66 results about "Urban logistics" patented technology

The invention relates to the technical field of logistics distribution, and discloses a big data analysis method and system based on traffic transportation logistics, and the method comprises the steps: constructing a space-time diagram convolution network model, and predicting the traffic congestion propagation trend of an urban region; based on the congestion propagation trend, the order density and the distribution resource state, executing a space-time dynamic partitioning algorithm, and dividing the urban distribution region into a plurality of dynamic micro-regions; constructing a double-layer intelligent decision-making system, and generating a logistics fleet separation and combination decision and path planning; heterogeneous distribution resource dynamic marshalling is executed, and different types of distribution tools are combined into a miniature formation; using a multi-agent reinforcement learning algorithm to optimize the cooperation process in the micro formation; according to the invention, through fusion of congestion propagation prediction, dynamic micro-area division, double-layer intelligent decision and heterogeneous resource collaboration, key problems of traffic congestion, resource scheduling and'last kilometer 'distribution in urban logistics distribution are effectively solved.

The invention discloses an unmanned aerial vehicle take-off and landing site selection method for urban end logistics, and relates to the technical field of facility site selection, and the method comprises the following steps: S1, obtaining a building contour of a region to be subjected to site selection, and constructing a customer buffer region according to the building contour; s2, based on the customer buffer area, performing noise analysis and safety analysis on the unmanned aerial vehicle, and constructing an overall restricted area; and S3, based on the overall restricted area, constructing a target function taking the lowest cost as an optimization target, a target function taking the highest satisfaction as an optimization target and a plurality of constraint conditions, and obtaining a site selection layout result. According to the method, the applicability of the grid map method in the complex urban environment is verified, and a new thought is provided for the layout of the urban logistics unmanned aerial vehicle take-off and landing points.

The invention relates to the technical field of logistics collaborative optimization, in particular to an urban logistics distribution range collaborative optimization method and system, and the method comprises the following steps: obtaining a path network node and order coordinate time sequence, constructing a path mapping number, extracting a direction vector and an included angle difference value, and recognizing a path sudden change node and a density change node. Dividing boundaries, calculating density ratio differences, screening unbalanced sections, extracting direction and distance abrupt change nodes, reconstructing a path structure and outputting a boundary graph. According to the method, the corresponding relation between the order and the path node is enhanced by constructing the path mapping number sequence, the adaptive capacity of the boundary to the path trend is improved, the density ratio analysis is combined with the spatial distribution difference to identify the non-uniform region, the responsiveness of the boundary to the order aggregation situation is enhanced, and the boundary node is jointly judged through the included angle and the centroid distance. And target judgment and structural stability control of boundary adjustment are realized, connection coherence is kept in path structure reconstruction, and a boundary path shape with dynamic adaptation capability is output.

The invention discloses a dynamic and static combined urban logistics unmanned aerial vehicle task planning method, which comprises the following steps of (1) inputting immediate and non-immediate distributed dynamic and static combined urban logistics unmanned aerial vehicle task planning urban logistics order data, and preliminarily selecting a logistics unmanned aerial vehicle airport position by using a k-means clustering algorithm improved based on space-time constraints; and (2) determining a static task planning result for non-instant delivery orders by using a static task planning method based on an HPSO algorithm, determining the positions of logistics delivery points, optimizing the site selection of the unmanned aerial vehicle airports according to the distribution of the logistics delivery points, and balancing the number of tasks of each airport. And (3) proposing a dynamic task planning method for improving a DDQN algorithm based on a static task planning result, determining a dynamic task planning result of the instant delivery order, and finally determining a planning and scheduling scheme of the logistics unmanned aerial vehicle fleet. According to the invention, through fusion of urban logistics orders of instant delivery and non-instant delivery, unmanned aerial vehicle facility site selection and order distribution are more reasonable.

Aiming at the problem of time-dependent multi-target green vehicle path optimization, the invention designs a space-time attenuation factor-driven urban logistics low-carbon robust optimization method, takes the total vehicle distribution time and carbon emission as optimization targets, and adopts double-layer ant colony pheromones to guide ant colony search, so that the optimal path optimization is realized. The adaptive capacity of ants in a high-disturbance uncertain environment is improved, the convergence of solutions is enhanced, more solution sets balancing robustness and optimality are excavated through solution robustness evaluation and a feedback mechanism thereof, the diversity of optimal solutions is improved, an optimal vehicle path robust optimization scheme is obtained, the carbon emission of urban road network distribution is reduced, and the urban road network distribution efficiency is improved. And the distribution efficiency of logistics is improved.

The invention discloses a multi-access-considered vehicle-unmanned aerial vehicle distribution path optimization method, relates to the technical field of vehicle and unmanned aerial vehicle collaborative distribution optimization, and aims to solve a path planning problem caused by complex constraints and multi-target optimization in urban logistics distribution. According to the method, firstly, multiple constraint conditions of vehicle-unmanned aerial vehicle cooperative distribution are analyzed, energy consumption cost and carbon emission factors are fused into a target function, an integer programming model with the minimum time and transportation cost as double targets is constructed, and carbon emission cost is considered in the transportation cost; secondly, solving a vehicle path and an initial solution by adopting an ant colony algorithm; and finally, a large-field search algorithm is used to solve the problem of vehicle-machine cooperation path optimization. According to the method, the vehicle and unmanned aerial vehicle cooperative distribution logistics efficiency is effectively improved, the total cost is reduced, and powerful support is provided for development of green logistics.

The invention belongs to the technical field of urban logistics and resource management, and provides a logistics resource scheduling method based on rule guidance and hierarchical greedy, which comprises the following steps: S1, customer demand structured analysis; s2, carrying out multi-dimensional logistics resource attribute modeling; s3, realizing resource filtering through rule guidance; s4, defining a combined path template; s5, obtaining an optimal solution through a hierarchical greedy resource matching algorithm for resource scheduling; comprising the following specific steps: S5.1, carrying out resource level dependence modeling; s5.2, setting a comprehensive matching degree algorithm and a matching priority rule; and S5.3, optimizing a resource combination path based on a hierarchical sequence and a greedy strategy. The method can adapt to different customer requirements to realize the reasonability of resource combination, and can also guarantee better resource matching efficiency.

The invention discloses an urban low-altitude multi-unmanned aerial vehicle autonomous route planning system and method, and belongs to the technical field of low-altitude airspace intelligent route planning. Comprising a gridding airspace construction module for constructing a digital urban low-altitude airspace model, an air route planning module for generating respective initial air routes for a plurality of unmanned aerial vehicles on the basis of the gridding airspace model, and a conflict detection and resolution module for ensuring that all finally output air routes are globally conflict-free, the invention further discloses an urban low-altitude multi-unmanned-aerial-vehicle autonomous route planning method based on the system. Through airspace grid division, an improved three-dimensional jump point search algorithm and a conflict resolution mechanism based on conflict search, efficient generation and global conflict-free collaboration of an unmanned aerial vehicle route are realized, and the method is suitable for multiple scenes such as urban logistics distribution, routing inspection and emergency response.

The invention provides a time-varying road network-based vehicle path problem planning method and system with a time window. The method comprises the following steps of obtaining client point distribution information and road network historical time-varying information; the method comprises the following steps: determining a road speed time dependence function and road section congestion coefficients and driving time in different time periods based on road network historical data; constructing a time-varying road network-based vehicle path problem mathematical model with a time window; solving the established model by adopting an improved ant colony algorithm to obtain an optimal distribution scheme; and dispatching the vehicle to run according to the optimal distribution scheme. According to the method, the influence of the time-varying road network on urban logistics distribution is considered, the distribution route is reasonably planned, the total distribution cost is reduced, and economization, efficiency and greenization of urban logistics distribution are realized.

The invention discloses an urban logistics unmanned aerial vehicle take-off and landing point layout optimization method and system, and belongs to the technical field of path planning, and the method comprises the steps: taking all existing express cabinets in an urban region as a candidate take-off and landing point set, and obtaining the express cabinet transformation cost of each candidate take-off and landing point; obtaining the distribution cost from each candidate take-off and landing point to the demand point; determining customer coverage of all existing express cabinets in the urban area; obtaining all paths from each candidate take-off and landing point to the demand point without passing through the no-fly zone, and determining a plurality of take-off and landing point layout schemes corresponding to the selected take-off and landing points and the delivery paths based on all the paths as to-be-optimized individuals; a multi-objective optimization model is constructed by taking minimization of the sum of the transformation cost and the distribution cost of the express cabinet and maximization of the customer coverage degree as objectives, optimization is carried out on individuals to be optimized, and the optimal take-off and landing point layout and the planning result of the demand point range of the service of the take-off and landing point layout are determined. According to the method, reasonable and efficient operation of urban unmanned aerial vehicle logistics can be realized.

The invention relates to a shared warehouse and oil-electricity hybrid motorcade path optimization method considering battery replacement. The method comprises the following steps: establishing an order allocation scheme of a hybrid motorcade joint mode according to the freight volume and a distribution group; aiming at the order distribution scheme, constructing a hybrid motorcade path optimization model taking minimization of the total cost of the delivery task as a target function; and solving the hybrid motorcade path optimization model based on a hybrid heuristic algorithm to obtain an optimal path meeting the constraint of the hybrid motorcade path optimization model. According to the method, an order distribution mode is determined according to different customer group freight volumes and distribution groups, a cooperative logistics mode of a traditional warehouse and a third-party electric vehicle battery sharing warehouse is constructed, the e-commerce material distribution efficiency is improved while the e-commerce urban logistics operation cost is reduced, the e-commerce product distribution cost and time are effectively reduced, and the system is suitable for large-scale popularization and application. And the e-commerce supply chain is enabled from the dimension of urban logistics, so that the business benefit is improved.

The invention discloses a vehicle and unmanned aerial vehicle cooperative distribution path planning method based on a temporary waiting mode, and the method comprises the steps: carrying out the preprocessing of a customer set, carrying out the clustering of the customer set served by a vehicle-mounted unmanned aerial vehicle based on the capacity of a truck, obtaining a clustering center and affiliated to-be-served customers, and obtaining an initial network; modifying the initial network, constructing a spatial expansion network, splitting each clustering center into an unmanned aerial vehicle virtual outflow node and a virtual inflow node, and introducing a corresponding virtual arc; establishing a mixed integer nonlinear programming model; performing objective function and soft time window constraint linearization processing on the mixed integer nonlinear programming model, and establishing an arc-based mixed integer linear programming model; and solving to obtain an optimal distribution scheme. According to the invention, complex constraints of multi-vehicle multi-unmanned aerial vehicle cooperative distribution can be systematically considered, distribution cost minimization and time window constraint satisfaction are realized, and a scientific and efficient scheduling planning scheme is provided for urban logistics and intelligent distribution.

The invention discloses an urban logistics unmanned aerial vehicle flight path planning method based on an improved Hybrid A * algorithm, is applied to the technical field of intelligent logistics distribution, and aims to solve the problem of unmanned aerial vehicle flight path planning in urban logistics distribution. The method comprises the following steps: firstly, establishing a three-dimensional model of unmanned aerial vehicle flight path planning in an urban environment, and setting a transition grid around an obstacle to ensure that obstacle information is accurately integrated into flight path planning; secondly, the feasible moving direction of the Hybrid A * algorithm is expanded to nine directions, the planned path is considered to enable the unmanned aerial vehicle to fly forwards in a straight line as much as possible, unnecessary turning is reduced, and penalty terms of unmanned aerial vehicle nodes in all the forward moving directions are added into a cost function; and finally, fusing an improved Hybrid A * algorithm with an artificial potential field method, and in the flight process of the unmanned aerial vehicle, judging whether the current position is located in the transition grid or not in real time so as to find a safe and efficient flight route.

The invention discloses a graded unmanned aerial vehicle take-off and landing site selection method for urban low-altitude logistics demands, relates to infrastructure site selection, and aims to improve the distribution efficiency of urban logistics unmanned aerial vehicles and promote low-altitude economic development. Firstly, urban low-altitude logistics demands are evaluated, and key factors influencing the low-altitude logistics demands are captured; secondly, determining unmanned aerial vehicle take-off and landing point candidate points by applying a multi-stage facility polygon intersection point set method; thirdly, establishing a multi-target grading unmanned aerial vehicle take-off and landing point maximum coverage site selection model by maximizing the demand coverage rate, minimizing the construction cost and the demand repeated coverage rate; and finally, a QGPS-NSGA-III algorithm with improved design is adopted to solve the site selection model. According to the method, key factors influencing site selection can be accurately identified, multiple optimization targets are met to the maximum extent, the rationality and feasibility of unmanned aerial vehicle take-off and landing site selection are remarkably improved, and scientific decision support is provided for planning and implementation of an urban low-altitude logistics system.

The invention relates to a path planning method for a distribution vehicle. The method comprises the following steps: obtaining distribution information of a customer to be served; the distribution information comprises distribution time, a distribution place and a distribution quantity; obtaining a distribution transfer station according to the distribution place and the charging station; constructing a path planning constraint model according to the distribution place, the charging station site, the distribution quantity, the distribution transfer station, the distribution time, the vehicle information of the distribution motorcade and the energy consumption and carbon emission of the vehicle; and obtaining a target path planning scheme according to a niche genetic algorithm and the path planning constraint model. According to the invention, energy consumption and carbon emission of each vehicle for realizing the target path planning scheme can be reduced, and economization, efficiency and greenization of urban logistics distribution can be realized to the greatest extent.

The present invention belongs to the field of urban smart logistics technology and relates to an urban logistics distribution method that collaborates with buses and drones. The present invention is a new bus-drone collaborative distribution model suitable for non-peak traffic scenarios, and designs and constructs a two-layer decision-making framework. The upper-layer decision-making adopts the Dueling DQN algorithm that integrates a multi-head attention mechanism to reasonably match the package with the bus it should take. After the package is transported to a specific drone station by bus according to the upper-layer decision-making result, the lower-layer decision-making uses a heuristic algorithm to plan the paths of each drone. The present invention forms an efficient network of "trunk transportation + flexible terminals" by coordinating the idle transportation capacity of buses during non-peak periods with drone terminal distribution, which not only activates bus resources but also improves distribution efficiency, reducing traffic and pollution problems caused by additional vehicles in traditional solutions.

The present invention discloses a method and system for simulating an urban logistics operation environment. The method includes three steps: basic information collection, data processing, and operation environment simulation. By collecting geographical related information, customer demand related information, vehicle related information, and freight traffic management policy related information, and processing urban road network information, customer demand information, and vehicle information, the simulation of three different urban logistics operation environments, namely, deterministic type, uncertain customer demand type, and uncertain vehicle travel time type, is realized. The present invention generates many calculation examples that consider different urban logistics operation environments by comprehensively and realistically simulating the urban logistics operation environment, and can provide rich and reliable research data for optimizing urban logistics operations and scientifically formulating freight traffic management policies.

The invention discloses an unmanned aerial vehicle and unmanned vehicle collaborative operation automatic goods changing base station, and belongs to the technical field of intelligent logistics, the unmanned aerial vehicle and unmanned vehicle collaborative operation automatic goods changing base station comprises an outer frame, a roadway goods changing device is arranged in the middle of the outer frame, base station storage systems are arranged on the two sides of the roadway goods changing device respectively, and the roadway goods changing device comprises a vertical transmission mechanism, a horizontal transmission mechanism and a goods fork; the base station warehousing system comprises an unmanned aerial vehicle / unmanned vehicle goods exchange area and a base station warehouse; through the cooperation mechanism of the unmanned vehicles and the unmanned aerial vehicles, full-automatic goods changing operation between the unmanned aerial vehicles can be achieved, and the operation efficiency of the logistics system is remarkably improved; the roadway goods changing device is matched with a self-developed low-cost horizontal bidirectional pallet fork, so that high efficiency and engineering feasibility are achieved; autonomous charging of unmanned transportation equipment in the parking process is achieved in the base station goods changing area, and continuity of energy supply is guaranteed; the whole set of base station structure supports transverse cascade expansion, the scale can be flexibly adjusted according to actual application scenes, and large-scale popularization and application in urban logistics networks are facilitated.

The invention provides an ARIMA model-based urban logistics carbon emission prediction method and system, a storage medium and electronic equipment, and relates to the technical field of carbon emission prediction. According to the method, firstly, a city is divided into a plurality of non-intersecting and uniform grids, and a carbon emission data time sequence of each grid is measured and calculated based on city big data; secondly, on the basis of the city big data, extracting multivariate spatial-temporal characteristics of each grid; decomposing each carbon emission data time sequence by using EMD, and performing noise reduction processing by using TSVD to obtain a corresponding denoised carbon emission matrix; on the same time scale, combining the multivariate spatial-temporal features of all the grids with the denoised carbon emission matrix to obtain a combined feature matrix; and finally, taking the combined feature matrix as the input of an ARIMA model, and predicting an urban logistics carbon emission matrix at the next moment. And urban multi-region real-time and fine-grained logistics carbon emission prediction is realized by combining urban big data and a time sequence analysis hybrid prediction method.

The invention relates to the technical field of logistics optimization management, and provides a multi-party collaborative optimization method and system for urban logistics distribution, and the method comprises the steps: obtaining the vehicle information of various types of vehicles in an urban logistics distribution fleet and the order information of a plurality of logistics orders; the method comprises the following steps: extracting a plurality of distribution centers, network points and distribution destinations of urban logistics distribution, constructing a distribution graph structure, and obtaining a shortest distribution path for each logistics order; obtaining a plurality of order categories and the total cargo weight of each order category; obtaining a preferred transportation factor; quantizing time transportation cost; constructing a transportation objective function in combination with the optimized transportation factors; and obtaining an optimal transport vehicle type for each section of road in the shortest distribution path of each order type, thereby carrying out logistics vehicle distribution on each logistics order, and realizing multi-party collaborative optimization distribution of urban logistics. The objective of the invention is to solve the problem that a logistics distribution fleet is complex in vehicle type and cannot accurately distribute tasks.

The invention discloses a light and small unmanned aerial vehicle urban logistics route structure design and optimization method. The method comprises the following steps: designing an urban logistics route concept; based on an urban logistics route concept, constructing an initial route configuration; constructing a parameterized model representing a route structure and a link mode between route segments; according to a pre-constructed feasible rule base, screening a change range of variable structure parameters in each initial airline configuration parameterized model to form a feasible search space; constructing a route structure evaluation model based on the parameterized model; and based on the feasible search space, solving the route structure evaluation model, determining an optimal variable structure parameter, and obtaining an optimized route structure. According to the method, the problem of connection between the approach and departure routes and the air route is solved, parameterized representation of the routes is constructed, a universal route structure evaluation and optimization method is constructed, high-density approach safety of the unmanned aerial vehicle is realized, and the total delay time and the total delay cost are reduced.

The invention relates to the technical field of intelligent driving, and discloses an urban logistics vehicle intelligent driving-human driving mode switching method considering driving risks and energy consumption. Comprising the steps of driver mode switching driving data extraction, mode switching interval acquisition considering driver styles and surrounding vehicle trajectory prediction, and mode switching opportunity optimization based on driving risks and limit energy consumption. The method comprises the following steps of: obtaining a risk interval and a risk change curve of a mode switching decision by combining a style identification result and utilizing a risk evaluation model according to historical characteristic data and surrounding vehicle track prediction in a driver mode switching process, and obtaining a time interval of the mode switching decision based on the risk interval and the risk change curve; and finally, an optimization model is established based on human driving mode speed prediction, and the switching opportunity is optimized. According to the method, the driving risk and the driving energy consumption are comprehensively considered, the problems of high driving energy consumption and insufficient safety caused by unreasonable man-machine co-driving mode switching of the intelligent driving logistics vehicle in a complex urban scene are effectively solved, and the passing efficiency of the urban logistics vehicle is improved.

This invention discloses an autonomous route planning system and method for multiple unmanned aerial vehicles (UAVs) in urban low-altitude airspace, belonging to the field of intelligent route planning technology for low-altitude airspace. It includes a gridded airspace construction module for building a digital urban low-altitude airspace model, a route planning module for generating initial routes for multiple UAVs based on the gridded airspace model, and a conflict detection and resolution module to ensure global conflict-free operation of all output routes. It also includes an autonomous route planning method for multiple UAVs in urban low-altitude airspace based on the above system. Through airspace grid division, an improved three-dimensional jump point search algorithm, and a conflict resolution mechanism based on conflict search, it achieves efficient generation of UAV routes and globally conflict-free coordination, applicable to various scenarios such as urban logistics distribution, inspection, and emergency response.

This invention discloses a traffic flow allocation method for urban logistics drones, with the following specific steps: Calculating the safety impedance of each segment of the airway network based on the safety impact of the logistics drone's flight on ground personnel; calculating the efficiency impedance of each segment of the airway network considering the maximum capacity of the airways and flight energy consumption; calculating the comprehensive impedance of the urban low-altitude public airway network segments by integrating safety and efficiency impedances; establishing a dynamic traffic flow allocation model for the urban low-altitude airway network and determining the constraints for traffic flow allocation; and finally, using a staged allocation method to complete the traffic flow allocation for the public airway network. This invention employs the above-mentioned traffic flow allocation method for urban logistics drones, which can ensure efficient and safe flight of low-altitude aircraft and improve the operational efficiency of low-altitude airspace traffic flow.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle logistics distribution method and device, computer equipment and a storage medium, and the method comprises the steps: building a feature attention fusion network based on a YOLOv8 model, enhancing the foggy day image feature extraction capability through a channel and pixel attention module, and improving the target recognition precision; resolving three-dimensional coordinates of a target and dynamically correcting a flight path by combining a frequency-modulated continuous wave radar and array antenna phase difference technology; the unmanned aerial vehicle is controlled to move through an ROS framework, hovers when approaching a target and performs secondary visual verification, and safe landing is ensured. According to the method, the improved model and the multi-sensor technology are fused, the detection reliability, the positioning precision and the distribution efficiency of the unmanned aerial vehicle in foggy days, rainy days and other scenes are remarkably improved through a low-cost scheme, and the method is suitable for urban logistics and remote region transportation.

This application provides an adaptive path planning method and system for urban distribution networks based on spatiotemporal correlation prediction, relating to the field of urban logistics and distribution technology. First, it obtains a set of constraint parameters for delivery tasks and a set of basic traffic parameters for the urban road network structure. Then, it constructs an initial solution space set for delivery paths based on these parameters. Next, it analyzes the travel time dependency relationship of road segment allocation sequences, generating travel time correlation parameters and travel time impact propagation parameters. Finally, it performs structural redundancy identification and dynamic response integration processing based on these parameters to generate a path decision set responding to real-time traffic events. This invention can perceive traffic conditions in real time and dynamically optimize delivery paths, improving delivery efficiency, accuracy, and reliability, reducing delivery costs, and enhancing customer satisfaction.

The utility model discloses a three-axis tilt rotor vertical take-off and landing aircraft, and belongs to the technical field of aviation. A side canard type triangular distribution structure is adopted, the flight gravity center is arranged in a connecting line of three propellers, and the vertical lifting stability is remarkably enhanced. The unmanned aerial vehicle is provided with a unique rotor wing system, a horizontal wing system and a switching mechanism, and efficient switching between vertical flight and horizontal flight can be achieved. During flight, the rotors vertically provide lift force in the take-off stage, the inclination angles and the pitch angles of the rotors are adjusted in the conversion stage, and the horizontal wings bear main lift force during horizontal flight. A flexible engine management strategy, an efficient fixed wing take-off mode and the like are further arranged. In mountainous area rescue, urban logistics and other implementation cases, the unmanned aerial vehicle has the advantages of flexible take-off and landing, efficient flight, accurate control and the like. The aircraft can save energy consumption by 20%-40%, the takeoff taxiing distance is shortened by 30%-60%, the flight speed is increased by 15%-30%, the voyage is increased by 20%-35%, and the aircraft can be widely applied to multiple fields such as emergency rescue and cargo transportation.

The invention relates to the technical field of smart city evaluation, in particular to a smart city evaluation method and system based on artificial intelligence. The method comprises the following steps: acquiring a smart city evaluation index, acquiring a city ground environment image through a satellite image acquisition system, analyzing the city ground environment image, an appearance evaluation index and an appearance index weight coefficient, and generating an appearance characterization value of the smart city; to-be-evaluated city data of the smart city are obtained, the to-be-evaluated city data comprise city network data, city logistics data, city community data and city traffic data, the to-be-evaluated city data, the non-appearance evaluation indexes and the non-appearance index weight coefficients, and non-appearance characterization values of the smart city are generated; and determining the smart city grade corresponding to the to-be-evaluated smart city according to the appearance characterization value and the non-appearance characterization value. The smart city evaluation method and device can improve the accuracy of smart city evaluation.

The invention discloses an unmanned aerial vehicle adaptive anti-interference precise landing control system for urban logistics, and the system comprises the following modules: a fractal intention tensor body construction module which is used for constructing a fractal intention tensor body; the asymmetric self-evolution intention generation network module is used for generating an intention section sequence; an anti-causal state projector module for generating a plurality of virtual future state nodes; the back projection function construction module is used for performing inverse mapping on each virtual future state node to generate a back-stepping control path set; the disturbance guide path search module is used for sampling and variation by adopting a disturbance guide Monte Carlo path search algorithm to generate an optimal control path; the anti-interference control instruction generation module is used for outputting an anti-interference control instruction; and the flight feedback updating module is used for collecting execution feedback information of the unmanned aerial vehicle and updating state evolution distribution of the fractal intention tensor body. According to the method, tensor modeling and disturbance optimization path backstepping are fused, and anti-interference accurate landing of the unmanned aerial vehicle is achieved.

The application discloses a kind of unmanned aerial vehicle cluster cooperative path planning methods for city logistics distribution, comprising: loading environment 3D model;According to the environment 3D model and task information, initialize path planning model;Path planning model is the problem model with the minimum comprehensive cost as the solving target when unmanned aerial vehicle cluster cooperative path is solved;Comprehensive cost comprehensively height cost, angle cost, avoid obstacle cost, collision cost, communication cost and path length cost;By improving wolf swarm algorithm, the path planning model is solved, and the optimal unmanned aerial vehicle cluster cooperative path is obtained;Improved search strategy is based on population vitality value and problem dimension selection search strategy to search.The application can realize accurate and efficient logistics distribution for dense building city.