learner; the knowledge graph construction module receives learning domain information, constructs a domain knowledge graph based on user learning parameters, sets knowledge node granularity, and performs association analysis on knowledge units to obtain association weight values of the knowledge nodes; the path generation module dynamically plans a learning path according to the association weight value and adjusts the learning path in real time in learning; and the effect evaluation module monitors the knowledge point mastering degree, the learning progress deviation and the path completion rate of the learner, and performs abnormal early warning. The system can provide learning paths fitting individual differences for learners, and improves the pertinence and flexibility of learning.

The invention discloses a scene semantic understanding-oriented surveying and mapping technical scheme intelligent generation method, and particularly relates to the technical field of surveying and mapping intellectualization, and the method comprises the following steps: constructing a task description vector to extract task feature parameters, obtaining dynamic environment perception data to recognize semantic feature tags, and generating structured representation; constructing a task-driven semantic vector field based on the task description vector and the semantic feature tag, distributing gravitation weight, calculating semantic weight entropy, dynamically adjusting the priority, finally determining a main control semantic path according to a main semantic sorting structure, and generating a surveying and mapping technical scheme matched with the current task. According to the method, the task description vector and the semantic vector field are constructed, the standardized expression of a surveying and mapping task target and dynamic association modeling of scene semantics are realized, a semantic weight entropy regulation and control mechanism is introduced, the problems of semantic priority misjudgment and scheme deviation are solved, and the accuracy, the stability and the intelligent level of surveying and mapping technical scheme generation are improved.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention discloses a power equipment inspection path planning method and device based on laser and vision, and relates to the technical field of intelligent robots and power inspection, and the method comprises the steps: constructing a multi-sensor system, carrying out the external parameter calibration and time synchronization of each sensor, and carrying out the environment modeling and positioning through employing an improved LIO-SAM algorithm; performing power equipment identification; generating an optimal inspection path based on the calculated coordinate set of the power equipment; according to the optimal inspection path, dynamic obstacles are detected in combination with semantic and geometric information, obstacle avoidance is achieved through local track adjustment and path re-planning, and a planned path is obtained; and driving the inspection robot to autonomously move according to the planned path, acquiring an image and infrared data at the target power equipment, and feeding back a result to the background system to complete closed-loop inspection. According to the invention, the autonomous inspection of the whole process of environment modeling, target equipment identification, path generation, dynamic obstacle avoidance and the like can be effectively realized.

The invention provides a personalized learning path planning system and method, and belongs to the technical field of emerging software and emerging technical services, and the system comprises a data acquisition module which is used for collecting learning behavior data of a terminal user, obtaining a learner portrait package based on the learning behavior data, and sending the learner portrait package to a server; the path planning module is used for carrying out node matching on the learning ability feature vector and a pre-constructed knowledge and skill map, outputting a to-be-learned content node set, dividing learning advanced levels and generating learning path description containing to-be-learned nodes and the advanced levels, and the path generation module is used for generating a primary path planning scheme, the scheme feasibility verification module is used for carrying out scheme feasibility verification in combination with the learning advanced level and the path adaptation parameters, and then outputting a final executable path scheme, and the scheme execution module is used for executing the final executable path scheme and controlling learning content pushing and progress adjustment. The problems that in the prior art, personalized learning path planning is not high in precision, not high in adaptability, lack of dynamic optimization and the like are solved.

The invention relates to a knowledge graph-based teaching resource database construction method and system, and belongs to the technical field of data resource processing, and the method comprises the following steps: S1, obtaining and preprocessing a multi-source heterogeneous teaching resource: carrying out the natural language processing of an unstructured text to recognize the association of an entity and a key concept, analyzing the user behavior log to extract hidden knowledge point association; s2, defining a multi-dimensional teaching knowledge graph mode layer: constructing a multi-dimensional graph mode layer; s3, collaborative extraction and fusion of graph entities and relationships: collaborative extraction of core teaching entities and specific relationship types from the preprocessed teaching resources to construct a knowledge graph; s4, adaptive graph updating and teaching path generation: taking the constructed knowledge graph as an underlying database, and responding to knowledge state query of a user; the method has the beneficial effects that massive and heterogeneous teaching resources and the knowledge graph are effectively fused.

The invention provides an unmanned aerial vehicle path planning method and system and a storage medium, and the method comprises the steps: constructing a three-dimensional path planning model of an unmanned aerial vehicle in a target flight region; solving the three-dimensional path planning model by using an improved artificial bee colony algorithm, obtaining the optimal flight path of the unmanned aerial vehicle under each target function, and summarizing the optimal flight path into a Pareto optimal solution set; constructing a plurality of decision intelligent agents in one-to-one correspondence with the plurality of objective functions, performing multi-dimensional scoring under different objective functions on each flight path in the Pareto optimal solution set by using the plurality of decision intelligent agents, obtaining a comprehensive score of each flight path, and determining a global optimal unmanned aerial vehicle flight path based on the comprehensive score; according to the method, through refined multi-dimensional constraint modeling, an improved multi-target artificial bee colony algorithm and multi-agent collaborative decision based on a near-end strategy optimization algorithm, full-process optimization from path generation to intelligent decision is realized.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly discloses an unmanned aerial vehicle path planning method based on a multi-strategy fusion information acquisition optimization algorithm, and the method comprises the steps: obtaining the digital elevation model map data of a target region, and constructing a three-dimensional environment space model based on a task environment; constructing a path cost function and initializing an optimization algorithm population, wherein the initialization adopts three strategy fusion mechanisms of hypercube initialization, chaos initialization and random initialization to enhance the diversity of the population; a crisscross strategy is introduced, the adaptability and stability of the algorithm in the dynamic search process are enhanced, and a sine disturbance mechanism is adopted, so that the global optimization capacity and local development precision of the algorithm are enhanced; the flight path of the unmanned aerial vehicle is constructed by optimizing the path point sequence, and the path is smoothed by introducing B spline interpolation, so that the path is ensured to have continuity and feasibility, the flight requirement of the unmanned aerial vehicle is met, and the robustness and environmental adaptability of path generation are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to a multi-aerial vehicle cooperative path planning method, system and device in a city and a storage medium. Comprising an information acquisition module, a path generation module, a clustering updating module and an optimal judgment module. According to the method, the initial position, the target position and the obstacle information of each unmanned aerial vehicle are acquired, an urban low-altitude airspace task scene is defined, the MP-GWO algorithm is utilized to generate a multi-aerial-vehicle cooperative initial path, differential penalty coefficients are set for different types of no-fly zones, a K-means clustering mechanism is embedded into a GWO main cycle, and the multi-aerial-vehicle cooperative initial path is obtained. According to the method, parallel sub-populations are divided according to a multi-target fitness vector, each sub-population independently executes an alpha, beta and delta guide updating strategy, a triple termination judgment mechanism integrating fixed iteration times, a convergence threshold value and elite stagnation is adopted, a current optimal planning path is output, efficient optimization of the path in a complex urban environment is achieved, and the path optimization efficiency is improved. The method is suitable for urban low-altitude airspace multi-unmanned aerial vehicle cooperative tasks.