120 results about "Travelling salesman problem" patented technology

The invention belongs to the related technical field of path optimization, and discloses an anti-collision laser cutting path optimization method and system based on a potential field ant colony algorithm, and the method comprises the following steps: step 1, making an initial cutting sequence of parts by taking the shortest moving distance of a cutter as a target; the initial cutting sequence is obtained by a traveling salesman problem solved by adopting an ant colony algorithm; 2, according to the anti-collision constraint, a potential field ant colony algorithm is adopted to optimize the cutting sequence to obtain an optimal path; according to the potential field ant colony algorithm, a gravitation factor and a repulsive force factor are added according to an artificial potential field method, so that a heuristic function of the potential field ant colony algorithm is improved. According to the method, the anti-collision optimization effect is achieved in the mode that the gravitation guides path searching and repulsive force eliminates obstacles, the number of paths passing through a cut area is reduced, the laser cutting speed is increased, the cutting quality is ensured, meanwhile, all planning of the part cutting paths is completed, and the machining efficiency is improved.

The invention discloses an unmanned aerial vehicle autonomous target search method, system and device based on hierarchical decision, which combine global exploration and local search to improve the search efficiency of an unmanned aerial vehicle in a complex unknown environment. The method comprises the following steps: in a global exploration layer, constructing an incremental environmental map, and modeling an access sequence of to-be-searched and explored areas as an asymmetric traveling salesman problem; a Bi-RRT algorithm is utilized to generate a connection path in a free space, the visibility cost of the path is evaluated, and an access sequence with the minimum total cost is selected; in the local search layer, a hierarchical Gaussian process is used for fitting and evaluating the search value of the target area, and a priority target area is determined; generating a plurality of candidate viewpoints for the local target area, and selecting an optimal viewpoint as a local target search point; and navigating to the target search point and identifying the specific target article in combination with the positioning information and the target detection model. The method has efficient search and environment adaptability.

The invention discloses a three-dimensional environment coverage method for on-line sub-region updating and hierarchical planning, which remarkably improves the exploration efficiency and reduces the calculation consumption through on-line sub-region updating and hierarchical planning. Firstly, a fast environment preprocessing method is used, only updated boundary extraction leading edge points are checked, and therefore the calculation burden is reduced. Secondly, constructing a sparse topological graph, discretizing the space, and storing topological nodes by using a KD tree, so that time-consuming path search on a three-dimensional voxel map is avoided; in addition, hierarchical structure division of sub-regions is provided, the environment is divided into a plurality of adaptive sub-regions, environment information is updated in real time, and subsequent exploration planning is supported. A hierarchical planning strategy is adopted, environment coverage is modeled as a traveling salesman problem, a global coverage path is solved, and the total path length of subarea access is considered. Compared with a greedy strategy, the method provides a long-term perspective and improves the exploration efficiency. Finally, the efficiency and feasibility of the method are proved through simulation and actual measurement verification.

The invention provides a TSP path combinatorial optimization method and system based on reinforcement learning, and the method comprises the steps: constructing an anisotropic graph neural network as a backbone network of a diffusion model through the combination of solution distribution learning and combinatorial optimization target learning, capturing a complex relation in graph structure data through the representation capability of the network, and carrying out the optimization of a TSP path. And probability distribution is modeled by using a diffusion model in a single-to-Markov forward process. In addition, two kinds of accelerated sampling methods DDIM and DPM-solver of diffusion models are introduced, the sampling process of denoising is accelerated, and the training efficiency is improved. According to the method, direct preference optimization can be introduced into a diffusion model to further provide preference-guided combinatorial optimization (PGCO), so that the generalization ability and adaptability of a traveling salesman problem (TSP) solving model are improved, and a more efficient and high-quality model for solving large-scale combinatorial optimization is provided.

The invention provides a deep reinforcement learning and heuristic algorithm combined traveling salesman problem solving method. The method comprises the steps of defining a task of learning an improved heuristic algorithm to solve a traveling salesman problem as a Markov decision process; designing a new model M based on Transform, parameterizing a strategy network, and introducing an action discarding mechanism to prevent action selection from over-fitting; a deep reinforcement learning method (RL-SA) combined with a simulated annealing mechanism is provided to learn a selection strategy of a node to an operator so as to improve the performance of a 2-opt algorithm; the RL-SA uses a whale optimization algorithm to generate an initial solution to improve the sampling efficiency, and uses a Gaussian perturbation strategy to solve the sparse reward problem in reinforcement learning. The method is obviously superior to an existing learning-based method in a TSPLIB data set and a real container liner shipping data set, and the gap between the method and a highly optimized solver is further reduced.

The invention discloses a strip mine watering cart scheduling method, and belongs to the technical field of strip mine flying dust monitoring. The server side obtains position information of all dust raising points; reading a mining area map, and calculating the shortest path between each watering cart and the dust raising point and the shortest path between the watering carts; and the positions of dust raising points are sequenced, so that the shortest total route of operation is ensured. The distance between dust raising points is automatically calculated through the shortest circuit algorithm, the sprinkler scheduling problem is converted into a traveling salesman problem to be solved, and the operation efficiency of the sprinkler is improved. Compared with a traditional scheduling method, the method automatically generates the operation route of the watering cart, can effectively reduce the scheduling difficulty of the watering operation, and improves the operation efficiency of the watering cart.

The invention relates to the technical field of wind power plant inspection, and discloses an unmanned aerial vehicle inspection path planning method and device for a wind power plant, equipment and a medium, and the method comprises the steps: building an inspection scene of the wind power plant and an unmanned aerial vehicle no-fly zone in the inspection scene, and determining a fan inspection point based on the unmanned aerial vehicle no-fly zone; taking the minimum inspection path cost as a target, and enabling a preset unmanned aerial vehicle global inspection task to be equivalent to a traveling salesman problem without a fixed starting point based on a fan inspection point; solving a feasible inspection path and a corresponding path length in the traveling salesman problem without a fixed starting point by adopting a target offset fast search tree algorithm of strategy interaction; and solving an optimal inspection path by adopting a genetic algorithm based on the feasible inspection path and the corresponding path length. The routing inspection path can avoid mountain obstacles and no-fly zones and deviates to the target point, the routing inspection efficiency is high, and the problem that unmanned aerial vehicle routing inspection path planning is not carried out for the wind power plant scene in the mountain land is solved.

A quantum-inspired parallel annealing method that enables full parallelism and improves solution quality, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. Tasks are experimentally solved, including unweighted and weighted Max-Cut and traveling salesman problem using an integrated memristor chip. The method claimed herewith effective exploits the natural parallelism, analog conductance states and all-to-all connection provided by memristor technology, and therefore demonstrates significant improvements in time- and energy-efficiency compared to previous simulated annealing and Ising machine implemented on other technologies, having a large potential for solving complex optimization problems with greater efficiency.

The invention discloses an optimization method for a multi-modal traveling salesman problem, and the method comprises the steps: 1, carrying out the preliminary optimization of a solution, and making a preparation for obtaining a key edge set in step 2; 2, on the basis of the key edge set obtained in the step 1, two individual generation strategies are adopted to carry out rapid global convergence on a solution, an environment selection strategy is adopted to delete or retain a multi-modal solution, and a redundant individual concept is provided; and step 3, adaptively generating clusters based on the target spatial distance, arranging an inter-class multi-modal solution according to the number of clusters, generating individuals based on archiving and inter-class multi-modal arrangement, and performing local optimization on the individuals. According to the multi-modal traveling salesman optimization method, the multi-modal traveling salesman problem is solved by adopting a staged optimization method of preprocessing-global convergence-local optimization, more multi-modal solutions can be found while a global optimal solution is found, and a more efficient solution is selected according to own specific requirements.

A parameter-setting method, system, and apparatus, a device, a storage medium, and a program product, relating to the technical field of database systems. The method comprises: obtaining parameter value ranges of various parameters of a database and a target function of the database (S310), the target function being used for measuring the performance index of the database; on the basis of the parameter value ranges of the various parameters and the target function, constructing a traveling salesman problem (TSP) model (S320), paths in the TSP model being parameter value combinations of the various parameters, and the path length in the TSP model being the target function; on the basis of the TSP model, constructing a quantum algorithm model (S330); on the basis of the quantum algorithm model, solving for the optimal solution of the TSP model (S340); and, on the basis of the parameter value combination corresponding to the optimal solution of the TSP model, setting the various parameters of the database (S350).

The invention relates to an aircraft air-dropped material recycling method and device and computer equipment. The method comprises the following steps: constructing a design variable of an optimal release point of the materials released by the aircraft; the design variables comprise release point coordinates and course angles; according to a motion equation of a material landing process and the design variable, establishing a relationship between the design variable and a landing point position vector; determining a drop point evaluation function according to a pre-acquired launching mode, simulating and calculating a drop point position vector corresponding to each course angle by adopting a course angle traversing mode, and selecting a corresponding course angle and a corresponding drop point position vector according to a drop point evaluation function optimization principle; and solving a traveling salesman problem by adopting a greedy algorithm, and traversing landing points of all the landing point position vectors to obtain an optimal path plan of material recovery. By adopting the method, the course angle can be dynamically adjusted according to the real-time environment data, and the material delivery requirement is met.

The invention discloses a multi-unmanned aerial vehicle cooperative regional patrol path planning method based on hierarchical optimization, and the method comprises the steps: firstly, abstracting a task space containing a base station and a plurality of target points into a weighted undirected complete graph, and building a mathematical model with a target of minimizing the total completion time of a system and the standard deviation of task time; then solving is carried out through a hierarchical strategy; firstly, k-means clustering is adopted to carry out initial task allocation; solving a traveling salesman problem for each task cluster by using a simulated annealing algorithm, generating an initial path, and performing local optimization by using a 2-OPT algorithm; and finally, carrying out global collaborative optimization by adopting adaptive large-scale neighborhood search, dynamically adjusting task allocation and paths through a cross-unmanned aerial vehicle damage and repair operator, and carrying out iterative optimization according to a composite acceptance criterion and an adaptive mechanism to generate a near-optimal scheme. According to the method, through tight coupling of task allocation and path planning, the defect that a traditional method is prone to local optimization is effectively overcome, and joint optimization of completion time and load balancing is achieved.

The invention relates to a multi-robot cooperation-oriented unknown scene exploration method, which specifically comprises a local exploration stage and a global exploration stage, in the local exploration stage, each robot explores in a local exploration area, each robot generates a semantic-leading viewpoint, and each robot generates a semantic-leading viewpoint; aiming at the semantic-leading viewpoint, solving a traveling salesman problem to generate a local path; in the global exploration stage, a dynamic topological graph is constructed, the semantic-leading viewpoint is stored to leaf nodes of the topological graph and shared to all robots, when each robot leaves the local exploration area, the leaf nodes in the topological graph are considered, a global path is generated by solving a multi-traveling salesman problem, and the global path is obtained. And the robots are coordinated to enter a new local exploration area nearby, so that multi-robot collaborative exploration is realized. According to the invention, efficient multi-robot collaborative exploration can be realized.

The invention discloses a printed circuit board element placement method and device based on a traveling salesman problem and a medium, belongs to the technical field of traveling salesman problems and printed circuit boards, and aims to solve the technical problem that diversified constraint conditions and efficiency are difficult to consider when the placement sequence of traditional printed circuit board elements is optimized. According to the technical scheme, the method comprises the following steps: constructing a training data set; performing feature coding on the placement requirement of the printed circuit board element by using an encoder to obtain a coding result; decoding a coding result through a decoder based on a double-pointer network, and generating an element placement sequence meeting the priority constraint; and training the encoder-decoder architecture by adopting a near-end strategy optimization algorithm based on reinforcement learning, and finally obtaining a deep reinforcement learning model for solving the priority constraint traveling salesman problem for placing the printed circuit board element.

The invention provides an automatic scanning and measuring method for a blade margin plate gap, and belongs to the technical field of industrial machining.The automatic scanning and measuring method comprises the steps that a corresponding relation between a workpiece and a machine coordinate system is established by constructing a three-dimensional measuring coordinate system, and a laser scanning measuring head is adopted for conducting full-circumferential automatic scanning to obtain high-density point cloud data; fitting a gap curved surface by using a least square method, calculating geometric deviation, constructing a multi-dimensional error compensation vector field of a spatial position, an optical system, material thermal expansion and the like, establishing an airflow model based on fluid mechanics, and analyzing airflow characteristics through a numerical solution and analytical solution method. A measurement problem is converted into a traveling salesman problem, an improved ant colony algorithm is applied to optimize a scanning path, gap uniformity analysis is carried out, a gap curve is decomposed through Fourier series expansion to recognize an abnormal rule, a comprehensive evaluation report is finally generated, and the core problem that the measurement precision is affected by multi-dimensional errors is solved.

The invention discloses a traveling salesman problem solving method based on a quantum self-attention neural network, and belongs to the field of quantum computing. A classic encoder-decoder structure is adopted, a quantum self-attention layer is designed in the encoder and comprises a quantum multi-head attention layer and a quantum full-connection feed-forward layer, and a variable component sub-circuit is used in a sub-layer to replace a full-connection layer of a classic neural network. Firstly, an encoder receives an input data set, node features are embedded into a quantum self-attention layer, and a complex relation between nodes is extracted through a quantum multi-head attention mechanism. The decoder then stepwise outputs the sequence of nodes to form an optimal path according to the embedding generated by the encoder. In the model training stage, a REINFORCE deep reinforcement learning algorithm is utilized, the total path length serves as an optimization target, and model parameters are updated through strategy gradients. Compared with the existing algorithm, the method has the advantage that the performance is well improved.

The invention belongs to the field of intelligent power grid inspection. The invention discloses a mobile nest assisted multi-unmanned aerial vehicle electric power inspection path planning method. The method is characterized by comprising the following steps: task modeling based on space coordinates and inspection constraints; a path budget adaptive clustering algorithm is adopted to realize efficient division of task clusters; constructing the task cluster as a generalized travel salesman problem (GTSP), converting the GTSP problem into a TSP problem through Noon-Bean mapping, and planning an optimal parking path of the MGV in combination with an LKH-3 algorithm; in each cluster, a two-segment hybrid coding NSGA-II algorithm is adopted to realize cooperative solution of task allocation and path optimization of multiple unmanned aerial vehicles; and finally, infeasible task clusters are identified through a fault-tolerant feedback mechanism to perform re-clustering and path planning strategies, so that the performability of the inspection scheme is ensured. According to the method, on the basis of considering the path cost and task balance, the global coordination and execution efficiency of multi-unmanned aerial vehicle collaborative operation are improved, and the method has good universality and engineering application prospects.

The invention discloses an unmanned aerial vehicle routing inspection sequence planning method and system for disaster situation data acquisition and return. The method comprises the steps of establishing an unmanned aerial vehicle routing inspection sequence planning task scene for unmanned aerial vehicle disaster situation data acquisition and return; in the task scene, based on a multi-path traveling salesman problem, establishing a post-disaster data acquisition and return task system model; and solving the post-disaster data acquisition and return task system model by adopting a multi-strategy improved grey wolf optimization algorithm to obtain an unmanned aerial vehicle inspection sequence scheme, and the method and the system can realize unmanned aerial vehicle inspection sequence planning.

The present invention belongs to the technical field related to path optimization, and discloses a collision-avoiding laser cutting path optimization method and system based on a potential field ant colony algorithm. The method comprises the following steps: Step 1: formulating an initial cutting sequence for parts with the goal of minimizing tool movement distance; the initial cutting sequence is obtained by solving a traveling salesman problem using an ant colony algorithm; Step 2: optimizing the cutting sequence using a potential field ant colony algorithm to obtain an optimal path based on collision-avoiding constraints; wherein the potential field ant colony algorithm adds an attraction factor and a repulsion factor based on an artificial potential field method to improve the potential field ant colony algorithm heuristic function. The present invention achieves collision-avoiding optimization by using gravity to guide path search and repulsion to eliminate obstacles, thereby reducing the number of paths passing through the cut area, increasing laser cutting speed, and completing the entire planning of the part cutting path while ensuring cutting quality, thereby improving processing efficiency.

The invention discloses an intelligent planning method and system for an unmanned aerial vehicle operation path in a sugarcane planting area, and relates to the technical field of sugarcane planting, and the method comprises the steps: obtaining the position information of each sugarcane planting area in a preset area and the priority of each sugarcane planting area; based on a traveling salesman problem mathematical model, in combination with the position information of each sugarcane planting area and the priority of each sugarcane planting area, converting the operation sequence of all the sugarcane planting areas into a total cost function about the total length of the path; the total cost is minimized based on the total cost function, the sequence of unmanned aerial vehicle operation in all the sugarcane planting areas is obtained, the position coordinates of the starting operation point and the position coordinates of the ending operation point of each sugarcane planting area are obtained according to the sequence, and then a path for unmanned aerial vehicle operation in the preset area is generated. Operation efficiency can be effectively improved, and the service life of the unmanned aerial vehicle is prolonged.

The invention provides an optical guiding method for recycling a deep-sea unmanned underwater vehicle, and belongs to the technical field of underwater vehicle recycling. Four blue-green LED guiding light sources are non-uniformly and annularly distributed and installed on the front end face of a recycling cage, the three-dimensional coordinates of the guiding light sources are calibrated in advance, and guiding light source images are collected in real time through a binocular camera; an adaptive exposure control technology is adopted to adjust image quality, statistical information calculation is performed on an identified light source contour, an effective guide light source target is determined through convex constraint configuration judgment and ellipse fitting screening, four guide light sources are constructed as a traveling salesman problem, and a nearest neighbor heuristic algorithm is adopted to complete number matching. A rotation matrix and a translation vector are solved through a singular value decomposition method according to the pre-measured coordinates and the real-time calculated coordinates, and collaborative optimization is carried out at the same time, so that the technical problems of insufficient optical guidance positioning precision and poor real-time performance in the recovery process of the unmanned underwater vehicle in the deep sea environment are solved.

The invention discloses a layered planning method for indoor autonomous exploration of a mobile robot based on dynamic prediction, and the method comprises the steps: S1, dividing an exploration environment where the robot is located into a plurality of local subspaces, and marking the state of each subspace as an unexplored state, an explored state, an explored state or a semi-explored state according to the data of a sensor and a semantic detection result; s2, establishing a global topological graph connected with each subspace, and planning a global access path by using a traveling salesman problem model; s3, integrating a pedestrian trajectory prediction model in the selected current subspace, and evaluating and selecting a local viewpoint capable of maximizing information gain and minimizing dynamic collision risk based on a prediction result; the method has the beneficial effects that a semi-exploration state and a self-adaptive revisit mechanism are introduced, and when the door is possibly opened, the check is turned back, so that the dead angle of a map is eliminated, and the exploration completeness is improved; pedestrian trajectory prediction is incorporated into viewpoint scoring, and people deadlock is avoided.

This invention discloses a method and system for multi-UAV task allocation and conflict-free path planning, belonging to the field of industrial IoT UAV technology. For collaborative inspection scenarios involving multiple heterogeneous UAVs, this invention first transforms multi-UAV task allocation into a single-travel salesman problem by using a multi-travel salesman problem transformation method, generating an optimal task sequence that satisfies sensor matching constraints. Then, it constructs a multi-agent reinforcement learning model based on MAPPO, combined with LSTM temporal environment encoding, to train a distributed conflict-free path planning strategy. This invention balances solution efficiency and planning quality, can be scaled to UAV swarms of any size, and effectively solves the problems of existing algorithms struggling to balance efficiency and quality, poor scalability, and insufficient dynamic obstacle avoidance capabilities. It is suitable for industrial inspection scenarios such as hazardous materials warehouses.

The invention provides a laser cleaning path planning method and system and electronic equipment, and relates to the technical field of laser surface treatment.The method comprises the steps that imaging collection is conducted on the surface of a to-be-cleaned workpiece, coordinate registration and binarization processing are conducted, and a binarization image of the surface of the to-be-cleaned workpiece is obtained; regularly partitioning the surface of the to-be-cleaned workpiece according to the binary image to obtain a plurality of sub-partitions, and performing path coverage on each sub-partition to obtain a local path of each sub-partition; mapping a starting point of the local path of each sub-partition to obtain a global representative point set of all the local paths, and generating a traveling salesman problem model according to the global representative point set; solving the traveling salesman problem model through a native heterogeneous competitive-cooperative ant colony optimization algorithm to obtain an optimal global access sequence of all local paths; and generating a laser cleaning scanning path of the surface of the to-be-cleaned workpiece. According to the invention, the sensitivity of path planning is improved, and finally a high-quality laser cleaning scanning path is generated.

The invention discloses an integrated processing path generation method for a triangular mesh curved surface, and the method comprises the steps: determining an optimal clamping direction through two-stage clustering based on the direction of an outer normal of the surface of a triangular mesh model, selecting an optimal class cluster through a k-means clustering normal vector in combination with a feature evaluation function, and determining a model base according to height secondary clustering; the shoal and the steep area are divided according to a preset boundary angle, small-area isolated shoal sub-areas are deleted, and holes in the shoal are processed through boundary expansion; constructing a GPU parallel acceleration collision detection model, and obtaining a feasible machining direction set of each point for the geometric structure of the ball end milling cutter; a cutting path section is generated based on a section plane method, a section plane normal vector is optimized and determined in a shoal area, and a steep area follows a clamping direction; generating an optimal cutter lifting fast moving line by combining a path section endpoint feasible direction domain and a traveling salesman problem optimization algorithm; and carrying out adaptive path encryption by adopting geometric projection dynamic interval adjustment to ensure that a preset residual height constraint is met.

The invention discloses a multi-modal multi-target travel salesman problem optimization method, which is used for searching more equivalent optimal solutions. An edge crossing strategy adopting non-dominated sorting is introduced to generate excellent individuals; the Spearman distance is combined with a diversity measurement standard based on the same edge in a decision space, and the similarity between different loop solutions is allowed to be evaluated more accurately; in addition, a concept of a special crowding distance is introduced in an environment selection process so as to ensure that a population keeps diversity in a balanced manner in a decision space and a target space; and after the evolution is finished, selecting the best individual to evaluate on the test set. According to the method, the multi-target traveling salesman problem is solved through MMTSPDS, and the algorithm provides various different solutions for the traveling salesman problem by storing a plurality of high-quality multi-modal solutions, so that the scientificity and flexibility of decision making are improved.

This invention belongs to the field of path planning technology and discloses an optimal path planning method for multi-specification point drilling tasks. The method includes teaching the locations of diamonds and drill holes, the properties of diamonds, and the properties of diamonds pasted to match each drill hole. A pick-and-place distance matrix is ​​generated based on the locations of diamonds and drill holes, and a diamond classification table is generated based on the properties of diamonds and the properties of diamonds pasted to match each drill hole. Based on the pick-and-place distance matrix and the diamond classification table, an optimal diamond-drill hole pairing scheme is solved using the Hungarian algorithm. Based on the optimal diamond-drill hole pairing scheme, an ant colony algorithm and a tabu search algorithm are used to determine the optimal diamond picking sequence, thereby obtaining the optimal path. This method eliminates the path access sequence constraints of the pick-and-place model, thereby simplifying the multi-specification point drilling problem into a generalized traveling salesman problem of a directed graph, significantly simplifying the computational complexity of path selection.

The invention discloses a path planning method with a time window in a multi-type place, which is based on a defined travel salesman problem M-TSPTW with the time window in the multi-type place and an objective function, and comprises four stages: classifying nodes according to places and time, and obtaining an initial path of a travel salesman every day based on nodes which can only be accessed in a certain day; searching the idle time of the initial path, and if the open time and the idle time of the multi-day time window node are overlapped, inserting the node into a corresponding position; for the nodes which are not successfully inserted into the path, performing neighborhood search based on the idle time of the current path, and adding the multi-day time window nodes into the path through multiple times of replacement to form a new path; and disturbing the new path to reduce the total length of the final path, and only changing the access sequence of the nodes in a certain day, or selecting and exchanging the nodes in the paths in certain two days. In order to solve the problem of overlong time consumption of the existing hard time constraint M-TSPTW precise algorithm, the invention provides a partially fixed strategy and a time-based neighborhood search strategy, thereby reducing the calculation amount and accurately judging the feasibility of the problem.

The present invention belongs to the technical field of agricultural situation monitoring, and particularly relates to an unmanned aerial vehicle (UAV) agricultural situation monitoring aerial survey method based on spatial sampling optimization, which includes constructing a digital farmland base model and dividing the smallest agronomic units with independent agronomic attributes; calculating the number of sampling points for each unit based on a sampling model, performing Monte Carlo random sampling and excluding abnormal points in combination with a spatial coordinate verification mechanism to achieve dynamic sampling planning; using a traveling salesman problem optimization algorithm to generate an intelligent flight path; implementing automatic control of the aerial survey task by calculating the flight altitude; dynamically adjusting the sampling density according to the crop growth heterogeneity coefficient to obtain the agricultural situation information of the smallest agronomic units, and integrating and generating a plot-level monitoring report. The UAV agricultural situation monitoring aerial survey method based on spatial sampling optimization according to the present invention can significantly improve the efficiency and accuracy of farmland monitoring and provide precise decision-making support for farmland management through the collaborative design of optimizing the flight path by a multi-objective ant colony algorithm and a dynamic hierarchical sampling mechanism.

The invention discloses an intelligent storage multi-AGV cooperative path planning method, device and system and a medium, and relates to the technical field of path planning. The path planning method comprises the following steps: acquiring carrying task information of a target AGV so as to determine an operation point set which needs to be accessed by the AGV; warehouse channel topological information is obtained, and the shortest driving distance between any two operation points is calculated to obtain a distance matrix; and constructing a traveling salesman problem instance by taking the operation point as a node and the distance as an edge weight. Obtaining m nearest neighbor nodes of each node in the distance matrix in the traveling salesman problem instance and a local neighborhood matrix of each node; and inputting the local neighborhood matrix into a pre-trained anchor point prediction network to predict whether each node is an anchor point or not, thereby dividing all nodes into an anchor point set and a non-anchor point set. And based on the distance matrix, the anchor point set and the non-anchor point set, constructing a planning path through an anchor point insertion algorithm. And converting the planned path into a transport path scheduling instruction of the AGV.