Unmanned aerial vehicle low-altitude logistics distribution path scheduling method, device, equipment and medium

Abstract

The application relates to a method, device, equipment and medium for scheduling a UAV low-altitude logistics distribution path. The method comprises the following steps: acquiring real-time obstacle distribution data in a distribution area to obtain accurate position information of the obstacle distribution; performing preliminary calculation on a UAV flight path according to the accurate position information to obtain a preliminary path planning result; acquiring wind speed information, resident distribution data and noise sensitive area information; if the wind speed of the current area exceeds a preset threshold, removing paths with a safety score lower than a preset threshold in the preliminary path planning result to obtain adjusted path segment data; and updating corresponding path segments in the preliminary path planning result according to the adjusted path segment data to obtain adjusted path planning data. The method can acquire real-time obstacle and wind speed update information, dynamically adjust the UAV flight path, and improve the distribution efficiency and safety.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) logistics and delivery, and in particular relates to methods, devices, equipment and media for scheduling UAV low-altitude logistics and delivery routes. Background Technology

[0002] Drone-based low-altitude logistics delivery, as an important innovation in modern logistics, has undeniable value in improving urban delivery efficiency and alleviating traffic congestion. This technology not only enables rapid and accurate cargo transportation but also demonstrates unparalleled flexibility in complex environments compared to traditional delivery methods, making it an indispensable part of future smart city construction.

[0003] In traditional technologies, many methods rely on static data of the delivery area for path planning and direction selection. However, in urban environments, obstacles such as buildings and trees are complex and dynamically changing, and drone flight path planning must respond to these unpredictable factors in real time.

[0004] This need for dynamic adjustments further necessitates higher requirements for flight direction selection, as direction not only affects energy consumption and flight stability but also involves finding a balance between noise control and impact on residents' lives. The complexity of direction selection is directly related to the ability to integrate real-time meteorological data; for example, changes in wind direction and speed can significantly interfere with flight stability, and failure to adjust direction in a timely manner may lead to delivery mission failure or safety hazards. Summary of the Invention

[0005] Therefore, it is necessary to provide methods, devices, equipment, and media for scheduling low-altitude logistics delivery routes using drones to address the aforementioned technical issues.

[0006] Firstly, this application provides a method for scheduling low-altitude logistics delivery routes using unmanned aerial vehicles (UAVs), including:

[0007] Obtain real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacles;

[0008] Based on precise location information, a preliminary calculation of the UAV's flight path is performed to obtain preliminary path planning results;

[0009] Obtain path impact parameters from the preliminary path planning results; path impact parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information.

[0010] Based on resident distribution data and noise-sensitive area information, the resident distribution area and noise-sensitive area are obtained;

[0011] Determine whether the natural wind speed in the target area exceeds the wind speed threshold for residential areas and noise-sensitive areas in the preliminary path planning results, and obtain the judgment result.

[0012] If the judgment result is that the wind speed in the target area exceeds the wind speed threshold of the residential area and the noise-sensitive area, the paths with safety scores lower than the preset threshold in the preliminary path planning results are removed to obtain the adjusted path segment data.

[0013] Based on the adjusted path segment data, update the corresponding path segments in the preliminary path planning results to obtain the adjusted path planning data.

[0014] Furthermore, obtaining obstacle distribution data along the delivery path to obtain precise location information of the obstacle distribution includes:

[0015] The original information of building locations and dynamic objects is obtained by scanning, resulting in a preliminary obstacle distribution dataset;

[0016] Based on the preliminary obstacle distribution dataset, a 3D model is constructed to spatially reconstruct the obstacle distribution, resulting in 3D structural data containing precise change information.

[0017] The positional changes of dynamic objects in the 3D structure data are compared with a preset threshold to obtain the comparison results.

[0018] If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained.

[0019] Based on the real-time movement trajectory, the dynamic changes in obstacle distribution are updated to obtain the latest obstacle distribution data;

[0020] Based on the latest obstacle distribution data, the position of the dynamic object at a preset time point is predicted, and the area range where the dynamic object is located at the preset time point is obtained.

[0021] Adjust the scanning frequency according to the area range, increase the data acquisition density, and obtain accurate location information of obstacle distribution.

[0022] Furthermore, if the judgment result indicates that the wind speed in the target area exceeds the preset wind speed threshold, paths with safety scores lower than the preset threshold in the preliminary path planning results are removed to obtain adjusted path segment data, including:

[0023] If the wind speed in the target area exceeds the preset wind speed threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the target area as a high-risk area, and the risk marking result is obtained.

[0024] Based on the risk labeling results, the route segments that pass through high-risk areas in the preliminary route planning results are obtained. The pre-established route optimization model is used for analysis to obtain the set of route segments that need to be adjusted.

[0025] Based on the set of path segments that need adjustment, wind direction and wind speed information are integrated, and path segments with safety scores below a preset threshold are removed to obtain the adjusted path segment data.

[0026] Furthermore, the method also includes:

[0027] Based on the adjusted path planning data, the angle adjustment value of the flight direction is calculated using vector analysis methods to obtain the final flight direction adjustment value;

[0028] By integrating the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology, real-time flight trajectory data of the UAV in a dynamic environment can be obtained.

[0029] Based on real-time flight trajectory data of drones in dynamic environments, flight time and battery consumption are obtained;

[0030] By calculating the actual flight efficiency based on flight duration and battery consumption, it can be determined whether the delivery efficiency requirements are met, and a judgment result can be obtained.

[0031] If the result indicates that the delivery efficiency requirements are not met, the flight distance is reduced according to the waypoint optimization formula to obtain the optimized flight path.

[0032] Based on the optimized flight path, the heading angle is adjusted according to the principle of maximizing ground speed to obtain the final adjusted flight planning data.

[0033] Furthermore, based on the adjusted path planning data, the step of calculating the angle adjustment value of the flight direction using vector analysis methods to obtain the final flight direction adjustment value includes:

[0034] Based on the adjusted path planning data, the angle between the wind direction and the flight direction is calculated using vector analysis to obtain the optimized flight direction parameters;

[0035] Based on the optimized flight direction parameters, combined with the acquired resident distribution data and noise-sensitive area information, it is determined whether the optimized flight direction parameters cause the noise impact to exceed the preset noise threshold, and the determination result is obtained.

[0036] If the result indicates that the noise impact exceeds the preset noise threshold, the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain the final flight direction adjustment value.

[0037] Furthermore, based on the adjusted path planning data, the angle between the wind direction and the flight direction is calculated using vector analysis to obtain optimized flight direction parameters, including:

[0038] From the adjusted path planning data, obtain the flight direction angle and wind direction angle of the flight direction;

[0039] Based on the flight direction angle and wind direction angle, the angle between the wind direction and the current flight direction is calculated using vector analysis to obtain the preliminary direction adjustment angle:

[0040] α=|(φ-θ)|

[0041]

[0042] Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the airspeed of the aircraft.

[0043] Based on the initial directional adjustment angle, the energy consumption balance and control conditions are compared with the preset threshold range to obtain the comparison results;

[0044] If the comparison result shows that the direction adjustment angle exceeds the threshold range, a restriction processing mechanism is adopted for the angle. The angle is cropped through a data filtering tool to obtain the adjustment angle range that meets the conditions.

[0045] Based on the acceptable adjustment angle range, and combined with the real-time changes in wind direction and speed data, the dynamic impact on flight direction parameters is analyzed, and optimized flight direction parameters are obtained.

[0046] Furthermore, the real-time data integration technology is used to integrate the final flight direction adjustment value and the adjusted path planning data to obtain real-time flight trajectory data of the UAV in a dynamic environment, including:

[0047] Based on the final flight direction adjustment value and the adjusted path planning data, a unified data structure is generated through real-time data integration technology to obtain a standardized flight control dataset.

[0048] Based on a standardized flight control dataset, flight commands are generated; these commands instruct the UAV to perform flight according to the flight control dataset and provide feedback on real-time flight trajectory data in a dynamic environment.

[0049] Based on real-time flight trajectory data, the system continuously monitors changes in obstacle distribution and updates wind direction and speed using airborne sensors to obtain monitoring results.

[0050] If changes in obstacle distribution or wind speed exceeding a preset safety threshold are detected, an adjustment command is generated. The adjustment command is used to activate the dynamic path adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in complex environments.

[0051] Secondly, this application also provides a drone low-altitude logistics delivery route scheduling device, including:

[0052] The data acquisition module is used to acquire real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacles.

[0053] The path planning module is used to perform preliminary calculations on the flight path of the UAV based on precise location information, and obtain preliminary path planning results.

[0054] The parameter acquisition module is used to acquire path impact parameters from the preliminary path planning results; path impact parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information;

[0055] The regional distribution acquisition module is used to obtain the residential distribution area and noise-sensitive area based on resident distribution data and noise-sensitive area information;

[0056] The judgment module is used to determine whether the natural wind speed in the target area in the preliminary path planning results exceeds the preset wind speed threshold for residential areas and noise-sensitive areas, and to obtain the judgment result.

[0057] The data update module is used to remove paths with safety scores below the preset threshold in the preliminary path planning results if the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, and obtain the adjusted path segment data.

[0058] The path update module is used to update the corresponding path segments in the preliminary path planning results based on the adjusted path segment data, so as to obtain the adjusted path planning data.

[0059] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores at least one instruction, at least one program, code, or instruction set, and the at least one instruction, at least one program, code, or instruction set is loaded and executed by the processor to implement any of the drone low-altitude logistics delivery path scheduling methods described in the embodiments of this application.

[0060] Fourthly, this application also provides a computer-readable storage medium storing at least one piece of program code, which is loaded and executed by a processor to implement the UAV low-altitude logistics delivery path scheduling method described in any of the embodiments of this application.

[0061] The aforementioned method, apparatus, equipment, and medium for scheduling low-altitude logistics delivery routes using unmanned aerial vehicles (UAVs) acquire real-time environmental information through multi-sensor data fusion. When wind speed exceeds a threshold or new obstacles are detected, path replanning is triggered. Taking into account the complex and dynamically changing distribution of obstacles such as buildings and trees in urban environments, the flight direction is adjusted promptly. Noise-sensitive areas are avoided to prevent disruption to residents' lives. This achieves adaptive path planning for UAVs in complex and dynamic environments, improving flight safety and mission completion efficiency. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart illustrating a method for scheduling low-altitude logistics delivery routes using drones in one embodiment.

[0064] Figure 2 This is a flowchart illustrating the steps for obtaining obstacle distribution data on a delivery route in one embodiment.

[0065] Figure 3 This is a schematic diagram of the structure of a drone low-altitude logistics delivery route scheduling device in one embodiment. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0067] In one embodiment, such as Figure 1 As shown, a method for scheduling low-altitude logistics delivery routes using unmanned aerial vehicles (UAVs) is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0068] Step S101: Obtain real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacle distribution.

[0069] For example, obstacle distribution data can be collected in real time from complex urban environments through sensor networks, the locations of buildings and dynamic objects can be scanned, and real-time map data of obstacle distribution can be generated using 3D modeling technology to obtain precise location information of obstacle distribution.

[0070] Step S102: Based on the precise location information, perform preliminary calculations on the UAV flight path to obtain preliminary path planning results.

[0071] For example, a path planning algorithm can be used to perform preliminary calculations of the UAV's flight path based on precise location information, yielding preliminary path planning results. A path planning algorithm is a method that uses mathematical models and logical rules to calculate a feasible path from a starting point to a destination for a mobile entity in a given environment. Mainstream path planning algorithms include genetic algorithms and A* algorithm.

[0072] For example, the A* algorithm can be used to perform preliminary calculations of the UAV's flight path to obtain preliminary path planning results. The A* algorithm is a heuristic graph search algorithm that searches for the optimal path within the obstacle distribution graph structure provided by the path planning model by combining the distance from the starting point to the current node and the estimated distance from the current node to the destination.

[0073] Step S103: Obtain the path impact parameters from the preliminary path planning results; the path impact parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information.

[0074] Among them, wind direction and speed information can be obtained by means of meteorological stations and distributed sensor networks in the logistics and distribution area, while resident distribution data and noise-sensitive area information can be obtained from urban databases.

[0075] For example, wind speed information from preliminary route planning results is collected through weather stations within the logistics and distribution area. These weather stations are equipped with high-precision anemometers, hygrometers, and other meteorological measuring instruments, enabling them to collect wind direction and speed data in real time. Resident distribution data and noise-sensitive area information are obtained from an urban database.

[0076] Step S104: Based on resident distribution data and noise-sensitive area information, obtain the resident distribution area and noise-sensitive area.

[0077] The noise-sensitive areas focus on regions with low noise tolerance that require special protection. These areas include hospitals, schools, and nursing homes. Noise tolerance threshold standards are set according to different regional regulations using a tiered wind speed threshold model; for example, 6 m / s for residential areas, 5 m / s for hospitals, 7 m / s for schools, and 4 m / s for nursing homes.

[0078] For example, the acquired resident distribution data and noise-sensitive area information are unified into a single coordinate system, and spatial clustering tools are used to aggregate discrete resident point data into continuous resident distribution areas. Spatial clustering tools are algorithms or tools used in Geographic Information Systems (GIS) and spatial data analysis to identify dense or similar areas in spatial data. Noise-sensitive area types are filtered, such as hospitals, schools, and nursing homes, and attribute information, such as sensitivity levels and permissible wind speed thresholds, is added to obtain the identified noise-sensitive areas.

[0079] Step S105: Determine whether the natural wind speed in the target area in the preliminary path planning results exceeds the preset wind speed threshold for residential areas and noise-sensitive areas, and obtain the judgment result.

[0080] For example, the natural wind speeds of residential distribution areas and noise-sensitive areas obtained in real time from the weather station are compared with their corresponding preset wind speed thresholds to determine whether the wind speed in the current area exceeds the preset wind speed thresholds, and a judgment result is obtained.

[0081] Step S106: If the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, remove the paths with safety scores lower than the preset threshold in the preliminary path planning results to obtain the adjusted path segment data.

[0082] For example, the preliminary path planning results contain multiple candidate path segments. A genetic algorithm is used to calculate the safety scores on the multiple candidate path segments. Candidate path segments with safety scores lower than a preset threshold are removed to obtain the adjusted path segment data.

[0083] Step S107: Update the corresponding path segment in the preliminary path planning result according to the adjusted path segment data to obtain the adjusted path planning data.

[0084] For example, after removing candidate path segments with safety scores below a preset threshold, the corresponding path segments in the initial path planning results are replaced with path segments with safety scores above the preset threshold to obtain adjusted path planning data.

[0085] The aforementioned method for scheduling low-altitude logistics delivery routes using drones employs a path segment-level safety optimization mechanism. This mechanism enables precise obstacle avoidance in high-risk areas while maintaining the overall path framework, thereby enhancing the drone's wind resistance and obstacle avoidance efficiency.

[0086] In one embodiment, such as Figure 2 As shown, obtaining obstacle distribution data on the delivery path to obtain precise location information of the obstacle distribution includes:

[0087] Step S201: Use scanning to obtain the original information of building locations and dynamic objects to obtain a preliminary obstacle distribution dataset.

[0088] The scanning methods include lidar, vision sensors, and inertial measurement units.

[0089] For example, an 80Hz, 120Hz, or 160Hz lidar can be selected to perform high-frequency scanning of the logistics and distribution area, which can quickly obtain the original information of building locations and dynamic objects, and obtain a preliminary obstacle distribution dataset.

[0090] Step S202: Based on the preliminary obstacle distribution dataset, construct a three-dimensional model to spatially reconstruct the obstacle distribution, and obtain three-dimensional structural data containing precise change information.

[0091] Among them, a 3D model is a digital representation of a real-world object or scene, using mathematical methods and computer technology, to transform the object into a digital representation with three dimensions: length, width, and height. It uses geometric elements such as points, lines, surfaces, and volumes, as well as attributes such as color, texture, and material, to precisely or abstractly describe the shape, structure, and spatial relationships of an object.

[0092] For example, after extracting features from the initial obstacle distribution dataset, timestamp synchronization is performed on multiple frames of high-frequency scanning data to ensure the consistency of the position of dynamic objects in consecutive frames. The building point cloud data is divided into different regions, and geometric surfaces such as planes and cylinders are fitted to each region to generate a parametric model, which intuitively presents the three-dimensional structure of the building. Based on a multi-target tracking algorithm, the dynamic object point data is associated in consecutive frames to generate motion trajectories. For the dynamic objects, a templated three-dimensional model is established, and the coordinates of the model in three-dimensional space are updated according to the real-time position and attitude to obtain three-dimensional structural data containing accurate change information.

[0093] Step S203: Compare the positional changes of dynamic objects in the three-dimensional structure data with a preset threshold to obtain the comparison result.

[0094] For example, the three-dimensional coordinates and timestamps of dynamic objects are parsed from 3D structural data, and the speed is calculated through differential computation:

[0095]

[0096] Where v is the velocity of the dynamic object, (x t ,y t ,z t Let (x, y) be the three-dimensional coordinates of the dynamic object at time t. t-1 ,y t-1 ,z t-1 Let be the three-dimensional coordinates of the dynamic object at time t-1, where t is the coordinates of the object. t t is the timestamp of the current moment. t-1 Using the timestamp of the previous moment, the obstacle displacement field is generated by calculating the timestamp difference, and the position change gradient within a 0.1s interval is recorded. Thresholds are dynamically set according to the object type. For example, the vehicle displacement threshold is set to the speed limit of 0.6m / s on urban roads, and the pedestrian displacement threshold is set to 1.5m / s to represent sudden running speed.

[0097] Step S204: If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, then the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained.

[0098] Among them, high-priority monitoring objects refer to those dynamic objects whose positional changes exceed a threshold and are marked as high-risk objects that need to be monitored with priority.

[0099] For example, once the displacement change of a dynamic object exceeds a threshold, the dynamic object is marked as an advanced monitoring object, and the real-time movement trajectory of the dynamic object in the next 0.5 seconds is obtained.

[0100] Step S205: Based on the real-time movement trajectory, update the dynamic changes in obstacle distribution to obtain the latest obstacle distribution data.

[0101] For example, the preliminary obstacle distribution dataset is updated based on the real-time movement trajectory of the dynamic object to obtain obstacle distribution data after the dynamic object moves.

[0102] Step S206: Based on the latest obstacle distribution data, predict the position of the dynamic object at a preset time point to obtain the area range where the dynamic object is located at the preset time point.

[0103] The preset time point refers to the time when the drone reaches a designated area on the predicted trajectory of the dynamic object. The position prediction of the object is accomplished by a Long Short-Term Memory (LSTM) network.

[0104] For example, based on the obstacle distribution data after the dynamic object moves, the trajectory envelope is predicted by an LSTM neural network, and the position of the dynamic object at a specified time point is predicted to obtain the range of the area where the dynamic object is located when it reaches the specified time point.

[0105] Step S207: Adjust the scanning frequency according to the area range, increase the data acquisition density, and obtain the precise location information of the obstacle distribution.

[0106] For example, the scanning frequency is adjusted according to the range of the dynamic object at a preset time point and the size of the coverage radius of the location area reached by the drone at the preset time point. For example, the scanning frequency is 50Hz within a coverage radius of 50 meters, and the collection density is increased within a radius of 10 meters. A scanning frequency of 80Hz is selected to obtain the precise location information of the obstacle distribution.

[0107] In this embodiment, a sensor network is constructed by cooperating with multiple sensors, which overcomes the physical limitations of a single sensor and predicts the real-time trajectory of dynamic objects to obtain accurate location information of obstacle distribution, thereby improving the obstacle avoidance success rate.

[0108] In one embodiment, if the determination result indicates that the wind speed in the target area exceeds the preset wind speed threshold, paths with safety scores lower than the preset threshold in the preliminary path planning results are removed to obtain adjusted path segment data, including:

[0109] Step S301: If the wind speed in the target area exceeds the preset wind speed threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the current area as a high-risk area, and the risk marking result is obtained.

[0110] For example, a graded wind speed threshold model is used to determine whether the wind speed in the current area exceeds a preset wind speed threshold. If it is detected that it exceeds the threshold, the corresponding area is marked as a high-risk area in the preliminary path data, and the risk marking result is obtained, which is then awaited for adjustment.

[0111] Step S302: Based on the risk marking results, obtain the path segments that pass through high-risk areas in the preliminary path planning results, and analyze them using a pre-established path optimization model to obtain a set of path segments that need to be adjusted.

[0112] Among them, the path optimization model is an optimization method for discretized path search using dynamic programming and mixed integer programming.

[0113] For example, the path segments that pass through high-risk areas in the preliminary path planning results are obtained. By using a pre-established path optimization model that uses dynamic programming and mixed integer programming for discretized path search, the obtained high-risk path segments are analyzed according to the constraints to obtain a set of path segments that need to be adjusted.

[0114] Step S303: Based on the set of path segments that need to be adjusted, wind direction information and wind speed information are integrated, and path segments with safety scores lower than a preset threshold are removed to obtain the adjusted path segment data.

[0115] For example, wind speed vector Decompose the tangential component W t (Parallel path) and normal component W n (Vertical path) Calculation of wind disturbance moment:

[0116]

[0117] Where ρ is the air density, C d Where A is the drag coefficient and A is the windward projected area (m²). 2 ), W n d represents the normal component of wind speed (m / s) and d represents the lever arm length (m). The safety score of the path segment is calculated based on the wind disturbance moment.

[0118]

[0119] Among them, S safe For safety rating, e -k1·MdThe exponential decay model is used, where k1 is the wind torque risk probability mapping value, fitted by a failure database. Path segments with safety scores below a preset threshold are removed, and the adjusted path segment data is obtained.

[0120] In this embodiment, by marking high-risk areas for wind speed and analyzing the safety scores of each path segment, paths below a preset threshold are eliminated, thereby improving the safety and efficiency of drone logistics delivery.

[0121] In one embodiment, the method further includes:

[0122] Step S401: Based on the adjusted path planning data, calculate the angle adjustment value of the flight direction using vector analysis to obtain the final flight direction adjustment value.

[0123] Vector analysis is a tool in mathematics and physics that uses vectors and their operations to study spatial geometry, physical field distribution, and dynamic changes.

[0124] For example, the vector analysis method is used to calculate the angle of the UAV's flight direction on the adjusted path planning data, and the flight angle is adjusted to obtain the adjusted flight direction value.

[0125] Step S402: By integrating the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology, the real-time flight trajectory data of the UAV in the dynamic environment is obtained.

[0126] Real-time data integration technology refers to a technical system that uses efficient data acquisition, transmission, processing and fusion mechanisms to seamlessly integrate multi-source heterogeneous data in a short time, usually at the millisecond to second level, to form a unified and accurate real-time dataset to support dynamic decision-making.

[0127] For example, in a dynamic flight environment scenario of an unmanned aerial vehicle (UAV), real-time data integration technology is used to fuse the flight direction adjustment value with the adjusted path planning data to generate accurate real-time flight trajectory data.

[0128] Step S403: Based on the real-time flight trajectory data of the UAV in the dynamic environment, obtain the flight time and battery consumption.

[0129] For example, in the real-time flight trajectory data of the drone, each trajectory point contains a precise timestamp. By calculating the difference between the start and end timestamps, the segmented flight time or the total flight time can be obtained. The charging and discharging current (A) of the battery is measured in real time by current sensors, such as Hall effect sensors and shunts, and the cumulative charge (Ah) is calculated by integrating over time, thereby estimating the battery consumption.

[0130] Step S404: Calculate the actual flight efficiency based on flight time and battery consumption, determine whether the delivery efficiency requirements are met, and obtain the judgment result.

[0131] For example, the actual flight efficiency is calculated using an efficiency formula based on power consumption, based on the calculated flight time and battery consumption:

[0132]

[0133] The actual flight efficiency is compared with the ideal efficiency calculated from the ideal flight range data provided by the drone manufacturer to determine whether the actual flight efficiency can meet the delivery efficiency requirements.

[0134] Step S405: If the judgment result is that the delivery efficiency requirement is not met, the flight distance is reduced according to the waypoint optimization formula to obtain the optimized flight path.

[0135] In UAV path planning, waypoint optimization formulas are typically based on mathematical algorithms, such as shortest path algorithms and heuristic algorithms, to improve efficiency by reducing flight distance.

[0136] For example, when the actual flight efficiency does not meet the delivery efficiency requirements, redundant waypoints are compressed using a waypoint optimization formula to reduce flight distance and optimize the flight path.

[0137] Step S406: Based on the optimized flight path, adjust the heading angle using the principle of maximizing ground speed to obtain the final adjusted flight planning data.

[0138] The ground speed maximization principle refers to optimizing flight efficiency by adjusting the heading angle of the UAV to achieve the maximum ground speed in a dynamic environment with wind interference.

[0139] For example, by adjusting the angle between the upwind direction of the optimized flight path and the target flight path of the UAV, the maximum ground speed can be calculated, thereby improving flight efficiency.

[0140] In this embodiment, by constructing a dynamically optimized chain, a synergistic leap in delivery efficiency and economic benefits is achieved while ensuring safety.

[0141] In one embodiment, the step of calculating the angle adjustment value of the flight direction based on the adjusted path planning data using a vector analysis method to obtain the final flight direction adjustment value includes:

[0142] Step S501: Based on the adjusted path planning data, the angle between the wind direction and the flight direction is calculated using vector analysis to obtain the optimized flight direction parameters.

[0143] For example, based on the adjusted path planning data, the airspeed of the UAV is obtained in real time through the airspeed meter, and the angle between the wind direction and the flight direction is calculated using the airspeed vector synthesis model to obtain the optimized flight direction parameters.

[0144] Step S502: Based on the optimized flight direction parameters and the acquired resident distribution data and noise-sensitive area information, determine whether the optimized flight direction parameters cause the noise impact to exceed the preset noise threshold, and obtain the judgment result.

[0145] For example, based on the optimized flight direction parameters, we can determine the aircraft's position and attitude on each segment of the path. Combining the acquired resident distribution data and noise-sensitive area information, we calculate the noise levels generated by the aircraft at each point along the path. We then determine whether the generated noise levels exceed a preset noise threshold for the current area.

[0146] Step S503: If the judgment result is that the noise impact exceeds the preset noise threshold, then change the flight direction, increase the flight altitude, or adjust the flight attitude to obtain the final flight direction adjustment value.

[0147] Changing the flight direction can reduce noise by deflecting the yaw angle, which will cause the main lobe of the propeller noise to deviate from the sensitive area; increasing the flight altitude is because the noise decreases inversely with the square of the distance as the sound intensity decreases; adjusting the flight attitude is because when the pitch angle is controlled between 5° and 8°, the propeller vortex separation point can be moved backward, reducing vortex shedding noise, which is the main source of broadband noise.

[0148] For example, when the noise of drone logistics delivery exceeds a preset threshold according to the calculated optimized flight parameters, the most suitable flight direction adjustment value can be obtained by deflecting the heading angle, increasing the flight altitude, and adjusting the flight attitude.

[0149] In this embodiment, the impact of noise is reduced by optimizing the flight direction.

[0150] In one embodiment, the step of calculating the angle between the wind direction and the flight direction using vector analysis based on the adjusted path planning data to obtain optimized flight direction parameters includes:

[0151] Step S601: Obtain the flight direction angle and wind direction angle of the flight direction from the adjusted path planning data.

[0152] The flight heading angle can be obtained directly through the navigation system, while the wind direction angle can be obtained through ground weather stations, weather radar, or satellites. It is usually expressed as "wind direction XX degrees", such as 90 degrees for east wind.

[0153] For example, the real-time position data of the aircraft is acquired through a GNSS receiver, and the flight path is calculated by combining the position changes at previous and subsequent times, thus obtaining the flight direction angle. The wind direction angle is obtained through a ground weather station.

[0154] Step S602: Based on the flight direction angle and wind direction angle, the angle between the wind direction and the current flight direction is calculated using vector analysis to obtain the preliminary direction adjustment angle.

[0155] α=|(φ-θ)|

[0156]

[0157] Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the airspeed of the aircraft.

[0158] Step S603: Based on the initial direction adjustment angle, the energy consumption balance and control conditions are compared with the preset threshold range to obtain the comparison results.

[0159] Among them, energy consumption balance and control conditions refer to the adjusted flight energy consumption not exceeding the system's allowable range, and the adjustment angle being within the range of the aircraft's hardware execution capability and dynamic response.

[0160] For example, taking into account battery capacity limitations, the calculated initial orientation adjustment angle is compared with the angle within the range of aircraft hardware execution capabilities and dynamic response to obtain the comparison results.

[0161] Step S604: If the comparison result shows that the direction adjustment angle exceeds the threshold range, then a restriction processing mechanism is adopted for the angle. The angle is cropped by a data filtering tool to obtain the adjustment angle range that meets the conditions.

[0162] The constraint processing mechanism refers to setting reasonable value ranges for the angle parameters involved in the system and ensuring, through specific algorithms, that these angles are always within a safe, valid, or physically constrained range. The data filtering tool, by setting an effective range for angles, forcibly adjusts angle values ​​exceeding that range to preset upper and lower limits to ensure that the data conforms to specific rules.

[0163] For example, if the calculated direction adjustment angle exceeds the threshold range, the upper and lower limits of the angle value need to be adjusted to be within the preset range.

[0164] Step S605: Based on the adjustment angle range that meets the conditions, and combined with the real-time changes in wind direction and wind speed data, analyze the dynamic impact on the flight direction parameters to obtain the optimized flight direction parameters.

[0165] For example, a suitable range of adjustment angles was obtained, and the changes in wind direction and wind speed data were acquired in real time through a weather station. The flight direction parameters were then adjusted in real time to obtain optimized flight direction parameters.

[0166] In this embodiment, by analyzing and obtaining a suitable directional adjustment angle, the flight direction parameters are adjusted in real time to ensure flight efficiency and safety.

[0167] In one embodiment, the real-time data integration technology is used to integrate the final flight direction adjustment value and the adjusted path planning data to obtain real-time flight trajectory data of the UAV in a dynamic environment, including:

[0168] Step S701: Based on the final flight direction adjustment value and the adjusted path planning data, a unified data structure is generated through real-time data integration technology to obtain a standardized flight control dataset.

[0169] Standardization refers to converting data from different dimensions, such as angles, coordinates, and timestamps, into a unified format that the flight control system can recognize.

[0170] For example, the final flight direction adjustment value and the adjusted path planning data are integrated in real time to unify spatial coordinates and obtain a standardized real-time flight control dataset for the flight system to execute in real time.

[0171] Step S702: Based on the standardized flight control dataset, generate flight commands. The flight commands are used to instruct the UAV to perform flight according to the flight control dataset and provide feedback on real-time flight trajectory data in the dynamic environment.

[0172] For example, after receiving a standardized flight control dataset, the UAV parser converts the standardized instructions into control signals that the UAV flight control system can recognize, schedules the execution order according to priority, and feeds back real-time flight trajectory data in the dynamic environment.

[0173] Step S703: Based on real-time flight trajectory data, continuously monitor the changes in obstacle distribution and the updated information on wind direction and speed through airborne sensors to obtain monitoring results.

[0174] Airborne sensors can be categorized into four main types based on their functions: environmental perception sensors, navigation and positioning sensors, status monitoring sensors, and mission payload sensors.

[0175] For example, based on real-time flight trajectory data, the UAV's environmental perception sensors continuously monitor the distribution of obstacles and changes in wind direction and speed to obtain updated information.

[0176] Step S704: If changes in obstacle distribution or wind speed exceeding a preset safety threshold are detected, an adjustment command is generated. The adjustment command is used to activate the path dynamic adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in complex environments.

[0177] Among them, the path dynamic adjustment mechanism refers to the entire logic and algorithm system that automatically corrects or replans the original flight path of drones or other intelligent mobile devices during the execution of tasks based on real-time perceived environmental changes, their own status or task requirements.

[0178] For example, when a change in obstacle distribution or a wind speed exceeding a preset safety or rollover threshold is detected, the drone automatically activates a dynamic path adjustment mechanism to adjust its flight trajectory in real time, choosing to avoid obstacles or high-wind-speed areas, thereby obtaining new flight trajectory data in a complex environment.

[0179] In this embodiment, real-time data integration technology is used to convert flight direction adjustment values ​​and path planning data into a unified format, reducing data processing latency and improving command execution efficiency.

[0180] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0181] Based on the same inventive concept, this application also provides a device for scheduling low-altitude logistics delivery routes for unmanned aerial vehicles (UAVs) as described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the UAV low-altitude logistics delivery route scheduling device provided below can be found in the limitations of the UAV low-altitude logistics delivery route scheduling method described above, and will not be repeated here.

[0182] In one exemplary embodiment, such as Figure 3 As shown, a drone low-altitude logistics delivery route scheduling device 300 is provided, including:

[0183] The data acquisition module 301 is used to acquire real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacle distribution.

[0184] The path planning module 302 is used to perform preliminary calculations on the flight path of the UAV based on the precise location information, and obtain preliminary path planning results.

[0185] The parameter acquisition module 303 is used to acquire path impact parameters from the preliminary path planning results; the path impact parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information.

[0186] The regional distribution acquisition module 304 is used to obtain the residential distribution area and the noise-sensitive area based on the resident distribution data and noise-sensitive area information.

[0187] The judgment module 305 is used to determine whether the natural wind speed in the target area in the preliminary path planning results exceeds the preset wind speed threshold for residential areas and noise-sensitive areas, and to obtain the judgment result.

[0188] The data update module 306 is used to remove paths with safety scores lower than the preset threshold in the preliminary path planning results if the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, and obtain the adjusted path segment data.

[0189] The path update module 307 is used to update the corresponding path segments in the preliminary path planning results based on the adjusted path segment data, so as to obtain the adjusted path planning data.

[0190] In one embodiment, the data acquisition module 301 is further configured to:

[0191] The original information of building locations and dynamic objects is obtained by scanning, resulting in a preliminary obstacle distribution dataset.

[0192] Based on the preliminary obstacle distribution dataset, a 3D model is constructed to spatially reconstruct the obstacle distribution, resulting in 3D structural data containing precise change information.

[0193] The positional changes of dynamic objects in the 3D structural data are compared with a preset threshold to obtain the comparison results.

[0194] If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained.

[0195] Based on real-time movement trajectories, the dynamic changes in obstacle distribution are updated to obtain the latest obstacle distribution data.

[0196] Based on the latest obstacle distribution data, the position of a dynamic object at a preset time point is predicted, and the area range where the dynamic object is located at the preset time point is obtained.

[0197] Adjust the scanning frequency according to the area range, increase the data acquisition density, and obtain accurate location information of obstacle distribution.

[0198] In one embodiment, the data update module 305 is further configured to:

[0199] If the current wind speed exceeds the preset threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the current area as a high-risk area, and the risk marking result is obtained.

[0200] Based on the risk labeling results, the route segments that pass through high-risk areas in the preliminary route planning results are obtained. The pre-established route optimization model is used for analysis to obtain the set of route segments that need to be adjusted.

[0201] Based on the set of path segments that need adjustment, wind direction and wind speed information are integrated, and path segments with safety scores below a preset threshold are removed to obtain the adjusted path segment data.

[0202] In one exemplary embodiment, a drone low-altitude logistics delivery route scheduling device 400 is provided, comprising:

[0203] The direction adjustment module 401 is used to calculate the angle adjustment value of the flight direction based on the adjusted path planning data through vector analysis, so as to obtain the final flight direction adjustment value.

[0204] The data integration module 402 is used to integrate the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology to obtain the real-time flight trajectory data of the UAV in a dynamic environment.

[0205] The parameter acquisition module 403 is used to acquire flight time and battery consumption based on the real-time flight trajectory data of the UAV in a dynamic environment.

[0206] The efficiency judgment module 404 is used to calculate the actual flight efficiency based on flight time and battery consumption, determine whether the delivery efficiency requirements are met, and obtain the judgment result.

[0207] The route optimization module 405 is used to reduce the flight distance according to the waypoint optimization formula to obtain an optimized flight path if the judgment result is that the delivery efficiency requirements are not met.

[0208] The heading angle adjustment module 406 is used to adjust the heading angle based on the optimized flight path and the principle of maximizing ground speed to obtain the final adjusted flight planning data.

[0209] In one embodiment, the direction adjustment module 401 is further configured to:

[0210] Based on the adjusted path planning data, the angle between the wind direction and the flight direction is calculated using vector analysis to obtain optimized flight direction parameters.

[0211] Based on the optimized flight direction parameters, combined with the acquired resident distribution data and noise-sensitive area information, it is determined whether the optimized flight direction parameters cause the noise impact to exceed the preset noise threshold, and the judgment result is obtained.

[0212] If the result indicates that the noise impact exceeds the preset noise threshold, the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain the final flight direction adjustment value.

[0213] In one embodiment, the direction adjustment module 401 is further configured to:

[0214] From the adjusted path planning data, obtain the flight direction angle and wind direction angle of the flight direction.

[0215] Based on the flight direction angle and wind direction angle, the angle between the wind direction and the current flight direction is calculated using vector analysis to obtain the preliminary direction adjustment angle:

[0216] α=|(φ-θ)|

[0217]

[0218] Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the airspeed of the aircraft.

[0219] Based on the initial directional adjustment angle, the energy consumption balance and control conditions are compared with the preset threshold range to obtain the comparison results.

[0220] If the comparison result shows that the direction adjustment angle exceeds the threshold range, a restriction processing mechanism is adopted for the angle. The angle is cropped through a data filtering tool to obtain the adjustment angle range that meets the conditions.

[0221] Based on the acceptable adjustment angle range, and combined with the real-time changes in wind direction and speed data, the dynamic impact on flight direction parameters is analyzed, and optimized flight direction parameters are obtained.

[0222] In one embodiment, the data integration module 402 is further configured to:

[0223] Based on the final flight direction adjustment value and the adjusted path planning data, a unified data structure is generated through real-time data integration technology to obtain a standardized flight control dataset.

[0224] Based on a standardized flight control dataset, flight commands are generated. These commands instruct the UAV to perform flight according to the flight control dataset and provide feedback on real-time flight trajectory data in a dynamic environment.

[0225] Based on real-time flight trajectory data, the system continuously monitors changes in obstacle distribution and updates wind direction and speed using airborne sensors to obtain monitoring results.

[0226] If changes in obstacle distribution or wind speed exceeding a preset safety threshold are detected, an adjustment command is generated. The adjustment command is used to activate the dynamic path adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in complex environments.

[0227] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the unmanned aerial vehicle (UAV) low-altitude logistics delivery route scheduling method as described above.

[0228] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0229] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0230] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for scheduling low-altitude logistics delivery routes using unmanned aerial vehicles (UAVs), characterized in that: The method includes: Obtain real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacles; Based on the precise location information, a preliminary calculation of the UAV's flight path is performed to obtain preliminary path planning results; Obtain the path impact parameters from the preliminary path planning results; the path impact parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information. Based on the resident distribution data and the noise-sensitive area information, the resident distribution area and the noise-sensitive area are obtained; Determine whether the natural wind speed in the target area in the preliminary path planning results exceeds the preset wind speed thresholds for the residential area and the noise-sensitive area, and obtain the determination result. If the judgment result is that the natural wind speed in the target area exceeds the preset wind speed threshold, the paths with a safety score lower than the preset threshold in the preliminary path planning result are removed to obtain the adjusted path segment data. Based on the adjusted path segment data, update the corresponding path segment in the preliminary path planning result to obtain the adjusted path planning data; The step of acquiring real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacle distribution includes: The original information of building locations and dynamic objects is obtained by scanning, resulting in a preliminary obstacle distribution dataset; Based on the preliminary obstacle distribution dataset, a three-dimensional model is constructed to spatially reconstruct the obstacle distribution, resulting in three-dimensional structural data containing precise change information. The positional changes of the dynamic objects in the three-dimensional structure data are compared with a preset threshold to obtain the comparison result; If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, then the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained. Based on the real-time movement trajectory, the dynamic changes in the obstacle distribution are updated to obtain the latest obstacle distribution data; Based on the latest obstacle distribution data, the position of the dynamic object at a preset time point is predicted, and the area range where the dynamic object is located at the preset time point is obtained. The scanning frequency is adjusted according to the area range to increase the data acquisition density and obtain the precise location information of the obstacle distribution; The method further includes: Based on the adjusted path planning data, the angle adjustment value of the flight direction is calculated using vector analysis to obtain the final flight direction adjustment value; By integrating the final flight direction adjustment value and the adjusted path planning data using real-time data integration technology, real-time flight trajectory data of the UAV in a dynamic environment is obtained. Based on the real-time flight trajectory data of the UAV in a dynamic environment, the flight time and battery consumption are obtained; The actual flight efficiency is calculated based on the flight time and battery consumption to determine whether the delivery efficiency requirements are met, and a judgment result is obtained. If the judgment result is that the delivery efficiency requirement is not met, the flight distance is reduced according to the waypoint optimization formula to obtain the optimized flight path; Based on the optimized flight path, the heading angle is adjusted using the principle of maximizing ground speed to obtain the final adjusted flight planning data.

2. The method according to claim 1, characterized in that, If the determination result indicates that the natural wind speed in the target area exceeds the preset wind speed threshold, paths with safety scores below the preset threshold in the preliminary path planning results are removed, resulting in adjusted path segment data, including: If the wind speed in the target area exceeds the preset wind speed threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the target area as a high-risk area, and a risk marking result is obtained. Based on the risk marking results, the path segments passing through the high-risk areas in the preliminary path planning results are obtained, and the pre-established path optimization model is used for analysis to obtain a set of path segments that need to be adjusted. Based on the set of path segments that need to be adjusted, the wind direction and wind speed information is integrated, and path segments with safety scores lower than a preset threshold are removed to obtain the adjusted path segment data.

3. The method according to claim 1, characterized in that, Based on the adjusted path planning data, the angle adjustment value of the flight direction is calculated using vector analysis methods to obtain the final flight direction adjustment value, including: Based on the adjusted path planning data, the angle between the wind direction and the flight direction is calculated using vector analysis to obtain optimized flight direction parameters; Based on the optimized flight direction parameters, and combined with the acquired resident distribution data and noise-sensitive area information, it is determined whether the optimized flight direction parameters cause the noise impact to exceed a preset noise threshold, and a determination result is obtained. If the determination result is that the noise impact exceeds the preset noise threshold, then the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain the final flight direction adjustment value.

4. The method according to claim 3, characterized in that, Based on the adjusted path planning data, the angle between the wind direction and the flight direction is calculated using vector analysis to obtain optimized flight direction parameters, including: From the adjusted path planning data, obtain the flight direction angle and wind direction angle of the flight direction; Based on the flight direction angle and wind direction angle of the stated flight direction, the angle between the wind direction and the current flight direction is calculated using vector analysis to obtain the preliminary direction adjustment angle: ； ； Where θ is the flight direction angle. α is the wind direction angle, β is the angle between the wind direction and the current flight direction, W is the wind speed, and V is the airspeed of the aircraft. Based on the initial directional adjustment angle, the energy consumption balance and control conditions are compared with the preset threshold range to obtain the comparison results; If the comparison result indicates that the initial direction adjustment angle exceeds the preset threshold range, then a restriction processing mechanism is adopted for the angle, and the angle is cropped through a data filtering tool to obtain an adjustment angle range that meets the conditions. Based on the specified range of adjustment angles that meet the conditions, and combined with the real-time changes in wind direction and wind speed data, the dynamic impact on flight direction parameters is analyzed to obtain optimized flight direction parameters.

5. The method according to claim 1, characterized in that, The process involves integrating the final flight direction adjustment value and the adjusted path planning data using real-time data integration technology to obtain real-time flight trajectory data of the UAV in a dynamic environment, including: Based on the final flight direction adjustment value and the adjusted path planning data, a unified data structure is generated through real-time data integration technology to obtain a standardized flight control dataset. Based on the standardized flight control dataset, flight commands are generated. These flight commands instruct the UAV to perform flight according to the standardized flight control dataset and provide feedback on real-time flight trajectory data in a dynamic environment. Based on the real-time flight trajectory data, the monitoring results are obtained by continuously monitoring the changes in obstacle distribution and updating the wind direction and speed through airborne sensors. If changes in the distribution of obstacles or wind direction and speed are detected to exceed a preset safety threshold range, an adjustment command is generated. The adjustment command is used to activate the dynamic path adjustment mechanism, adjust the flight trajectory in real time, and obtain real-time flight trajectory data of the UAV in a dynamic environment.

6. A drone low-altitude logistics delivery route scheduling device, characterized in that, The device includes: The data acquisition module is used to acquire real-time obstacle distribution data within the delivery area to obtain precise location information of the obstacles. The path planning module is used to perform preliminary calculations on the UAV flight path based on the precise location information to obtain preliminary path planning results; The parameter acquisition module is used to acquire path impact parameters from the preliminary path planning results; the path impact parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information. The regional distribution acquisition module is used to obtain the residential distribution area and the noise-sensitive area based on the resident distribution data and the noise-sensitive area information; The judgment module is used to determine whether the natural wind speed in the target area in the preliminary path planning result exceeds the preset wind speed threshold of the residential area and the noise-sensitive area, and to obtain the judgment result; The data update module is used to remove paths with safety scores lower than the preset threshold in the preliminary path planning results if the judgment result is that the natural wind speed in the target area exceeds the preset wind speed threshold, and to obtain the adjusted path segment data. The path update module is used to update the corresponding path segment in the preliminary path planning result according to the adjusted path segment data, so as to obtain the adjusted path planning data. The data acquisition module includes: The original information of building locations and dynamic objects is obtained by scanning, resulting in a preliminary obstacle distribution dataset; Based on the preliminary obstacle distribution dataset, a three-dimensional model is constructed to spatially reconstruct the obstacle distribution, resulting in three-dimensional structural data containing precise change information. The positional changes of the dynamic objects in the three-dimensional structure data are compared with a preset threshold to obtain the comparison result; If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, then the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained. Based on the real-time movement trajectory, the dynamic changes in the obstacle distribution are updated to obtain the latest obstacle distribution data; Based on the latest obstacle distribution data, the position of the dynamic object at a preset time point is predicted, and the area range where the dynamic object is located at the preset time point is obtained. The scanning frequency is adjusted according to the area range to increase the data acquisition density and obtain the precise location information of the obstacle distribution; The device further includes: The direction adjustment module is used to calculate the angle adjustment value of the flight direction based on the adjusted path planning data using vector analysis methods, so as to obtain the final flight direction adjustment value. The data integration module is used to integrate the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology to obtain the real-time flight trajectory data of the UAV in a dynamic environment. The parameter acquisition module is used to acquire flight time and battery consumption based on the real-time flight trajectory data of the UAV in a dynamic environment. The efficiency judgment module is used to calculate the actual flight efficiency based on the flight time and battery consumption, determine whether the delivery efficiency requirements are met, and obtain a judgment result. The route optimization module is used to reduce the flight distance according to the waypoint optimization formula to obtain an optimized flight path if the judgment result is that the delivery efficiency requirement is not met. The heading angle adjustment module is used to adjust the heading angle based on the optimized flight path and the principle of maximizing ground speed to obtain the final adjusted flight planning data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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