25 results about "Urban logistics" patented technology

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 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.

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 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.

The application provides a planning method and system for a simultaneous pickup and delivery vehicle routing problem with multiple time windows under uncertain pickup demand, comprising the following steps: obtaining distribution information of customer points to be served; constructing a fuzzy chance-constrained programming model for the simultaneous pickup and delivery vehicle routing problem with multiple time windows based on credibility measure theory; solving the model by using a genetic and harmony search algorithm to obtain a pre-optimized distribution scheme; scheduling a vehicle to travel according to the pre-optimized distribution scheme to determine the pickup demand of each customer; judging whether there is a failure point with a vehicle residual loading capacity less than the actual pickup demand of the customer, if there is a failure point, adopting a failure point service strategy to re-optimize the path planning for the failure point within the remaining serviceable time window; if there is no failure point, storing the determined distribution information of the customer points and outputting an optimal distribution scheme. The application makes full use of the loading capacity of the vehicle and maximizes the economicization, efficiency and greenization of urban logistics distribution.

The invention discloses a logistics end distribution post site selection method and system based on deep learning, and relates to the field of artificial intelligence, and the method comprises the steps: firstly constructing a regional topology network comprising service demand points and resource alternative points, obtaining the description parameters of all position points, and obtaining the description parameters of all the position points; wherein the service demand points comprise dense demand points which have topological links with the resource alternative points; extracting a sample position point and determining a corresponding association link; training an initial site selection decision feature extraction model and an initial feature conversion module by using description parameters on the associated link to obtain a target model and module; and finally, based on the target model and module, determining a target site selection point with the highest adaptation score by calculating the deviation degree between the site selection decision feature and the conversion feature of the dense demand point and the resource matching feature space association degree with the candidate courier site selection points. According to the method, the complex association between the location points is excavated through deep learning, the accuracy of site selection of the courier station is improved, and the method is suitable for a complex urban logistics network scene.

The invention discloses an unmanned aerial vehicle cluster collaborative path planning method for urban logistics distribution. The method comprises the following steps: loading an environment 3D model; initializing a path planning model according to the environment 3D model and the task information; the path planning model is a problem model taking an unmanned aerial vehicle cluster collaborative path as a solving target when the comprehensive cost is minimized; the comprehensive cost integrates the height cost, the angle cost, the obstacle avoidance cost, the collision cost, the communication cost and the path length cost; solving the path planning model through an improved wolf pack algorithm to obtain an optimal unmanned aerial vehicle cluster cooperation path; and the improved search strategy is used for searching based on the population vitality value and the problem dimension selection search strategy. According to the invention, accurate and efficient logistics distribution can be realized for dense building cities.

The invention relates to the technical field of logistics, in particular to an urban logistics unmanned aerial vehicle three-dimensional obstacle avoidance and path optimization method, which constructs an unmanned aerial vehicle flight path planning model fusing safety height constraint and obstacle space distribution characteristics, improves an A * algorithm, introduces a direction heuristic optimization strategy, and optimizes the three-dimensional obstacle avoidance and path optimization of an urban logistics unmanned aerial vehicle. The optimal path area can be locked more quickly by the algorithm, so that the calculation overhead is greatly reduced and the search efficiency is improved while the optimality of the solution is ensured. Experiments show that the trajectory planned by the method provided by the invention can effectively avoid static obstacles, a safe distance is kept in the whole process, the path search efficiency and the algorithm robustness are remarkably improved, and the method has excellent performance in the aspects of path length, smoothness, calculation performance and the like, and is suitable for large-scale popularization and application. And a reliable technical support is provided for three-dimensional flight path planning of the logistics unmanned aerial vehicle in an urban complex scene.

The invention discloses an urban logistics unmanned aerial vehicle navigation station network optimization method and device, and relates to the technical field of civil engineering computer algorithms. The method comprises the following steps: establishing an unmanned aerial vehicle route topology based on Delaunay triangulation; performing sample expansion calculation based on the urban travel data to obtain an outdoor exposed population model; performing unmanned aerial vehicle failure probability and head injury criterion-simple injury grading coupling operation on the outdoor exposed population model, and constructing a risk cost spatial distribution map; executing space weighted Gini coefficient operation to obtain the equality of service population distribution; constructing an energy consumption model; and constructing a Boolean mask matrix based on a gridding outdoor exposed population model, and executing a non-dominated sorting genetic algorithm under the constraint of a Boolean mask by taking a risk cost spatial distribution map, the equality of service population distribution and an energy consumption model as three targets to obtain an optimal navigation station spatial layout with a minimum mean square error. According to the invention, the performance of the urban logistics unmanned aerial vehicle navigation station network can be effectively optimized.

The invention discloses an urban low-altitude cableway logistics distribution system built based on urban street lamp poles, and relates to the technical field of urban logistics. According to the system, urban street lamp poles conforming to national technical standards are used as supports, and safe distribution of goods (including 2.5 kg of transportation bins) within 12.5 kg is achieved through a cableway system, a power transmission mechanism, a beautifying type goods carrying device and an intelligent dispatching module which are optimally designed. The system ensures that the street lamp pole has no potential safety hazard through mechanical optimization design, adopts landscape design to be integrated into the urban environment, and has a cross-intersection safe passing mechanism. The method does not relate to a cableway erection engineering technology, only protects the structural combination, safety control and landscape adaptation scheme of the system, has the advantages of high safety, good environment fusion degree, high adaptability and the like, and can be widely applied to scenes such as urban communities and commercial streets.

The invention relates to the technical field of automatic driving environment perception, in particular to a logistics scene-oriented depth enhanced sparse perception method and system, which realizes deep binding of geometric information and semantic information instead of later splicing through early-stage and multi-scale fusion of a dense depth estimation sub-network and a sparse trunk. And the network has strong geometric reasoning ability from the underlying features, and the effect of improving the non-standard target perception is obvious. The centrality and the course degree are predicted at the same time in a sparse sensing framework, and the centrality and the course degree and the classification confidence jointly form a three-dimensional quality gold triangle. The interference of a'high-resolution low-quality 'frame in a traditional method is effectively solved, and pure and reliable sensing input is provided for subsequent modules. And finally, through full-link customization design of data (category reweighting), loss (distance self-adaption) and matching strategies (quality guidance), system characteristics are highly aligned with perception requirements of an urban logistics scene, and qualitative change from a general framework to a special system is realized.

The application discloses a kind of urban logistics unmanned aircraft path planning methods, belong to unmanned aircraft path planning technical field, S1, construct city global air-ground integration risk base static three-dimensional grid model;S2, construct based on risk level matching space-time hierarchical dynamic twin model;S3, construct the coupling path planning cost function of double risk hard constraint preposition;S4, global benchmark path planning;S5, global-local linkage local dynamic obstacle avoidance and hierarchical re-planning;S6, risk check and closed-loop optimization of airworthiness traceability;The application is based on the space-time hierarchical dynamic twin modeling mechanism of risk level matching, according to the ground third-party risk level of grid, modeling accuracy and data update frequency are differentiated matching, high-precision modeling is adopted in high-risk area to ensure safety redundancy, meter-level lightweight modeling is adopted in low-risk area to reduce calculation amount, while effectively reducing the overall calculation amount of global modeling, the fine management and control of high-risk area are realized.

The invention discloses an urban logistics hub system based on digital twinning and a use method, and relates to the technical field of intelligent logistics and industrial Internet of Things, and the system comprises a dynamic data collection layer which is used for collecting multi-modal data and carrying out the data preprocessing; a digital twinning modeling layer which generates a digital twinning model based on the multi-modal data; the collaborative optimization engine layer comprises a path planning module and a reinforcement learning module, the path planning module is used for carrying out cross-modal path planning and generating an initial path, and the reinforcement learning module carries out conflict detection based on an equipment scheduling result of the initial path and selects an action to carry out strategy adjustment when a conflict is detected. Calling a path planning module to re-plan the path according to the action result; the digital twinborn model is further used for simulating the planned path to judge whether the constraint condition is met or not. According to the invention, the resource utilization rate, the dynamic response efficiency and the emergency processing capability of the logistics hub are improved, and the global collaborative decision in a multimodal transport scene is supported.

The invention discloses an underground rail transit transportation system and method, the underground rail transit transportation system comprises a passenger transport system and a freight transport system, and the passenger transport system and the freight transport system share a rail transit station; the freight transport system comprises a civil engineering supporting structure used for constructing a cargo transport space in a rail transit station space; the cargo guide rail subsystem is used for forming a cargo transportation path; and the goods transportation subsystem is used for transporting the goods from the starting position to the ending position. According to the technical scheme, on the premise that civil engineering design of an existing passenger transportation urban rail transit station is not greatly changed, traditional space can be fully utilized to efficiently achieve passenger and freight mixed use of the urban rail transit station, a good channel is provided for urban logistics transportation, and intensive, green and efficient urban physical system construction is assisted.

The application relates to the technical field of urban logistics prediction, and provides a logistics node traffic prediction method and device. The method comprises the following steps: acquiring historical characteristics corresponding to a plurality of real logistics nodes; mapping each logistics node to a plurality of preset implicit representation nodes based on the historical characteristics, wherein the number of the implicit representation nodes is smaller than that of the logistics nodes; determining the change amplitude of each implicit representation node in a future period based on the characteristics of each implicit representation node; and performing reverse mapping on the change amplitude of each implicit representation node in the future period to determine the traffic prediction result of each logistics node in the future period. The logistics node traffic prediction method provided by the application can improve the generalization, accuracy and efficiency of dynamic logistics network prediction, thereby better meeting the online deployment requirements in instant delivery, shared travel and urban logistics scheduling scenarios.

The invention relates to the technical field of logistics, in particular to an urban logistics unmanned aerial vehicle docking station site selection method, and the method comprises the steps: constructing a multi-target optimization model, determining an optimization target to minimize the sum of path distances from a logistics center to all docking stations, and minimize the total construction number of the docking stations; the method comprises the following steps of: selecting a plurality of stations, minimizing the total negative influence value of all stations on sensitive facilities such as surrounding schools, hospitals and superstores, solving the model by adopting an NSGA-II algorithm in combination with conditional constraints, verifying the model and the algorithm through a simulation data experiment, and solving the logistics unmanned aerial vehicle station site selection problem. According to the method, the limitation of a traditional single-target site selection method is overcome, the optimal balance between economic benefits and social benefits is realized on the premise of ensuring 100% demand coverage, and more scientific and comprehensive decision support is provided for logistics unmanned aerial vehicle stop station planning.

The invention discloses an unmanned aerial vehicle accurate delivery control method and system for urban logistics, and belongs to the field of unmanned aerial vehicle control, and the method specifically comprises the steps: collecting unmanned aerial vehicle data, target region data and urban environment data, building a tail end micrometeorological disturbance field and a grid map based on the urban environment data and the target region data, and carrying out the control of the unmanned aerial vehicle accurate delivery. The influence level of a target area is judged by performing obstacle detection on the grid map, the delivery permission level of the unmanned aerial vehicle is judged in combination with a tail end micrometeorological disturbance field, the delivery attitude and the delivery track of the unmanned aerial vehicle are adjusted according to different delivery permission levels, and accurate delivery control is performed; according to the invention, the accurate control capability of the real-time attitude and the delivery track of the unmanned aerial vehicle is improved, and the reliability of logistics transportation of the unmanned aerial vehicle is enhanced.

This invention discloses a path planning method for urban logistics drones, belonging to the field of drone path planning technology. The method includes: S1, constructing a static three-dimensional grid model of an integrated air-ground risk base for the entire urban area; S2, constructing a spatiotemporal hierarchical dynamic twin model based on risk level matching; S3, constructing a coupled path planning cost function with dual-risk hard constraints; S4, global baseline path planning; S5, local dynamic obstacle avoidance and hierarchical replanning with global-local linkage; and S6, airworthiness traceability risk verification and closed-loop optimization. This invention utilizes a spatiotemporal hierarchical dynamic twin modeling mechanism based on risk level matching. According to the ground-based third-party risk level of the grid, it differentiates the matching modeling accuracy and data update frequency. In high-risk areas, centimeter-level high-precision modeling is used to ensure safety redundancy, while in low-risk areas, meter-level lightweight modeling is used to reduce computational load. This effectively reduces the overall computational load of the entire area modeling while achieving refined control of all elements in high-risk areas.

The invention discloses an automatic urban logistics distribution unmanned aerial vehicle device, and relates to the field of logistics distribution unmanned aerial vehicles. The two sets of supporting leg assemblies are symmetrically distributed at the two ends of the bottom of the bottom plate, and the two sets of supporting leg assemblies are hinged to the bottom plate through connecting bases. By arranging the clamping mechanism, the logistics box can be conveniently and automatically clamped or loosened when the unmanned aerial vehicle takes off and lands, manual auxiliary material box loading and unloading are not needed, the labor intensity of workers is reduced, secondly, by arranging a buffering and damping structure formed by combining the supporting leg assemblies and the dampers, the impact force borne by the unmanned aerial vehicle when the unmanned aerial vehicle lands on the ground is reduced, and the safety of the unmanned aerial vehicle is improved. Therefore, the service life of the unmanned aerial vehicle is prolonged, and articles in the material box are prevented from being damaged by impact; when the unmanned aerial vehicle takes off, the lower ends of the supporting leg assemblies move towards the inner side, and meanwhile the fixing plates are moved to the two sides of the bottom of the logistics box to wrap the bottom of the logistics box.

This invention discloses a precise delivery control method and system for drones in urban logistics, belonging to the field of drone control. Specifically, the invention includes: collecting drone data, target area data, and urban environmental data; constructing a terminal micro-meteorological disturbance field and a grid map based on the urban environmental data and target area data; determining the impact level of the target area by obstacle detection on the grid map; determining the drone's delivery permission level based on the terminal micro-meteorological disturbance field; and adjusting the drone's delivery attitude and trajectory according to different delivery permission levels to achieve precise delivery control. This application improves the precise control capability of drone real-time attitude and delivery trajectory, enhancing the reliability of drone logistics transportation.

The invention discloses a power layout and mode conversion control method of a vertical take-off and landing flying wing unmanned aerial vehicle, and belongs to the technical field of aviation aircrafts. The unmanned aerial vehicle adopts a wing-body fused flying wing type aerodynamic layout and is provided with four leading edge power devices capable of independently controlling tilting angles and two rear fixed lift devices, a flight control system is electrically connected with the power devices, and stable switching of flight modes is realized through real-time calculation. In the vertical take-off and landing mode, the power device cooperatively provides lift force to realize vertical take-off, hovering and landing; in the forward flight mode, the power device is adjusted to high-speed cruise mainly based on the aerodynamic lift force of the flying wings; and during reverse rotation, the control system stably swings back the power device and adjusts the thrust, so that safe landing is realized. Efficient and stable conversion between vertical take-off and landing and high-speed forward flight is achieved, the multi-rotor-wing unmanned aerial vehicle has the multi-rotor-wing flexibility and the fixed-wing high-speed long-endurance capacity, and the multi-rotor-wing unmanned aerial vehicle is suitable for urban logistics, area patrol, disaster prevention monitoring, military reconnaissance and other scenes.

This invention relates to the field of logistics optimization management technology, and proposes a multi-party collaborative optimization method and system for urban logistics distribution. The method includes: acquiring vehicle information for various types of vehicles in an urban logistics distribution fleet and order information for several logistics orders; extracting several distribution centers, outlets, and delivery destinations for urban logistics distribution and constructing a distribution map structure; obtaining the shortest delivery path for each logistics order; obtaining several order categories and the total weight of goods in each order category; obtaining optimal transportation factors; quantifying time-based transportation costs; constructing a transportation objective function based on the optimal transportation factors; and obtaining the optimal vehicle type for each segment of the shortest delivery path for each order category, thereby allocating logistics vehicles to each logistics order and achieving multi-party collaborative optimization of urban logistics distribution. This invention aims to solve the problem of inaccurate task allocation due to the complexity of vehicle types in logistics distribution fleets.

The application discloses a power layout and mode conversion control method of a vertical take-off and landing flying wing unmanned aerial vehicle and belongs to the technical field of aerial vehicles. The unmanned aerial vehicle adopts a flying wing aerodynamic layout of wing-body fusion, is equipped with four leading edge power devices capable of independently controlling a tilting angle and two rear fixed lift devices, a flight control system is electrically connected with the power devices, and smooth switching of a flight mode is realized through real-time calculation. In a vertical take-off and landing mode, the power devices cooperatively provide lift, vertical take-off, hovering and landing are realized, in a forward flight mode, the power devices are adjusted to high-speed cruising mainly by the flying wing aerodynamic lift, and when reverse conversion is performed, the control system stably swings the power devices and adjusts thrust, and safe landing is realized. The application realizes efficient and smooth conversion of vertical take-off and high-speed forward flight, has the flexibility of a multi-rotor and the high-speed and long-time flight capability of a fixed wing, and is suitable for scenes such as city logistics, regional patrol, disaster prevention monitoring and military reconnaissance.

The invention discloses a community multifunctional unmanned aerial vehicle parking apron, and relates to the technical field of urban logistics distribution. Comprising an opening and closing type disc with the diameter of 4000 mm, and two semicircular parts are driven by a servo motor to achieve opening and closing; the lower square goods storage system comprises a 2800mm * 2800mm falling cabinet, and a liftable storage frame and four independent storage units are arranged in the lower square goods storage system; the laser radar identification system is characterized in that two laser radars perform cross scanning to form a 3000mm height identification area; the control system integrates an industrial computer, a PLC and various communication modules. The surface of the disc is provided with a wireless charging interface and a contact charging interface, and the top of the storage frame is provided with an electromagnetic and mechanical double-unhooking device. The system realizes full-process automation of unmanned aerial vehicle automatic identification, goods delivery, automatic unhooking, charging and user self-service pick-up, and supports 24-hour unattended operation. The daily processing capacity of a single parking apron reaches 500 or above, and the parking apron can bear 1-200 kg of goods and is suitable for various distribution scenes such as express delivery and medical supplies.