Precision delivery control methods and systems for drones in urban logistics

Abstract

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.

Description

Technical Field

[0001] This invention relates to a method and system for precise delivery control of unmanned aerial vehicles (UAVs) for urban logistics, belonging to the field of UAV control. Background Technology

[0002] As an emerging urban cargo delivery tool, drones have become one of the core tools for urban logistics delivery due to their advantages such as high flexibility, low deployment cost, and lack of restrictions from ground transportation. However, in the complex urban environment, the unique urban canyon effect between urban buildings can cause complex and irregular micro-meteorological disturbances. High-rise building clusters can significantly alter the complex natural wind field structure, including eddies and channel acceleration effects. On the other hand, there are also falling debris and other drones with unpredictable paths in the airspace of urban delivery points, affecting the drones' flight attitude and positioning drift.

[0003] In existing technologies, methods for delivery control of UAVs typically generate control commands by calculating the error between the desired and actual states, and use sensor noise data for fusion filtering to provide attitude, position, and velocity estimates for the UAV controller to suppress external disturbances. However, existing technologies have the following problems: when irregularly moving obstacles or sudden wind disturbances occur in the flight path, the UAV may inadvertently enter an airspace with more turbulent winds to avoid the obstacles. Attitude control to resist wind disturbances may cause the UAV to deviate from the predetermined flight path, resulting in unstable UAV attitude control and low delivery accuracy during delivery. Summary of the Invention

[0004] The purpose of this invention is to provide a precise delivery control method and system for drones in urban logistics, so as to solve the problems of unstable attitude control and low delivery accuracy of drones in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] This invention provides a method for precise delivery control of unmanned aerial vehicles (UAVs) for urban logistics, comprising:

[0007] Collect drone data, target area data, and urban environmental data. The drone data includes cargo attributes, attitude data, and positioning data. The target area data includes static obstacle data and dynamic obstacle data. The urban environmental data includes building wind field data, basic wind field, and real-time wind speed data.

[0008] Based on urban environmental data and target area data, the terminal micro-meteorological disturbance field is constructed by calculating the instantaneous three-dimensional wind field of the target area.

[0009] A grid map is constructed based on the target area data. Obstacles and occupancy status are detected on the grid map to obtain the impact level of the target area. Combined with the terminal micro-meteorological disturbance field, the delivery permission level of the UAV is determined.

[0010] According to the delivery permission level, a pre-compensation amount is set during the pre-trigger period in the deviation range. Based on the pre-compensation amount and angular rate, the delivery attitude and delivery trajectory of the UAV are adjusted at the attitude verification point to achieve precise delivery control. The deviation range is obtained by dividing the three-dimensional grid cell fluctuation period map and attitude data constructed based on the terminal micro-meteorological disturbance field. The angular rate is calculated based on the deviation range.

[0011] Specifically, based on urban environmental data combined with target area data, the terminal micro-meteorological disturbance field is constructed by calculating the instantaneous three-dimensional wind field of the target area, including:

[0012] Based on the location of the target area, a three-dimensional calculation range is preset, the three-dimensional calculation range is divided into three-dimensional grid cells and the corresponding spatial coordinates are calculated.

[0013] Based on the building wind field data, the shading coefficient and diversion coefficient of the three-dimensional grid cells are determined. The base wind field is used as the initial reference and the shading coefficient and diversion coefficient are combined to calculate the initial predicted wind field.

[0014] The instantaneous three-dimensional wind field is calculated based on real-time wind speed data, initial predicted wind field and preset mapping rule set, and the terminal micro-meteorological disturbance field is constructed.

[0015] Specifically, based on real-time wind speed data, initial predicted wind field, and a preset mapping rule set, the instantaneous three-dimensional wind field is calculated to construct the terminal micro-meteorological disturbance field, including:

[0016] A state prediction equation is constructed based on real-time wind speed data and initial predicted wind field, and the predicted wind field is calculated based on the state prediction equation.

[0017] Theoretical attitude data is calculated based on the predicted wind field and the mapping rule set. The attitude deviation is obtained based on the attitude data and the theoretical attitude data. The prediction deviation is obtained based on the initial predicted wind field and the predicted wind field.

[0018] The predicted wind field is adjusted by setting the first gain coefficient and the second gain coefficient based on the attitude deviation and the prediction deviation, and an instantaneous three-dimensional wind field is generated.

[0019] Wind field stability is set based on instantaneous three-dimensional wind field, and terminal micro-meteorological disturbance field is generated based on instantaneous three-dimensional wind field and wind field stability.

[0020] Specifically, a grid map is constructed based on target area data. Obstacles and occupancy status are detected on the grid map to obtain the impact level of the target area. Combined with the terminal micro-meteorological disturbance field, the delivery permission level of the UAV is determined, including:

[0021] Calculate the spatial coordinates of static obstacles based on static obstacle data, and assign static occupancy labels to the three-dimensional mesh cells corresponding to the spatial coordinates of static obstacles;

[0022] The initial spatial coordinates of dynamic obstacles are calculated based on dynamic obstacle data, and an intent prediction model is constructed to predict the movement trajectory of dynamic obstacles.

[0023] The occupancy probability of the corresponding 3D grid cell is calculated based on the motion trajectory. Labels are assigned to the 3D grid cells according to the occupancy probability. Empty labels are assigned to the 3D grid cells without obstacles to obtain a grid map.

[0024] The impact level of the target area is determined based on the grid map, and the delivery permit level of the UAV is determined based on the impact level and the terminal micro-meteorological disturbance field.

[0025] Specifically, the occupancy probability of the corresponding 3D mesh cell is calculated based on the motion trajectory, and labels are assigned to the 3D mesh cell according to the occupancy probability, including:

[0026] The occupancy probability of the corresponding three-dimensional mesh cell is calculated based on the motion trajectory. The time window, velocity difference and acceleration change rate are calculated based on the dynamic obstacle data. The avoidance intention and relative motion entropy of the dynamic obstacle are calculated in combination with the motion trajectory. The avoidance intention includes acceleration, deceleration and stopping.

[0027] If the three-dimensional grid cells containing static and dynamic obstacles overlap, the three-dimensional grid cells are assigned instantaneous or continuous overlapping occupation labels based on the avoidance intention of the dynamic obstacle and the relative motion entropy.

[0028] If dynamic obstacles and static obstacles are located in adjacent 3D grid cells, calculate the included angle grid cells based on the delivery trajectory tangent, avoidance intention, relative motion entropy and static obstacles, and assign blind occupancy labels to them;

[0029] If multiple dynamic obstacles are located in adjacent 3D grid cells, the 3D grid cells are assigned corresponding instantaneous overlapping occupation labels, continuous overlapping occupation labels, and pending overlapping occupation labels based on the avoidance intention and relative motion entropy.

[0030] Specifically, the impact level of the target area is determined based on a grid map, and the delivery permit level of the UAV is determined based on the impact level and the terminal micro-meteorological disturbance field, including:

[0031] Based on the proportion of various tags in the grid map, calculate the obstacle factor, set the sensitivity threshold according to the cargo attributes, and calculate the adaptation factor based on the sensitivity threshold and the obstacle factor.

[0032] The impact score is calculated based on the obstacle factor and the adaptation factor. The impact level of the three-dimensional mesh cell is determined based on the impact score. The impact level of the three-dimensional calculation range is generated based on the impact level of each three-dimensional mesh cell. The impact level is divided into no impact, first-level impact and second-level impact.

[0033] Based on the impact level and the wind field stability in the terminal micro-meteorological disturbance field, the delivery permit level is determined, which is divided into normal permit, restricted permit, and suspended permit.

[0034] Specifically, the delivery attitude and trajectory of the drone are adjusted according to the delivery permit level to achieve precise delivery control, including:

[0035] When the delivery permission level is suspended, a preset waiting window is set to re-determine the delivery permission level. If the delivery permission is suspended within the waiting window, the drone is controlled to return to the starting point. If the delivery permission is normal or restricted, the delivery attitude and delivery trajectory of the drone are adjusted.

[0036] When the delivery permission level is normal, the minimum aerodynamic interference angle of the delivery attitude is calculated based on the terminal micro-meteorological disturbance field, and precise delivery control is carried out according to the predetermined delivery trajectory.

[0037] When the delivery permit level is restricted, precise delivery control is carried out based on the terminal micro-meteorological disturbance field.

[0038] Specifically, when the delivery permit level is restricted, precise delivery control is performed based on the terminal micro-meteorological disturbance field, including:

[0039] When the delivery permit level is restricted, a delivery trajectory set is generated based on the terminal micro-meteorological disturbance field, the delivery target point, and the three-dimensional grid cell where the UAV is located. The timeliness sensitivity is set based on the delivery target point and the three-dimensional grid cell where the UAV is located.

[0040] Based on the terminal micro-meteorological disturbance field and time sensitivity, a hierarchical analysis of the delivery trajectory is performed, and the initial weight and dispersion of the initial weight of the delivery trajectory are calculated. The weight of the delivery trajectory is obtained based on the initial weight and dispersion of the delivery trajectory, and the attitude verification point of the delivery trajectory corresponding to the minimum delivery trajectory weight is set.

[0041] The wave period map is constructed based on the terminal micro-meteorological disturbance field, and the characteristics of the wave period map are generated.

[0042] The angular deviation is determined based on attitude data. According to the characteristics of the wave period spectrum, the angular deviation is divided into L deviation intervals. A deviation correlation model is constructed within the deviation intervals, and the angular rate is calculated by combining the preset wave factor.

[0043] Set a pre-compensation amount during the pre-trigger period in the deviation range, and adjust the attitude at the attitude verification point based on the pre-compensation amount and angular rate to achieve precise delivery control.

[0044] Specifically, a pre-compensation amount is set during the pre-triggering period in the deviation range. Based on the pre-compensation amount and angular rate, the attitude is adjusted at the attitude verification point to achieve precise delivery control, including:

[0045] Within the deviation range, a set of pre-trigger windows is determined based on the fluctuation cycle spectrum, and a corresponding time period is assigned to each pre-trigger window;

[0046] Within the pre-trigger window, the initial pre-compensation amount is determined based on the interval position of the angular deviation and the fluctuation factor, and a pre-compensation sequence is generated;

[0047] The pre-compensation sequence is coupled with the angular rate to obtain the attitude adjustment input.

[0048] When the attitude verification point is triggered, the attitude of the UAV is controlled based on the attitude adjustment input to align the attitude of the UAV with the target delivery trajectory and perform precise delivery control.

[0049] A drone precision delivery control system for urban logistics includes:

[0050] The data acquisition module is used to collect drone data, target area data, and urban environment data. The drone data includes cargo attributes, attitude data, and positioning data. The target area data includes static obstacle data and dynamic obstacle data. The urban environment data includes building wind field data, basic wind field, and real-time wind speed data.

[0051] The wind field sensing module is used to construct the terminal micro-meteorological disturbance field by calculating the instantaneous three-dimensional wind field of the target area based on urban environmental data and target area data.

[0052] The evaluation module is used to construct a grid map based on target area data, detect obstacles and occupancy status on the grid map, obtain the impact level of the target area, and determine the delivery permission level of the UAV by combining the terminal micro-meteorological disturbance field.

[0053] The delivery control module is used to set a pre-compensation amount during the pre-triggering period in the deviation range according to the delivery permission level, and adjust the delivery attitude and delivery trajectory of the UAV at the attitude verification point based on the pre-compensation amount and angular rate to perform precise delivery control. The deviation range is obtained by dividing the three-dimensional grid cell fluctuation period map and attitude data constructed based on the terminal micro-meteorological disturbance field, and the angular rate is calculated based on the deviation range.

[0054] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention calculates the instantaneous three-dimensional wind field of the target area to obtain the terminal micro-meteorological disturbance field, constructs a grid map of the target area, detects obstacles in the grid map, and classifies the drone's delivery permission level based on the terminal micro-meteorological disturbance field, adjusting the drone's delivery attitude and trajectory for precise delivery control; it solves the problem of insufficient perception and processing capabilities of the external wind field environment and instantaneous risk level of obstacles in existing technologies, which leads to unstable drone control and low delivery accuracy, and improves the precise control capability of the drone's real-time attitude and delivery trajectory.

[0055] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this disclosure. Attached Figure Description

[0056] Figure 1 Flowchart of the UAV precision delivery control method for urban logistics provided by the present invention;

[0057] Figure 2 A schematic diagram of the three-dimensional calculation range provided by the present invention;

[0058] Figure 3 The flowchart for UAV attitude adjustment provided by this invention;

[0059] Figure 4 This is an overall framework diagram of the drone precision delivery control system for urban logistics provided by the present invention. Detailed Implementation

[0060] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0061] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0062] Example 1

[0063] Please see Figures 1-3 This invention provides an embodiment of a precise delivery control method for drones used in urban logistics, comprising the following specific steps:

[0064] Step S1: Collect UAV data, target area data, and urban environment data. The UAV data includes cargo attributes, attitude data, and positioning data. The target area data includes static obstacle data and dynamic obstacle data. The urban environment data includes building wind field data, basic wind field, and real-time wind speed data.

[0065] It should be noted that the data collected from drones, target areas, and urban environments are preprocessed using existing technologies, including sliding window mean filtering and data noise reduction, which will not be elaborated on further here.

[0066] Step S2: Based on urban environmental data and target area data, construct the terminal micro-meteorological disturbance field by calculating the instantaneous three-dimensional wind field of the target area.

[0067] The specific steps of step S2 are as follows:

[0068] Step S201: Based on the location of the target area, a three-dimensional calculation range is preset, the three-dimensional calculation range is divided into three-dimensional mesh units, and the corresponding spatial coordinates are calculated.

[0069] In this embodiment, the three-dimensional calculation range covers the delivery target point and surrounding area in the horizontal direction, and covers the hovering height of the UAV and the height of the delivery target point in the vertical direction. The three-dimensional calculation range is divided into three-dimensional grid units in an equal proportion, and the spatial coordinates corresponding to the three-dimensional grid units are calculated.

[0070] The size of the three-dimensional calculation range shall be set by those skilled in the art according to the actual situation.

[0071] Step S202: Based on the building wind field data, determine the shading coefficient and conduction coefficient of the three-dimensional grid cell, and use the base wind field as the initial reference to calculate the initial predicted wind field by combining the shading coefficient and conduction coefficient.

[0072] In this embodiment, the degree of wind obstruction by the building is determined based on the building wind field data. The obstruction coefficient is determined by the building height and the angle between the building wall and the prevailing wind direction. The degree of wind guidance by the building is also determined based on the building wind field data. In the building canyon area, the canyon effect enhances the wind speed, and the building spacing is negatively correlated with the conduction coefficient. In the building leeward area, eddies cause the wind speed to weaken, and the building height is negatively correlated with the conduction coefficient. The basic wind field is used as the initial benchmark, and the basic wind field is corrected by the obstruction coefficient and the conduction coefficient. The result of the correction is used as the initial predicted wind field.

[0073] Step S203: Calculate the instantaneous three-dimensional wind field based on real-time wind speed data, initial predicted wind field and preset mapping rule set, and construct the terminal micro-meteorological disturbance field.

[0074] The specific steps of step S203 are as follows:

[0075] Step S2031: Construct a state prediction equation based on real-time wind speed data and initial predicted wind field, and calculate the predicted wind field based on the state prediction equation.

[0076] In this embodiment, based on real-time wind speed data and initial predicted wind field, a nonlinear state prediction equation in discrete time is constructed with wind speed and wind direction of three-dimensional grid cells as state variables. The shading coefficient and the diversion coefficient are used as inputs to calculate the wind field parameters at the next moment, and the wind field parameters are integrated into the predicted wind field.

[0077] Step S2032: Calculate theoretical attitude data based on the predicted wind field and mapping rule set, obtain attitude deviation based on attitude data and theoretical attitude data, and obtain prediction deviation based on initial predicted wind field and predicted wind field.

[0078] In this embodiment, the predicted wind field is matched with the mapping rule set, and theoretical attitude data adapted to the current predicted wind field is generated by linear interpolation. The attitude data of the UAV is used as a reference, and the difference operation is performed between the theoretical attitude data and the attitude data to obtain the attitude deviation. The attitude deviation represents the degree of deviation between the actual flight attitude of the UAV and the optimal attitude adapted to the predicted wind field. The parameters in the initial predicted wind field are compared with the parameters in the predicted wind field, and all differences are integrated to obtain the prediction deviation. For example, the wind speed deviation is obtained by comparing the wind speed in the predicted wind field and the wind speed in the initial predicted wind field.

[0079] The mapping rule set is set by those skilled in the art according to the actual situation. The mapping rule set represents the correspondence between different predicted wind field parameters and the theoretical attitude data of the UAV. For example, when the wind speed increases in the positive direction of the x-axis, the theoretical pitch angle needs to increase synchronously to offset the influence of wind field disturbance on the attitude of the fuselage.

[0080] Step S2033: Adjust the predicted wind field by setting the first gain coefficient and the second gain coefficient according to the attitude deviation and the prediction deviation, and generate the instantaneous three-dimensional wind field.

[0081] In this embodiment, a first gain coefficient is determined based on the magnitude of the attitude deviation to correct the influence of the wind field on the UAV's attitude. A second gain coefficient is determined based on the magnitude of the prediction deviation to correct the accuracy of the wind field prediction. The first gain coefficient is multiplied by the attitude deviation to obtain the attitude compensation component. When the attitude deviation causes the fuselage to tilt, this component will adjust the tilt force in the corresponding direction to balance the wind speed influence. The second gain coefficient is multiplied by the prediction deviation to obtain the wind field correction component. When the predicted wind speed is too low, this component will increase the wind speed value of the corresponding three-dimensional grid cell. The predicted wind field is adjusted based on the attitude compensation component and the wind field correction component to generate an instantaneous three-dimensional wind field.

[0082] Step S2034: Set wind field stability based on instantaneous three-dimensional wind field, and generate terminal micro-meteorological disturbance field based on instantaneous three-dimensional wind field and wind field stability.

[0083] In this embodiment, wind field stability is set based on the instantaneous three-dimensional wind field. The wind field stability includes stable, substable and unstable. The wind field stability is used to characterize the degree of irregular disturbance of the local wind field under the urban canyon effect. For example, the leeward area of ​​buildings is prone to vortex wind fields with high dispersion, and the canyon area of ​​buildings is prone to acceleration wind fields with high frequency changes. The wind field stability of each three-dimensional grid cell is correlated with the instantaneous three-dimensional wind field to obtain the terminal micro-meteorological disturbance field used to represent the local micro-meteorological irregular disturbance state.

[0084] Step S3: Construct a grid map based on the target area data, detect obstacles and occupancy status on the grid map, obtain the impact level of the target area, and determine the delivery permission level of the UAV by combining the terminal micro-meteorological disturbance field.

[0085] Step S301: Calculate the spatial coordinates of static obstacles based on the static obstacle data, and assign static occupancy labels to the three-dimensional mesh cells corresponding to the spatial coordinates of static obstacles.

[0086] In this embodiment, the static obstacle data includes, but is not limited to, the latitude and longitude information, bottom and top height, and planar dimensions of the static obstacles. The latitude and longitude information is converted into Cartesian coordinates, and the three-dimensional spatial boundary is determined by combining the bottom and top height and planar dimensions to obtain the spatial coordinates of the static obstacles. Static occupancy labels are assigned to the three-dimensional mesh units corresponding to the spatial coordinates of the static obstacles. By assigning labels, the scattered static obstacles are transformed into obstacle area markers in the three-dimensional mesh map, eliminating the space occupied by static obstacles and preventing collision risks caused by misjudging static obstacle areas when the drone is deployed.

[0087] Step S302: Calculate the initial spatial coordinates of the dynamic obstacle based on the dynamic obstacle data, construct an intent prediction model, and predict the motion trajectory of the dynamic obstacle.

[0088] In this embodiment, the dynamic obstacle data includes, but is not limited to, the bottom and top heights, planar dimensions, movement height, initial movement state, stability coefficient, and latitude and longitude information of the dynamic obstacle. The latitude and longitude information is converted into planar rectangular coordinates. The three-dimensional spatial boundary is determined by combining the bottom and top heights, planar dimensions, and movement height. The initial spatial coordinates of the three-dimensional grid cell occupied by the dynamic obstacle are determined based on the three-dimensional spatial boundary. The initial spatial coordinates and initial movement state are used as input features. An intent prediction model is constructed using a long short-term memory network combined with a logistic regression algorithm to predict future movement parameters. The movement trajectory of the dynamic obstacle is determined by combining the spatial coordinates of neighboring three-dimensional grid cells. By predicting the movement trajectory of the dynamic obstacle, the potential occupancy risk of the three-dimensional grid cell can be marked, preventing the UAV from deviating from the delivery trajectory or colliding due to failure to predict the position of the dynamic obstacle.

[0089] Step S303: Calculate the occupancy probability of the corresponding three-dimensional grid cell based on the motion trajectory, assign labels to the three-dimensional grid cell according to the occupancy probability, assign empty labels to the three-dimensional grid cell without obstacles, and obtain the grid map.

[0090] The specific steps of step S303 are as follows:

[0091] Step S3031: Calculate the occupancy probability of the corresponding three-dimensional mesh cell based on the motion trajectory, calculate the time window, velocity difference and acceleration change rate based on the dynamic obstacle data, and calculate the avoidance intention and relative motion entropy of the dynamic obstacle in combination with the motion trajectory. The avoidance intention includes acceleration, deceleration and stopping.

[0092] In this embodiment, the spatial overlap is calculated based on the motion trajectory, planar dimensions, and three-dimensional grid cell dimensions of the dynamic obstacle. The occupancy probability is obtained by multiplying the dwell time of the dynamic obstacle in the three-dimensional grid cell, the spatial overlap, and the stability coefficient. The time when the dynamic obstacle enters the three-dimensional grid cell and the time when it leaves the cell are used as time windows to represent the period when the three-dimensional grid cell has a dynamic occupancy risk. The velocity difference is obtained based on the vector difference between the current velocity and the previous velocity of the dynamic obstacle. The acceleration change rate is obtained by dividing the difference between the current acceleration and the previous acceleration of the dynamic obstacle by the time interval.

[0093] If the speed difference and acceleration change rate are positive, the avoidance intention is to accelerate, meaning to quickly leave the area by increasing speed. If the speed difference and acceleration change rate are negative, the avoidance intention is to decelerate, meaning to allow more time for avoidance decision by reducing speed. If the speed difference and acceleration change rate are less than a preset speed threshold, the avoidance intention is to stop, meaning to maintain the current position. The relative motion entropy of the dynamic obstacle is calculated using the information entropy formula based on the speed fluctuation value, acceleration fluctuation value, and trajectory offset of the dynamic obstacle. The higher the entropy value, the more unstable the motion state of the dynamic obstacle. The speed threshold is set by those skilled in the art according to the actual situation.

[0094] Step S3032: If the three-dimensional grid cells where static obstacles and dynamic obstacles are located overlap, assign instantaneous or continuous overlap occupancy labels to the three-dimensional grid cells based on the avoidance intention of the dynamic obstacles and the relative motion entropy.

[0095] In this embodiment, the spatial coordinates of the three-dimensional grid cells covered by the dynamic obstacle's trajectory are compared with the spatial coordinates of the three-dimensional grid cells where the static obstacle is located. Three-dimensional grid cells with the same spatial coordinates are determined to be overlapping. If the avoidance intention is to accelerate and the relative motion entropy is large, an instantaneous overlapping occupation label is assigned to the three-dimensional grid cell. If the avoidance intention is to decelerate or stop and the relative motion entropy is small, a continuous overlapping occupation label is assigned to the three-dimensional grid cell.

[0096] Step S3033: If the dynamic obstacle and the static obstacle are adjacent to each other in the three-dimensional mesh cell, calculate the included angle mesh cell based on the delivery trajectory tangent, avoidance intention, relative motion entropy and static obstacle, and assign blind occupancy labels to it.

[0097] In this embodiment, the spatial distance is calculated by subtracting the spatial coordinates of the three-dimensional grid cells covered by the dynamic obstacle's trajectory and the spatial coordinates of the three-dimensional grid cells where the static obstacle is located. When the spatial distance is the same as the side length of the three-dimensional grid cell, it is determined to be adjacent. Based on the delivery trajectory, a delivery trajectory curve is generated by polynomial fitting. The first derivative of the delivery trajectory curve is calculated to obtain the tangent direction vector. The motion direction vector is determined according to the avoidance intention of the dynamic obstacle and the relative motion entropy. The blocking direction vector is set according to the spatial coordinates of the static obstacle. The first angle is obtained by the tangent direction vector and the motion direction vector. The second angle is obtained by the blocking direction vector and the motion direction vector. The first angle and the second angle are extended into an angle interval by the relative motion entropy. Their intersection is taken as the effective angle range. The three-dimensional grid cells covered by the effective angle range are taken as angle grid cells, and blind occupancy tags are assigned to them.

[0098] Step S3034: If multiple dynamic obstacles are located in adjacent 3D grid cells, assign corresponding instantaneous overlapping occupation labels, continuous overlapping occupation labels, and pending overlapping occupation labels to the 3D grid cells based on the avoidance intention and relative motion entropy.

[0099] In this embodiment, the spatial distance is calculated by subtracting the spatial coordinates of the three-dimensional grid cells covered by the motion trajectories of each dynamic obstacle. When the spatial distance is the same as the side length of the three-dimensional grid cell, it is determined to be adjacent, and the three-dimensional grid cell is assigned a tag to be occupied. When the spatial coordinates of the three-dimensional grid cells covered by the motion trajectories of the dynamic obstacles are the same, they are determined to be overlapping. Based on the avoidance intention and relative motion entropy of each dynamic obstacle, if the avoidance intention of the dynamic obstacle is to accelerate and the relative motion entropy is high, the three-dimensional grid cell is assigned a tag to be occupied instantaneously. If the avoidance intention of the dynamic obstacle is to decelerate and the relative motion entropy is low, or if the avoidance intention of at least one dynamic obstacle is to stop, the three-dimensional grid cell is assigned a tag to be occupied continuously.

[0100] Step S304: Determine the impact level of the target area based on the grid map, and determine the delivery permission level of the UAV based on the impact level and the terminal micro-meteorological disturbance field.

[0101] The specific steps of step S304 are as follows:

[0102] Step S3041: Calculate the obstacle factor based on the proportion of each type of label in the grid map, set the sensitivity threshold based on the cargo attributes, and calculate the adaptation factor based on the sensitivity threshold and the obstacle factor.

[0103] In this embodiment, the number of various labels in the grid map is counted, and the weights of various labels are set. An initial obstacle factor is obtained by weighted summation based on the number of labels and the label weights. The quotient of the initial obstacle factor and the number of three-dimensional grid cells is used as the obstacle factor. The obstacle factor reflects the overall density of obstacle risk in the grid map. The magnitude of the obstacle factor is positively correlated with the impact of delivery. Sensitivity is set according to the cargo attributes. The sensitivity includes high sensitivity, medium sensitivity, and low sensitivity. A sensitivity threshold is set according to the sensitivity type. An adaptation factor is calculated based on the sensitivity threshold and the obstacle factor. When the obstacle factor is less than the sensitivity threshold, the obstacle factor and the adaptation factor are negatively correlated. When the obstacle factor is greater than the sensitivity threshold, the obstacle factor and the adaptation factor are positively correlated.

[0104] Step S3042: Calculate the impact score based on the obstacle factor and the adaptation factor, determine the impact level of the three-dimensional mesh cell according to the impact score, and generate the impact level of the three-dimensional calculation range according to the impact level of each three-dimensional mesh cell. The impact level is divided into no impact, first-level impact and second-level impact.

[0105] In this embodiment, the influence score is calculated by weighted summation based on the obstacle factor and the fit factor. The influence level of the three-dimensional grid cell is determined according to the influence score. The influence level of the three-dimensional grid cell is generated according to the distribution ratio of the influence level of the three-dimensional grid cell within the three-dimensional calculation range and the preset judgment rules. When the influence level is no influence, it means that there are few obstacles and the fit of goods is high in the three-dimensional grid cell. The influence level is Level 1 influence, which means that there are slight obstacles or medium fit in the three-dimensional grid cell. The influence level is Level 2 influence, which means that there are dense obstacles or low fit in the three-dimensional grid cell.

[0106] The judgment rules are set by those skilled in the art based on the actual situation.

[0107] Step S3043: Based on the impact level and the wind field stability in the terminal micro-meteorological disturbance field, determine the delivery permit level, which is divided into normal permit, restricted permit and suspended permit.

[0108] In this embodiment, if the impact level is no impact and the wind field stability in the terminal micro-meteorological disturbance field is stable, the delivery permit level is determined to be normal permit. If the impact level is Level 1 impact or the wind field stability in the terminal micro-meteorological disturbance field is sub-stable, the delivery permit level is determined to be restricted permit. If the impact level is Level 2 impact or the wind field stability in the terminal micro-meteorological disturbance field is unstable, the delivery permit level is determined to be suspended permit.

[0109] exist Figure 2 In the diagram, dashed rectangles represent the 3D calculation range, warning graphics represent any type of obstacle, either static or dynamic, ellipses represent delivery target points, drone graphics represent drones, solid lines represent the delivery trajectory with the lowest weight, dashed lines represent other delivery trajectories, and wind field graphics represent the wind field within the 3D calculation range.

[0110] Step S4: Set a pre-compensation amount during the pre-triggering period in the deviation range according to the delivery permission level. Adjust the delivery attitude and delivery trajectory of the UAV at the attitude verification point based on the pre-compensation amount and angular rate to perform precise delivery control. The deviation range is obtained by dividing the three-dimensional grid cell fluctuation period map and attitude data constructed based on the terminal micro-meteorological disturbance field. The angular rate is calculated based on the deviation range.

[0111] Step S401: When the delivery permission level is suspended, a preset waiting window is set to re-determine the delivery permission level. If the delivery permission is suspended within the waiting window, the drone is controlled to return to the starting point. If the delivery permission is normal or restricted, the delivery attitude and delivery trajectory of the drone are adjusted.

[0112] In this embodiment, when the delivery permission level is suspended, a waiting window is preset. During the waiting window, the UAV is controlled to enter a low-speed hovering state, which reduces the energy consumption rate of the UAV and keeps the fuselage relatively stable during periodic disturbances. The current delivery permission level is re-evaluated. If it is still suspended, the UAV is controlled to return to the starting point. If it is normal or restricted, it indicates that the wind field stability in the terminal micro-meteorological disturbance field has increased and the impact level has decreased. The current delivery attitude and delivery trajectory of the UAV are adjusted before delivery.

[0113] The waiting window is set by those skilled in the art through simulation experiments or according to actual conditions.

[0114] Step S402: When the delivery permission level is normal, calculate the minimum aerodynamic interference angle of the delivery attitude based on the terminal micro-meteorological disturbance field, and perform precise delivery control according to the predetermined delivery trajectory.

[0115] In this embodiment, when the delivery permission level is normal, the prevailing wind direction and wind speed are calculated based on the terminal micro-meteorological disturbance field and the wind field stability is stable. The minimum aerodynamic interference angle is determined according to the prevailing wind direction, wind speed and the angle of the UAV rotor rotation plane, so that the disturbance torque of the stable wind field on the UAV fuselage is naturally canceled out by the small difference in rotor speed, without the need for additional trajectory correction.

[0116] Step S403: When the delivery permit level is restricted, precise delivery control is performed based on the terminal micro-meteorological disturbance field.

[0117] The specific steps of step S403 are as follows:

[0118] Step S4031: When the delivery permission level is restricted, a delivery trajectory set is generated based on the terminal micro-meteorological disturbance field, the delivery target point and the three-dimensional grid cell where the UAV is located, and the time sensitivity is set based on the delivery target point and the three-dimensional grid cell where the UAV is located.

[0119] In this embodiment, the three-dimensional grid cell of the delivery target point is taken as the endpoint cell, and the three-dimensional grid cell where the UAV is located is taken as the starting cell. Based on the terminal micro-meteorological disturbance field, three-dimensional grid cells are selected sequentially from different directions starting from the starting cell until the endpoint cell to generate a delivery trajectory set. The spatial distance is calculated based on the spatial coordinates of the delivery target point and the three-dimensional grid cell where the UAV is located. The time sensitivity is set according to the spatial distance and cargo attributes. The magnitude of the time sensitivity is positively correlated with the time requirement.

[0120] Step S4032: Perform hierarchical analysis on the delivery trajectory based on the terminal micro-meteorological disturbance field and time sensitivity, calculate the initial weight and dispersion of the delivery trajectory, obtain the weight of the delivery trajectory based on the initial weight and dispersion, and set the attitude verification point of the delivery trajectory corresponding to the minimum delivery trajectory weight.

[0121] In this embodiment, the delivery trajectory is compared and traversed based on the terminal micro-meteorological disturbance field and timeliness sensitivity. An evaluation matrix is ​​constructed according to the importance of the terminal micro-meteorological disturbance field and timeliness sensitivity on the delivery trajectory. The evaluation vector is obtained by calculating the evaluation matrix using the square root method. Each component of the evaluation vector is matched with the delivery trajectory to obtain the initial weight of the delivery trajectory. The average value of the initial weights is calculated based on the initial weights of the delivery trajectories, and the sum of squared deviations of each initial weight from the average value is calculated. The sum of all sums of squared deviations is then calculated, and its quotient with the number of delivery trajectories is obtained to obtain the initial dispersion. The initial dispersion is then square-rooted to obtain the dispersion. The dispersion reflects the distribution difference of the initial weights; a larger value indicates a more significant difference in the overall performance of different trajectories. The initial weights of the delivery trajectories are adjusted using the dispersion to obtain the weights of the delivery trajectories.

[0122] Select the delivery trajectory with the smallest weight, and set attitude verification points on the delivery trajectory. The number of attitude verification points is set according to the length of the delivery trajectory. The actual attitude of the UAV is adjusted at the attitude verification points to prevent attitude loss of control. It should be noted that the smaller the weight of the delivery trajectory, the better the delivery trajectory.

[0123] Step S4033: Construct a three-dimensional grid cell wave period map based on the terminal micro-meteorological disturbance field, and generate the characteristics of the wave period map.

[0124] In this embodiment, wavelet transform is used to decompose the wave components based on the terminal micro-meteorological disturbance field, and sinusoidal wave characteristics and intermittent turbulence are distinguished. The wave period, real-time phase and amplitude of the sinusoidal wave characteristics are extracted, and the wave period phase difference is calculated. The wave period phase difference reflects the synchronicity of the sinusoidal wave of the wind field in adjacent three-dimensional grid cells. The turbulent wave frequency and force field vector of the intermittent turbulence are extracted. The wave period spectrum is generated based on the wave period phase difference, turbulent wave frequency and force field vector.

[0125] Step S4034: Determine the angular deviation based on the attitude data, divide the angular deviation into L deviation intervals according to the characteristics of the fluctuation period spectrum, construct a deviation correlation model within the deviation intervals, and calculate the angular rate by combining the preset fluctuation factor.

[0126] In this embodiment, spatial comparison is performed based on attitude data and a preset target attitude angle to determine the consistency of attitude angle deviation in the three-dimensional spatial unit. The angle deviation is the degree to which the overall attitude deviates in that direction. According to the characteristics of the wave period spectrum, the angle deviation is divided into L deviation intervals. If the turbulent wave frequency is frequent, the force field vector magnitude and the phase difference of the sinusoidal wave are large, then L increases. If the turbulent wave frequency is gentle, the force field vector magnitude and the phase difference of the sinusoidal wave are small, then L decreases.

[0127] Using the turbulent wave frequency and force field vector as inputs and the angular rate adjustment coefficient as output, the basic correlation is obtained through data fitting. The basic correlation is then optimized by the correction coefficient determined by the sinusoidal wave phase difference to generate a deviation correlation model. Based on the preset wave factor, the angular rate adjustment coefficient output by the deviation correlation model is multiplied by the wave factor to obtain the angular rate.

[0128] The fluctuation factor is set by those skilled in the art through simulation experiments or according to actual conditions.

[0129] Step S4035: Set the pre-compensation amount during the pre-trigger period in the deviation range, and adjust the attitude at the attitude verification point based on the pre-compensation amount and angular rate to perform precise delivery control.

[0130] The specific steps of step S4035 are as follows:

[0131] Step S40351: Within the deviation interval, determine the set of pre-trigger windows based on the fluctuation period spectrum, and assign a corresponding time period to each pre-trigger window.

[0132] In this embodiment, periodic features are extracted based on the wave period map, including the estimated occurrence interval of the main period of sinusoidal wave and intermittent turbulence. Pre-trigger windows are marked within the deviation interval according to the estimated occurrence interval of the main period of sinusoidal wave and intermittent turbulence. At the same time, a specific time period is assigned to each pre-trigger window. The moment when the UAV enters the current three-dimensional grid cell is used as the starting reference. Combined with the wave peak time in the wave period map, the start and end times of each window are determined to generate a set of pre-trigger windows. By setting pre-trigger windows, pre-compensation amount is injected into the pre-trigger windows before wind disturbance actually affects the attitude of the UAV, thereby suppressing the angular deviation caused by wind disturbance.

[0133] Step S40352: Within the pre-trigger window, determine the initial pre-compensation amount based on the interval position of the angular deviation and the fluctuation factor, and generate a pre-compensation sequence.

[0134] In this embodiment, the pre-compensation amount is set according to the interval position of the angular deviation. If the angular deviation is in the upper half of the deviation interval, it indicates that the current attitude deviates from the target trajectory to a high degree, and the pre-compensation amount is increased accordingly. If it is in the lower half of the interval, it indicates that the current attitude deviates from the target trajectory to a low degree, and the pre-compensation amount is decreased accordingly. The pre-trigger window is divided into multiple consecutive sub-time periods. The pre-compensation amount is adjusted based on the fluctuation factor and the wind field stability of each sub-time period. For example, in a sub-time period with a stable wind field, the pre-compensation amount of the current sub-time period decreases according to the previous sub-time period. In a sub-time period with an unstable wind field, the pre-compensation amount of the current sub-time period increases according to the previous sub-time period. The pre-compensation amounts corresponding to each sub-time period are arranged sequentially to generate a pre-compensation sequence. By generating the pre-compensation sequence by time period, the pre-compensation amount is dynamically adapted to the real-time wind field status.

[0135] Step S40353: Couple the pre-compensation sequence with the angular rate to obtain the attitude adjustment input.

[0136] In this embodiment, the angular rate and the pre-compensation amount of the pre-compensation sequence are time-matched based on the sub-time period. If the time required to calculate the attitude adjustment amplitude corresponding to the pre-compensation amount according to the current angular rate is the same as the length of the sub-time period, then the pre-compensation amount is bound to the angular rate. If the time required to calculate the attitude adjustment amplitude corresponding to the pre-compensation amount is less than the sub-time period, then the angular rate of the sub-time period is reduced to ensure that the adjustment process covers the sub-time period. If the time required to calculate the attitude adjustment amplitude corresponding to the pre-compensation amount is greater than the sub-time period, then the angular rate of the sub-time period is increased. The pre-compensation amount and angular rate of each sub-time period are coupled into a set of attitude adjustment parameters to generate a complete attitude adjustment input.

[0137] Step S40354: When the attitude verification point is triggered, the attitude of the UAV is controlled based on the attitude adjustment input to align the attitude of the UAV with the target delivery trajectory and perform precise delivery control.

[0138] In this embodiment, when the UAV reaches the attitude verification point in the three-dimensional grid cell, attitude control of the UAV is performed. The attitude adjustment input is decomposed into specific control components for the three axes of UAV tilt, pitch and yaw. The specific control components are associated with the target delivery trajectory, and attitude control commands are applied to the UAV through the specific control components to adjust the attitude angle, so as to ensure that the UAV attitude is aligned with the target trajectory and to perform precise delivery control.

[0139] exist Figure 3In this process, the delivery permission level is used as input. If the delivery permission level is suspended, the UAV enters a waiting window. If the delivery permission level improves within the waiting window, delivery continues; otherwise, a return command is executed. If the delivery permission level is normal, the minimum aerodynamic interference angle that can offset the interference of the stable wind field is calculated, and the UAV is controlled to perform precise delivery control according to the original delivery trajectory. If the delivery permission level is restricted, a set of delivery trajectories is generated, and the delivery trajectory with the minimum weight is calculated based on hierarchical analysis. The pre-compensation amount and angular rate are calculated based on the fluctuation period spectrum. When the UAV flies to the attitude verification point, a control command is triggered, coupling the pre-compensation amount and the real-time angular rate to adjust the UAV attitude for precise delivery control.

[0140] Example 2

[0141] Please see Figure 4 One embodiment of the present invention is a drone precision delivery control system for urban logistics, comprising a data acquisition module, a wind field perception module, an evaluation module, and a delivery control module.

[0142] The data acquisition module is used to collect drone data, target area data, and urban environment data. The drone data includes cargo attributes, attitude data, and positioning data. The target area data includes static obstacle data and dynamic obstacle data. The urban environment data includes building wind field data, basic wind field, and real-time wind speed data.

[0143] The wind field sensing module is used to construct the terminal micro-meteorological disturbance field by calculating the instantaneous three-dimensional wind field of the target area based on urban environmental data and target area data.

[0144] The evaluation module is used to construct a grid map based on target area data, detect obstacles and occupancy status on the grid map, obtain the impact level of the target area, and determine the delivery permission level of the UAV by combining the terminal micro-meteorological disturbance field.

[0145] The delivery control module is used to set a pre-compensation amount during the pre-triggering period in the deviation range according to the delivery permission level, and adjust the delivery attitude and delivery trajectory of the UAV at the attitude verification point based on the pre-compensation amount and angular rate to perform precise delivery control. The deviation range is obtained by dividing the three-dimensional grid cell fluctuation period map and attitude data constructed based on the terminal micro-meteorological disturbance field, and the angular rate is calculated based on the deviation range.

[0146] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0147] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of the present invention without departing from the spirit and scope of the present invention, and all of these modifications are within the scope of protection of the present invention.

Claims

1. A method for controlling precise delivery of a UAV for urban logistics, characterized in that, The method comprises the following steps: Collecting unmanned aerial vehicle data, target area data and urban environment data, wherein the unmanned aerial vehicle data comprises cargo attributes, attitude data and positioning data, the target area data comprises static obstacle data and dynamic obstacle data, and the urban environment data comprises building wind field data, basic wind field and real-time wind speed data; Based on the urban environment data and the target area data, the instantaneous three-dimensional wind field of the target area is calculated to construct the end micro-meteorological disturbance field; Based on the target area data, a grid map is constructed, and the grid map is detected for obstacles and occupancy states to obtain the influence level of the target area, and the delivery permission level of the unmanned aerial vehicle is determined in combination with the end micro-meteorological disturbance field; According to the pre-compensation amount set in the pre-trigger period of the deviation interval based on the delivery permission level, the delivery attitude and trajectory of the unmanned aerial vehicle are adjusted at the attitude check point based on the pre-compensation amount and the angular velocity to perform accurate delivery control, wherein the deviation interval is divided based on the three-dimensional grid element fluctuation period atlas constructed by the end micro-meteorological disturbance field and the attitude data, and the angular velocity is calculated based on the deviation interval; The method for constructing the end micro-meteorological disturbance field based on the urban environment data and the target area data by calculating the instantaneous three-dimensional wind field of the target area comprises the following steps: A three-dimensional calculation range is preset based on the position of the target area, the three-dimensional calculation range is divided into three-dimensional grid elements, and the corresponding space coordinates are calculated; According to the building wind field data, the shielding coefficient and the flow guiding coefficient of the three-dimensional grid element are determined, the basic wind field is calculated as an initial reference in combination with the shielding coefficient and the flow guiding coefficient to obtain an initial predicted wind field; Based on the real-time wind speed data, the initial predicted wind field and a preset mapping rule set, the instantaneous three-dimensional wind field is calculated to construct the end micro-meteorological disturbance field; The method for constructing the end micro-meteorological disturbance field based on the real-time wind speed data, the initial predicted wind field and the preset mapping rule set comprises the following steps: A state prediction equation is constructed based on the real-time wind speed data and the initial predicted wind field, and the predicted wind field is calculated according to the state prediction equation; Theoretical attitude data are calculated based on the predicted wind field and the mapping rule set, attitude deviation is obtained according to the attitude data and the theoretical attitude data, and prediction deviation is obtained according to the initial predicted wind field and the predicted wind field; The first gain coefficient and the second gain coefficient are set according to the attitude deviation and the prediction deviation to adjust the predicted wind field, and the instantaneous three-dimensional wind field is generated; The wind field stability is set based on the instantaneous three-dimensional wind field, and the end micro-meteorological disturbance field is generated based on the instantaneous three-dimensional wind field and the wind field stability; The method for constructing the grid map based on the target area data, detecting the grid map for obstacles and occupancy states to obtain the influence level of the target area, and determining the delivery permission level of the unmanned aerial vehicle in combination with the end micro-meteorological disturbance field comprises the following steps: The static obstacle space coordinates are calculated based on the static obstacle data, and the static obstacle space coordinates corresponding three-dimensional grid elements are assigned with static occupancy labels; The initial space coordinates of the dynamic obstacle are calculated based on the dynamic obstacle data, an intention prediction model is constructed, and the motion trajectory of the dynamic obstacle is predicted. The occupancy probability of the corresponding three-dimensional grid cell is calculated based on the motion trajectory, and the three-dimensional grid cell is labeled according to the occupancy probability, and the three-dimensional grid cell without obstacles is labeled as an empty label to obtain a grid map; The influence level of the target area is determined based on the grid map, and the delivery permission level of the unmanned aerial vehicle is determined based on the influence level and the end micro-meteorological disturbance field. 2.The urban logistics oriented UAV precision delivery control method of claim 1, wherein, The occupancy probability of the corresponding three-dimensional grid cell is calculated based on the motion trajectory, and the three-dimensional grid cell is labeled according to the occupancy probability, including: The occupancy probability of the corresponding three-dimensional grid cell is calculated based on the motion trajectory, the time window, the speed difference and the acceleration change rate are calculated based on the dynamic obstacle data, the avoidance intention and the relative motion entropy of the dynamic obstacle are calculated in combination with the motion trajectory, and the avoidance intention includes acceleration, deceleration and stop; If the three-dimensional grid cells where the static obstacles and the dynamic obstacles are located coincide, the three-dimensional grid cell is labeled as a transient or continuous coincidence occupancy label based on the avoidance intention and the relative motion entropy of the dynamic obstacle; If the three-dimensional grid cells where the dynamic obstacles and the static obstacles are located are adjacent, the angle grid cell is calculated based on the delivery trajectory tangent, the avoidance intention, the relative motion entropy and the static obstacle, and is labeled as a blind occupancy label; If the three-dimensional grid cells where the dynamic obstacles are located are adjacent, the three-dimensional grid cell is labeled as a corresponding transient coincidence occupancy label, a continuous coincidence occupancy label and a to-be-coincidence occupancy label based on the avoidance intention and the relative motion entropy. 3.The urban logistics oriented UAV precision delivery control method of claim 2, wherein, The influence level of the target area is determined based on the grid map, and the delivery permission level of the unmanned aerial vehicle is determined based on the influence level and the end micro-meteorological disturbance field, including: An obstacle factor is calculated according to the proportion of each type of label in the grid map, a sensitivity threshold is set according to the cargo attribute, and an adaptation factor is calculated according to the sensitivity threshold and the obstacle factor; An influence score is calculated based on the obstacle factor and the adaptation factor, the influence level of the three-dimensional grid cell is determined according to the influence score, and the influence level of the three-dimensional calculation range is generated according to the influence level of each three-dimensional grid cell, wherein the influence level is divided into no influence, first-level influence and second-level influence; The delivery permission level is determined based on the influence level and the wind field stability in the end micro-meteorological disturbance field, and the delivery permission level is divided into normal permission, limited permission and suspended permission. 4.The urban logistics oriented UAV precision delivery control method of claim 3, wherein, The pre-compensation amount is set in the pre-trigger period of the deviation interval according to the delivery permission level, the delivery attitude and the delivery trajectory of the unmanned aerial vehicle are adjusted at the attitude check point based on the pre-compensation amount and the angular velocity, and precise delivery control is performed, including: When the delivery permission level is suspended permission, a waiting window is preset, the delivery permission level is re-determined, if it is suspended permission in the waiting window, the unmanned aerial vehicle is controlled to return to the starting point, if it is normal permission or limited permission, the delivery attitude and the delivery trajectory of the unmanned aerial vehicle are adjusted; When the delivery permission level is normal permission, the minimum aerodynamic disturbance angle of the delivery attitude is calculated based on the end micro-meteorological disturbance field, and precise delivery control is performed according to the predetermined delivery trajectory; When the delivery permission level is limited permission, precise delivery control is performed based on the end micro-meteorological disturbance field. 5.The urban logistics oriented UAV precision delivery control method of claim 4, wherein, When the delivery permission level is limited permission, precise delivery control is performed based on the end micro-meteorological disturbance field. When the delivery permission level is a restricted license, a delivery trajectory set is generated based on the end micro-meteorological disturbance field, the delivery target point, and the three-dimensional grid cell in which the UAV is located, and a time-sensitive degree is set based on the delivery target point and the three-dimensional grid cell in which the UAV is located; The delivery trajectory is analyzed based on the end micro-meteorological disturbance field and the time-sensitive degree, and the initial weight and the dispersion of the initial weight of the delivery trajectory are calculated, the weight of the delivery trajectory is obtained based on the initial weight and the dispersion of the delivery trajectory, and an attitude check point of the delivery trajectory corresponding to the minimum delivery trajectory weight is set; A fluctuation period atlas of the three-dimensional grid cell is constructed based on the end micro-meteorological disturbance field, and features of the fluctuation period atlas are generated; An angular deviation is determined based on the attitude data, the angular deviation is divided into L deviation intervals according to the features of the fluctuation period atlas, a deviation correlation model is constructed in the deviation interval, and an angular rate is calculated by combining a preset fluctuation factor; A pre-compensation amount is set in a pre-trigger period of the deviation interval, the attitude is adjusted at the attitude check point based on the pre-compensation amount and the angular rate, and precise delivery control is performed. 6.The urban logistics oriented UAV precision delivery control method of claim 5, wherein, The pre-compensation amount is set in the pre-trigger period of the deviation interval, the attitude is adjusted at the attitude check point based on the pre-compensation amount and the angular rate, and precise delivery control is performed, including: In the deviation interval, a set of pre-trigger windows is determined based on the fluctuation period atlas, and each pre-trigger window is assigned a corresponding time period; In the pre-trigger window, an initial pre-compensation amount is determined according to the interval position of the angular deviation and the fluctuation factor, and a pre-compensation sequence is generated; The pre-compensation sequence is coupled with the angular rate to obtain an attitude adjustment input; When the attitude check point is triggered, the attitude of the UAV is controlled based on the attitude adjustment input, so that the attitude of the UAV is aligned with the target delivery trajectory, and precise delivery control is performed.

7. The UAV precision delivery control system for urban logistics, for implementing the UAV precision delivery control method for urban logistics according to any one of claims 1-6, characterized in that, It includes a data acquisition module, a wind field perception module, an evaluation module, and a delivery control module, wherein: The data acquisition module is configured to acquire UAV data, target area data, and urban environment data, the UAV data including cargo attributes, attitude data, and positioning data, the target area data including static obstacle data and dynamic obstacle data, and the urban environment data including building wind field data, basic wind field, and real-time wind speed data; The wind field perception module is configured to construct an end micro-meteorological disturbance field by calculating the instantaneous three-dimensional wind field of the target area based on the urban environment data and the target area data; The evaluation module is configured to construct a grid map based on the target area data, detect obstacles and occupancy states of the grid map, obtain the influence level of the target area, and determine the delivery permission level of the UAV in combination with the end micro-meteorological disturbance field; The delivery control module is configured to set a pre-compensation amount in a pre-trigger period of a deviation interval according to the delivery permission level, adjust the delivery attitude and the delivery trajectory of the UAV at an attitude check point based on the pre-compensation amount and an angular rate, and perform precise delivery control, wherein the deviation interval is obtained based on a fluctuation period atlas of a three-dimensional grid cell constructed based on the end micro-meteorological disturbance field and attitude data, and the angular rate is calculated based on the deviation interval.

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