Method for regulating and controlling unmanned aerial vehicle through positioning coordinates

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses a method for regulating and controlling an unmanned aerial vehicle through positioning coordinates, which comprises the steps of evaluating the load capacity of the unmanned aerial vehicle and matching logistics tasks; planning an initial path according to the initial site and the target position of the unmanned aerial vehicle, and performing optimization by combining historical path data to obtain an optimal path; when approaching the target, judging the landing standard; if not, selecting a new landing point; and if yes, judging whether to accurately adjust the residual route, and adjusting according to the real-time information. According to the method, the safety and the stability of the unmanned aerial vehicle in the flight process can be guaranteed, the most suitable unmanned aerial vehicle can be automatically selected for distribution according to specific logistics task requirements, and the efficiency and the accuracy of logistics transportation are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method for positioning coordinates and controlling an unmanned aerial vehicle. Background Art

[0002] In the current logistics industry, drones, as a new delivery tool, have gradually attracted widespread attention and application due to their high efficiency, speed and flexibility. However, during the delivery process, especially in complex environments, how to ensure the accuracy of the drone's path and stable flight has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, the present invention proposes a method for positioning coordinates and controlling a UAV, aiming to improve the path accuracy and flight stability of the UAV in a complex environment.

[0004] The present invention proposes a method for positioning coordinates and controlling a drone, comprising:

[0005] Assess the load capacity of humans and machines, and match logistics tasks based on the load capacity assessment results;

[0006] A coordinate system is established according to the initial position and target position of the UAV, and a path is planned according to the initial position, target position and historical path data of the UAV to obtain an initial path; the initial path is adjusted according to the historical path data to obtain an optimal route, and the UAV travels according to the optimal route;

[0007] When the UAV approaches the target location, determining whether the target location meets the landing criteria;

[0008] When the target position does not meet the landing criteria, a new landing point is selected according to the positioning coordinates in the coordinate system and the real-time environmental information;

[0009] When the target position meets the landing standard, it is determined whether to make precise adjustments to the remaining route; when the determination result is that the remaining route is to be precisely adjusted, the remaining route is precisely adjusted according to the positioning coordinates in the coordinate system and the real-time environmental information.

[0010] Furthermore, the flight stability of the UAV is evaluated based on the maximum take-off weight, maximum load, flight speed, flight altitude, battery range, power-to-load ratio, hovering power and flight power of the UAV, and the load capacity of the UAV is evaluated based on the flight stability; the maximum take-off weight, maximum load, flight speed, flight altitude, battery range, power-to-load ratio, hovering power and flight power of the UAV are calculated according to the following calculation formula:

[0011] Wp=MTW-Wd-Wb-Wf;

[0012]

[0013] D = Vave·Tend;

[0014]

[0015] Among them, MTW is the maximum take-off weight; Wd is the weight of the drone itself; Wp is the payload weight; Wb is the battery weight; Wf is the fuel weight; Tend is the endurance time; Cbat is the capacity of the battery; Iavg is the average current consumption of the drone; D is the endurance distance; Vavg is the average flight speed of the drone; Ttot is the total thrust of the drone; Phov is the hovering power; Vhov is the downdraft speed when hovering; η is the total efficiency of the system.

[0016] Furthermore, the flight stability of the drone is evaluated based on the drone's maximum take-off weight, maximum payload, flight speed, flight altitude, battery range, power-to-load ratio, hovering power, and flight power, including:

[0017] The maximum take-off weight, maximum load, flight speed, flight altitude, battery endurance, power-to-load ratio, hovering power and flight power are standardized. The formula for the standardized processing is:

[0018]

[0019] Wherein, x is the parameter value to be standardized; xmax is the maximum value of the parameter value to be standardized; xmin is the minimum value of the parameter value to be standardized;

[0020] The flight stability score is calculated according to the following formula:

[0021] A=a·φMTW+b·φPay+c·φSpe+d·φAlt+e·φRan+f·φPLR+g·φHor+h·φFli;

[0022] Among them, A is the flight stability score; a, b, c, d, e, f, g, and h are weight coefficients; фMTW is the standardized score of maximum take-off weight; фPay is the standardized score of maximum payload; фSpe is the standardized score of flight speed; фAlt is the standardized score of flight altitude; фRan is the standardized score of battery range; фPLR is the standardized score of power-to-load ratio; фHor is the standardized score of hovering power; and фFli is the standardized score of flight power.

[0023] Furthermore, when evaluating the load capacity of the human-machine according to the flight stability, it includes:

[0024] The payload capacity score is calculated based on the flight stability score, the normalized score of the maximum payload, and the normalized score of the battery range. The calculation formula is:

[0025] Z=k1·φPay+k2·A+k3·φRan;

[0026] Among them, Z is the load capacity score; k1, k2, k3 are weight coefficients.

[0027] Further, obtaining material requirements of the logistics task, wherein the material requirements include task load requirements, task distance requirements, and task time requirements;

[0028] The task load requirement, task distance requirement and task time requirement are standardized to obtain a standardized score of the task load requirement, a standardized score of the task distance requirement and a standardized score of the task time requirement. The formula for the standardized processing is:

[0029]

[0030] Wherein, x is the parameter value to be standardized; xmax is the maximum value of the parameter value to be standardized; xmin is the minimum value of the parameter value to be standardized;

[0031] A task scoring model is established according to the material requirements of the logistics task. The task scoring model is:

[0032] B=w1·λPay+w2·λDis+w3·λTim;

[0033] Where B is the task score; λPay is the standardized score of the task load requirement; λDis is the standardized score of the task distance requirement; λTim is the standardized score of the task time requirement; w1, w2, w3 are weight coefficients;

[0034] When Z ≥ B, the judgment result is that the current drone matches the current logistics task;

[0035] When Z<B, the judgment result is that the current drone does not match the current logistics task.

[0036] Furthermore, a coordinate system is established according to the initial position and target position of the UAV, and path planning is performed according to the initial position, target position and historical path data of the UAV. When the initial path is obtained, it includes:

[0037] The initial position of the drone is taken as the coordinate starting point, the target position of the drone is taken as the coordinate target point, and a three-dimensional coordinate system is established according to the coordinate starting point and the coordinate target point. In the three-dimensional coordinate system, the straight line connecting the coordinate starting point and the coordinate target point is the basic path;

[0038] The area with the midpoint of the basic path as the center and the basic path as the diameter is the obstacle identification area; obtaining the historical path data of the obstacle identification area, wherein the historical path data includes the historical flight trajectory and obstacle information;

[0039] Obstacle heights in the obstacle information are obtained and converted into obstacle height coordinates in the three-dimensional coordinate system; flight safety and flight efficiency of the UAV in the obstacle identification area are analyzed according to historical flight trajectories and obstacle height coordinates, and the basic path is adjusted according to the flight safety and flight efficiency to obtain an initial path.

[0040] Further, the flight safety and flight efficiency of the UAV in the obstacle identification area are analyzed according to the historical flight trajectory and the obstacle height coordinates, and the basic path is adjusted according to the flight safety and flight efficiency to obtain the initial path, including:

[0041] Identify all obstacles in the obstacle identification area and obtain location information and height coordinates of all obstacles;

[0042] Combined with historical flight trajectories, all candidate flight paths of the UAV in the obstacle identification area are obtained;

[0043] Performing a safety assessment on each candidate flight path, wherein the safety assessment is to assess whether the minimum safety distance between the UAV and obstacles during flight meets a preset safety standard;

[0044] When the minimum safety distance between the UAV and all obstacles in a candidate flight path is not less than a preset safety standard, the candidate flight path is determined to be a feasible flight path;

[0045] At this time, the basic path is adjusted to the feasible flight path;

[0046] When there are multiple feasible flight paths, flight efficiency evaluation is performed on all feasible flight paths, and the optimal route is selected based on the flight efficiency evaluation results.

[0047] Furthermore, when there are multiple feasible flight paths, flight efficiency evaluation is performed on all feasible flight paths, and the optimal route is selected according to the flight efficiency evaluation results, including:

[0048] Obtain flight time, flight distance and flight energy consumption data for all feasible flight paths;

[0049] Inputting the flight time, flight distance and flight energy consumption data into a preset flight efficiency evaluation model for processing to obtain flight efficiency evaluation values ​​of all feasible flight paths;

[0050] All flight efficiency evaluation values ​​are sorted from large to small, and the feasible flight path with the highest flight efficiency evaluation value is selected as the optimal route.

[0051] Furthermore, when the UAV approaches the target position, determining whether the target position meets the landing criteria includes:

[0052] Detect the ground flatness and obstacles at the target location; when the ground flatness at the target location is lower than a preset flatness threshold, or there are obstacles that affect the landing of the drone, determine that the target location does not meet the landing standard.

[0053] Further, when the target position does not meet the landing standard, selecting a new landing point according to the positioning coordinates in the coordinate system and the real-time environmental information includes:

[0054] Preset a number of candidate landing points within a range of radius R with the coordinates of the target position as the origin, and obtain the coordinates of the candidate landing points;

[0055] Detect the ground flatness and obstacles of each candidate landing point, calculate the suitability score of each candidate landing point based on the ground flatness and obstacles, and select the candidate landing point with the highest suitability score as the new landing point.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] By comprehensively considering the flight stability, load capacity, compatibility with logistics tasks, path planning, landing point selection and other factors of the drone, the present invention provides a comprehensive, efficient and intelligent drone logistics transportation method. This method can not only ensure the safety and stability of the drone during flight, but also automatically select the most suitable drone for delivery according to the specific logistics task requirements, greatly improving the efficiency and accuracy of logistics transportation.

[0058] In the specific implementation process, this method can automatically obtain key data such as the flight stability score and load capacity score of the drone, and match them with the material requirements of the logistics task to ensure that the drone can successfully complete the task. At the same time, the system can also perform intelligent path planning based on the drone's initial location, target location, and historical path data, select the optimal flight route, reduce flight time and energy consumption, and improve flight efficiency.

[0059] In terms of the selection of the landing point, this method can automatically detect the ground flatness and obstacles at the target location to ensure that the drone can land safely. When the target location does not meet the landing standards, this method can also automatically select a new landing point to ensure the smooth completion of the logistics task.

[0060] In addition, the present invention also provides flight efficiency evaluation models, suitability score calculation algorithms, etc., which further improve the intelligence and accuracy of the system. These algorithms can comprehensively consider multiple factors, provide scientific decision support for UAV logistics transportation, and realize the automation, intelligence and efficiency of logistics transportation.

[0061] In summary, the present invention provides a comprehensive, efficient and intelligent drone logistics transportation method, which brings new development opportunities and challenges to the logistics and transportation industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0063] Figure 1 Flowchart of a method for controlling a drone for positioning coordinates. DETAILED DESCRIPTION

[0064] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] See also Figure 1 This embodiment provides a method for positioning coordinates and controlling a drone, including:

[0066] S1. Evaluate the load capacity of humans and machines, and match logistics tasks based on the load capacity evaluation results;

[0067] S2. Establishing a coordinate system according to the initial position and target position of the UAV, performing path planning according to the initial position, target position and historical path data of the UAV to obtain an initial path; adjusting the initial path according to the historical path data to obtain an optimal route, and the UAV travels according to the optimal route;

[0068] S3. When the UAV approaches the target position, determining whether the target position meets the landing criteria;

[0069] S4. When the target position does not meet the landing standard, a new landing point is selected according to the positioning coordinates in the coordinate system and the real-time environmental information;

[0070] S5. When the target position meets the landing standard, determine whether to make precise adjustments to the remaining route; when the determination result is to make precise adjustments to the remaining route, make precise adjustments to the remaining route according to the positioning coordinates in the coordinate system and the real-time environmental information.

[0071] It is understood that this embodiment describes in detail a method for accurately controlling the positioning coordinates of a drone. The method first involves evaluating the load capacity of the drone, which is to ensure that the drone can safely and effectively perform the assigned tasks. By carefully analyzing the results of the load evaluation, the drone can be matched with a logistics task that is suitable for its carrying capacity, thereby optimizing task allocation and improving the efficiency and success rate of task completion.

[0072] Next, the method requires the establishment of a coordinate system that uses the drone's starting location and the predetermined target location as reference points. After establishing the coordinate system, we will use the drone's starting location, target location, and its historical path data to perform path planning. Through this series of calculations and analysis, we aim to obtain an initial path that will guide the drone from the starting point to the target location safely.

[0073] However, having an initial path is not enough. We also need to adjust this path based on the drone’s past flight data in similar environments in order to obtain a more efficient and safe optimal route. The drone will fly according to this optimal route, which can minimize flight time, fuel consumption, and ensure flight safety.

[0074] During the flight of the drone, once it approaches the predetermined target location, we will determine whether the location meets the landing criteria. These criteria may include but are not limited to factors such as the stability of the landing site, the presence of obstacles, and the weather conditions of the surrounding environment. If the target location does not meet the landing criteria, we will select a new landing point based on the positioning coordinates in the coordinate system and real-time environmental information to ensure that the drone can land safely.

[0075] If the target location meets the landing criteria, we also need to determine whether the remaining flight path of the drone needs to be precisely adjusted. This step is to ensure that the drone can land with the highest accuracy, while taking into account the impact that actual environmental factors may have on the flight path. If precise adjustments are required, we will make detailed adjustments to the remaining flight path based on the positioning coordinates in the coordinate system and real-time environmental information to ensure that the drone can complete the mission accurately and land safely.

[0076] In general, the method provided in this embodiment is a comprehensive and detailed method for controlling the positioning coordinates of a UAV, which ensures the safety and efficiency of the UAV's mission execution through precise evaluation, careful planning, and timely adjustment.

[0077] In some embodiments of the present application, the flight stability of the drone is evaluated according to the maximum take-off weight, maximum load, flight speed, flight altitude, battery endurance, power-to-load ratio, hovering power and flight power of the drone, and the load capacity of the drone is evaluated according to the flight stability; the maximum take-off weight, maximum load, flight speed, flight altitude, battery endurance, power-to-load ratio, hovering power and flight power of the drone are calculated according to the following calculation formula:

[0078] Wp=MTW-Wd-Wb-Wf;

[0079]

[0080] D = Vave·Tend;

[0081]

[0082]

[0083] Among them, MTW is the maximum take-off weight; Wd is the weight of the drone itself; Wp is the payload weight; Wb is the battery weight; Wf is the fuel weight; Tend is the endurance time; Cbat is the capacity of the battery; Iavg is the average current consumption of the drone; D is the endurance distance; Vavg is the average flight speed of the drone; Ttot is the total thrust of the drone; Phov is the hovering power; Vhov is the downdraft speed when hovering; η is the total efficiency of the system.

[0084] It is understandable that this embodiment can achieve a more accurate and scientific evaluation by comprehensively considering multiple key performance parameters of the drone (such as maximum take-off weight, maximum load, flight speed, flight altitude, battery range, power-to-load ratio, hovering power, and flight power). Specifically, the beneficial effects of this evaluation method are reflected in the following aspects:

[0085] 1. Improved flight safety: By evaluating the flight stability of the drone, it can be ensured that the drone can maintain a stable flight state when carrying loads of different weights, avoiding flight accidents caused by overload or insufficient performance.

[0086] 2. Optimize payload configuration: Based on the flight stability evaluation results, the maximum payload capacity of the drone can be determined more accurately, thereby helping users or operators optimize payload configuration and achieve more efficient mission execution.

[0087] 3. Extend the service life of drones: By avoiding overloading and unreasonable use, the service life of drones can be extended and maintenance and replacement costs can be reduced.

[0088] 4. Improve mission efficiency: Based on the evaluation results, the flight performance and endurance of the drone can be predicted more accurately, thereby helping users or operators to formulate more reasonable mission plans and improve mission execution efficiency.

[0089] 5. Enhance system reliability: Taking into account multiple performance parameters, the flight stability of the drone can be evaluated more comprehensively, thereby enhancing the reliability of the entire system and reducing the failure rate.

[0090] In addition, the calculation formula of this embodiment provides a specific method to calculate various key performance parameters of the drone, which provides a more accurate and scientific basis for the evaluation of flight stability. Through these calculations, the performance characteristics of the drone can be understood more accurately, thereby providing strong support for its optimization and use in practical applications.

[0091] In some embodiments of the present application, evaluating the flight stability of the drone based on the maximum take-off weight, maximum load, flight speed, flight altitude, battery range, power-to-load ratio, hovering power, and flight power of the drone includes:

[0092] The maximum take-off weight, maximum load, flight speed, flight altitude, battery endurance, power-to-load ratio, hovering power and flight power are standardized. The formula for the standardized processing is:

[0093]

[0094] Wherein, x is the parameter value to be standardized; xmax is the maximum value of the parameter value to be standardized; xmin is the minimum value of the parameter value to be standardized;

[0095] The flight stability score is calculated according to the following formula:

[0096] A=a·φMTW+b·φPay+c·φSpe+d·φAlt+e·φRan+f·φPLR+g·φHor+h·φFli;

[0097] Among them, A is the flight stability score; a, b, c, d, e, f, g, and h are weight coefficients; фMTW is the standardized score of maximum take-off weight; фPay is the standardized score of maximum payload; фSpe is the standardized score of flight speed; фAlt is the standardized score of flight altitude; фRan is the standardized score of battery range; фPLR is the standardized score of power-to-load ratio; фHor is the standardized score of hovering power; and фFli is the standardized score of flight power.

[0098] It is understandable that the beneficial effects of this method in this embodiment include:

[0099] 1. Standardization processing:

[0100] By standardizing various parameters of drones (such as maximum take-off weight, maximum load, etc.), the unit and dimension differences between different parameters can be eliminated, making the parameters comparable.

[0101] The parameter value after standardization is between [0,1], which is convenient for subsequent scoring calculation.

[0102] 2. Introduction of weight coefficient:

[0103] Different parameters may have different importance when evaluating the flight stability of a drone. For example, for some applications, flight speed may be more important, while for other applications, battery range may be more critical.

[0104] By introducing weight coefficients (a, b, c, d, e, f, g, h), the weights of various parameters in the scoring calculation can be adjusted according to actual needs, so that the evaluation results are more in line with actual needs.

[0105] 3. Comprehensive evaluation:

[0106] By weighting and summing the standardized parameter scores, a comprehensive flight stability score (A) can be obtained. This score can fully reflect the impact of various performance parameters of the drone on flight stability.

[0107] The comprehensive evaluation method can avoid focusing on a single parameter while ignoring other parameters, thereby obtaining a more comprehensive and accurate evaluation result.

[0108] 4. Decision support:

[0109] The flight stability score can be used as an important basis for decision-making on drone selection, improvement and optimization. For example, when selecting a drone, you can choose a drone with a higher flight stability score; when improving and optimizing, you can focus on improving parameters with lower scores.

[0110] In addition, the flight stability score can also be used for performance monitoring and fault diagnosis of drones. By regularly evaluating the flight stability score of drones, potential performance problems can be discovered and addressed in a timely manner, improving the reliability and safety of drones.

[0111] In summary, the UAV flight stability assessment method can comprehensively and accurately evaluate the flight stability of UAVs through standardized processing, introduction of weight coefficients and comprehensive evaluation, and provide strong decision-making support for the selection, improvement and optimization of UAVs.

[0112] In some embodiments of the present application, when evaluating the load capacity of the human-machine according to the flight stability, it includes:

[0113] The payload capacity score is calculated based on the flight stability score, the normalized score of the maximum payload, and the normalized score of the battery range. The calculation formula is:

[0114] Z=k1·φPay+k2·A+k3·φRan;

[0115] Among them, Z is the load capacity score; k1, k2, k3 are weight coefficients.

[0116] It is understandable that the relationship between the flight stability assessment and the man-machine load capacity calculation in this embodiment reflects a comprehensive assessment strategy. This method comprehensively assesses the man-machine load capacity by combining the flight stability score, the standardized score of the maximum load, and the standardized score of the battery range. The following is an analysis of the beneficial effects of this assessment method:

[0117] 1. Comprehensiveness and accuracy: By comprehensively considering the three key factors of flight stability, maximum load and battery life, the evaluation results are more comprehensive and accurate. These three factors are important factors affecting the human-machine load capacity, and the neglect of any one factor may lead to inaccurate evaluation results.

[0118] 2. Flexibility: By setting the weight coefficients k1, k2, and k3, the importance of different factors in the evaluation can be adjusted according to actual needs. For example, in some applications, flight stability may be more important, so the weight of k1 can be increased; in other applications, the maximum load or battery life may be more critical, so the weight of k2 or k3 can be adjusted accordingly.

[0119] 3. Easy to understand and operate: The load capacity score is calculated by combining the scores of different factors through a simple mathematical formula. This method is easy to understand and operate. The user or operator only needs to understand the score of each factor and the setting of the weight coefficient to calculate the load capacity score.

[0120] 4. Provide strong support for decision-making: The load capacity score calculated can provide strong support for decision makers. Decision makers can judge whether the load capacity of the human-machine under the current conditions meets the requirements based on the score results, and make corresponding decisions.

[0121] 5. Promote human-machine system optimization: Through the evaluation results, problems or deficiencies in the human-machine system can be discovered, so that targeted optimization and improvement can be carried out. For example, if the evaluation results show that the flight stability score is low, then improvements can be made to the flight stability; if the battery range score is low, then optimization of battery performance or increase of battery capacity can be considered.

[0122] In summary, by comprehensively considering factors such as flight stability, maximum load and battery life to evaluate the load capacity of the human-machine system, we can not only improve the comprehensiveness and accuracy of the evaluation results, but also provide strong support for decision-making and promote the optimization and improvement of the human-machine system.

[0123] In some embodiments of the present application, material requirements of a logistics task are obtained, and the material requirements include task load requirements, task distance requirements, and task time requirements;

[0124] The task load requirement, task distance requirement and task time requirement are standardized to obtain a standardized score of the task load requirement, a standardized score of the task distance requirement and a standardized score of the task time requirement. The formula for the standardized processing is:

[0125]

[0126] Wherein, x is the parameter value to be standardized; xmax is the maximum value of the parameter value to be standardized; xmin is the minimum value of the parameter value to be standardized;

[0127] A task scoring model is established according to the material requirements of the logistics task. The task scoring model is:

[0128] B=w1·λPay+w2·λDis+w3·λTim;

[0129] Where B is the task score; λPay is the standardized score of the task load requirement; λDis is the standardized score of the task distance requirement; λTim is the standardized score of the task time requirement; w1, w2, w3 are weight coefficients;

[0130] When Z ≥ B, the judgment result is that the current drone matches the current logistics task;

[0131] When Z<B, the judgment result is that the current drone does not match the current logistics task.

[0132] It is understandable that this embodiment specifically discloses a method for evaluating the compatibility of a drone with a logistics task. The beneficial effects of this method are mainly reflected in the following aspects:

[0133] 1. Standardization improves accuracy: By standardizing the mission load requirements, mission distance requirements, and mission time requirements, the dimensional differences between different mission parameters can be eliminated, so that various requirements can be compared and evaluated under the same standard, thereby improving the accuracy of matching.

[0134] 2. Flexible adjustment of weight coefficients: In the task scoring model, by introducing weight coefficients w1, w2, and w3, the importance of different requirements can be flexibly adjusted according to actual conditions. For example, in some cases, task time may be more important, while in other cases, task load or distance may be more critical. This flexibility makes the scoring model more in line with the needs of actual applications.

[0135] 3. Fast matching improves efficiency: Through simple mathematical operations and comparisons, it is possible to quickly determine whether the drone matches the logistics task. This greatly reduces the time for manual screening and evaluation and improves the efficiency of logistics task allocation.

[0136] 4. Optimize resource allocation: By accurately matching drones with logistics tasks, it can ensure that drones can fully utilize their performance when performing tasks and avoid wasting resources. At the same time, it can also ensure that logistics tasks can be completed on time and improve customer satisfaction.

[0137] 5. Strong adaptability: This method is not only applicable to the matching of drones and logistics tasks, but can also be extended to other similar tasks and resource matching scenarios. By adjusting the parameters and scoring model, it can adapt to different application scenarios and needs.

[0138] In summary, this method of matching drones with logistics tasks based on standardized processing and scoring models has many beneficial effects, including improving accuracy, flexibility, efficiency, and optimizing resource allocation.

[0139] In some embodiments of the present application, a coordinate system is established according to the initial position and target position of the drone, and path planning is performed according to the initial position, target position and historical path data of the drone. When the initial path is obtained, the following steps are included:

[0140] The initial position of the drone is taken as the coordinate starting point, the target position of the drone is taken as the coordinate target point, and a three-dimensional coordinate system is established according to the coordinate starting point and the coordinate target point. In the three-dimensional coordinate system, the straight line connecting the coordinate starting point and the coordinate target point is the basic path;

[0141] The area with the midpoint of the basic path as the center and the basic path as the diameter is the obstacle identification area; obtaining the historical path data of the obstacle identification area, wherein the historical path data includes the historical flight trajectory and obstacle information;

[0142] Obstacle heights in the obstacle information are obtained and converted into obstacle height coordinates in the three-dimensional coordinate system; flight safety and flight efficiency of the UAV in the obstacle identification area are analyzed according to historical flight trajectories and obstacle height coordinates, and the basic path is adjusted according to the flight safety and flight efficiency to obtain an initial path.

[0143] It can be understood that the beneficial effects of this embodiment are mainly reflected in the process of drone path planning, especially in the comprehensive consideration of the drone's initial location, target location and historical path data.

[0144] Here is a detailed explanation of these beneficial effects:

[0145] 1. Accurate establishment of three-dimensional coordinate system:

[0146] By setting the initial position of the drone as the coordinate starting point and the target position as the coordinate target point, a three-dimensional coordinate system can be accurately established. The establishment of this three-dimensional coordinate system helps to more accurately describe the flight environment and possible flight path of the drone.

[0147] 2. Intelligent setting of obstacle recognition area:

[0148] The area with the midpoint of the basic path as the center and the basic path as the diameter is set as the obstacle identification area. The setting of this area can ensure that the drone can identify and avoid possible obstacles in advance when approaching the target location.

[0149] 3. Effective use of historical route data:

[0150] By acquiring historical path data of the obstacle identification zone, including historical flight trajectories and obstacle information, the drone can learn and draw on past flight experience. This not only helps avoid known obstacles, but also improves flight safety and efficiency.

[0151] 4. Accurate processing of obstacle information:

[0152] Converting the obstacle height in the obstacle information into obstacle height coordinates in a three-dimensional coordinate system can ensure that the height of the obstacle can be accurately considered when the drone plans the path, thereby avoiding collisions.

[0153] 5. Dual optimization of flight safety and flight efficiency:

[0154] After analyzing the flight safety and efficiency of the drone in the obstacle identification area, the system can adjust the basic path to obtain an initial path that is both safe and efficient. This dual-optimization path planning method can ensure the stable flight of the drone in complex environments.

[0155] In summary, this UAV path planning method based on the initial position, target position and historical path data greatly improves the safety and efficiency of UAV flight through the establishment of accurate three-dimensional coordinate system, intelligent obstacle identification area setting, effective use of historical path data, accurate obstacle information processing and dual optimization of flight safety and flight efficiency.

[0156] In some embodiments of the present application, the flight safety and flight efficiency of the UAV in the obstacle identification area are analyzed according to the historical flight trajectory and the obstacle height coordinates, and the basic path is adjusted according to the flight safety and flight efficiency to obtain the initial path, including:

[0157] Identify all obstacles in the obstacle identification area and obtain location information and height coordinates of all obstacles;

[0158] Combined with historical flight trajectories, all candidate flight paths of the UAV in the obstacle identification area are obtained;

[0159] Performing a safety assessment on each candidate flight path, wherein the safety assessment is to assess whether the minimum safety distance between the UAV and obstacles during flight meets a preset safety standard;

[0160] When the minimum safety distance between the UAV and all obstacles in a candidate flight path is not less than a preset safety standard, the candidate flight path is determined to be a feasible flight path;

[0161] At this time, the basic path is adjusted to the feasible flight path;

[0162] When there are multiple feasible flight paths, flight efficiency evaluation is performed on all feasible flight paths, and the optimal route is selected based on the flight efficiency evaluation results.

[0163] It is understandable that in the UAV flight path planning system of this embodiment, by combining the historical flight trajectory and the height coordinates of the obstacles, the system can intelligently analyze the flight safety and flight efficiency of the UAV in the obstacle identification area, and adjust the basic path according to these analysis results, so as to obtain a more suitable initial flight path. This method has the following significant beneficial effects:

[0164] 1. Improve flight safety:

[0165] The system can accurately identify all obstacles within the obstacle identification area and obtain their location information and altitude coordinates, which provides an accurate data basis for subsequent flight path planning.

[0166] By conducting a safety assessment on each candidate flight path, we ensure that the minimum safe distance between the drone and obstacles during flight meets the preset safety standards, effectively avoiding the risk of collision between the drone and obstacles and improving flight safety.

[0167] 2. Improve flight efficiency:

[0168] On the basis of ensuring flight safety, the system will also evaluate the flight efficiency of all feasible flight paths, and select the optimal flight path by comparing the flight efficiency of different paths.

[0169] This optimization method enables the drone to choose the shortest path, the most time-saving flight strategy or the most energy-efficient flight mode, thereby improving the drone's flight efficiency.

[0170] 3. Intelligent Adaptation and Flexibility:

[0171] The system not only plans the path based on current obstacle information and historical flight trajectories, but can also dynamically adjust the flight path according to real-time environmental data, showing a high degree of intelligent adaptability and flexibility.

[0172] When the flight environment changes, such as the appearance of new obstacles or the change of the position of existing obstacles, the system can respond quickly and re-plan the flight path to ensure that the drone can complete the flight mission safely and efficiently.

[0173] 4. Reduce operational difficulty and complexity:

[0174] Traditional UAV flight path planning often requires operators to have rich experience and professional knowledge. However, this method realizes automatic flight path planning through intelligent algorithms and data analysis, reducing the difficulty and complexity of operation.

[0175] The operator only needs to set the flight mission and target point, and the system can automatically complete the path planning, greatly improving the convenience and efficiency of operation.

[0176] In summary, the system combines historical flight trajectories and the height coordinates of obstacles for flight path planning, which not only improves the flight safety and efficiency of UAVs, but also shows a high degree of intelligent adaptability and flexibility, reduces the difficulty and complexity of operation, and provides strong technical support for the widespread application of UAVs.

[0177] In some embodiments of the present application, when there are multiple feasible flight paths, flight efficiency evaluation is performed on all feasible flight paths, and the optimal route is selected according to the flight efficiency evaluation result, including:

[0178] Obtain flight time, flight distance and flight energy consumption data for all feasible flight paths;

[0179] Inputting the flight time, flight distance and flight energy consumption data into a preset flight efficiency evaluation model for processing to obtain flight efficiency evaluation values ​​of all feasible flight paths;

[0180] All flight efficiency evaluation values ​​are sorted from large to small, and the feasible flight path with the highest flight efficiency evaluation value is selected as the optimal route.

[0181] The beneficial effects of the embodiments of the present application are mainly reflected in the following aspects:

[0182] 1. Optimize flight efficiency: By comprehensively considering the flight time, flight distance and flight energy consumption data of all feasible flight paths, flight efficiency can be evaluated more comprehensively. This evaluation method not only takes into account the speed factor (flight time), but also takes into account economy (flight energy consumption) and directness (flight distance), so as to select the flight path with the highest comprehensive efficiency as the optimal route.

[0183] 2. Intelligent decision-making: The introduction of a preset flight efficiency evaluation model makes the process of selecting the optimal flight path more intelligent. This model can be trained and optimized based on a large amount of historical data and flight experience, so as to more accurately predict and evaluate the efficiency of different flight paths.

[0184] 3. Improve safety: In addition to efficiency factors, other safety factors can also be considered when selecting the optimal flight path, such as weather conditions, airspace congestion, etc. The flight efficiency evaluation model can be further expanded to include these factors to ensure the safety and reliability of the selected flight path.

[0185] To sum up, the embodiments of the present application comprehensively consider multiple factors such as flight time, flight distance and flight energy consumption, and use a preset flight efficiency evaluation model to select the optimal flight path, which has multiple beneficial effects such as optimizing flight efficiency, intelligent decision-making, improving safety, reducing operating costs and improving user experience.

[0186] In some embodiments of the present application, the process of establishing the flight efficiency evaluation model includes:

[0187] Collect historical flight data, including flight time, flight distance, flight energy consumption under different flight paths, and real-time environmental data during the flight;

[0188] Pre-process the collected historical flight data to remove outliers and noise data to ensure data quality and accuracy;

[0189] Based on the preprocessed historical flight data, a preliminary structure of a flight efficiency evaluation model is established; the preliminary structure is a multiple linear regression model or a neural network model;

[0190] Use some historical flight data as training sets to train the flight efficiency evaluation model. During the training process, adjust the parameters and structure of the model so that the model can more accurately predict and evaluate the flight efficiency of different flight paths.

[0191] Use another part of historical flight data as a test set to test and verify the trained flight efficiency evaluation model; evaluate the accuracy and reliability of the model by comparing the model prediction results with the actual flight data;

[0192] Based on the test and verification results, the flight efficiency evaluation model is further optimized and adjusted to improve the accuracy and stability of its prediction and evaluation;

[0193] Finally, the optimized flight efficiency evaluation model is applied to actual flight path planning.

[0194] In the process of establishing the flight efficiency evaluation model, the following points should be noted:

[0195] 1. Data diversity: The historical flight data collected should be diverse enough to cover a variety of situations under different flight conditions. This helps ensure the accuracy and reliability of the flight efficiency evaluation model in various real-world scenarios.

[0196] 2. Model generalization ability: During the training process, we should focus on improving the generalization ability of the model. This means that the model can not only achieve good prediction results on the training set, but also maintain stable performance on new, unseen data.

[0197] 3. Real-time update: With the continuous development of flight technology and the continuous accumulation of new data, the flight efficiency evaluation model needs to be updated and optimized regularly. This helps to maintain the advancement and adaptability of the model and ensure that it can continue to provide accurate and reliable flight path planning suggestions in practical applications.

[0198] The flight efficiency evaluation model established through the above steps can provide strong support for the UAV flight path planning system. In practical applications, the model can quickly and accurately evaluate the flight efficiency of different flight paths according to the real-time flight environment and mission requirements, and select the optimal flight path. This can not only improve the flight safety and efficiency of the UAV, but also reduce operating costs and improve user experience. At the same time, the model is also highly flexible and scalable, and can be customized and optimized according to actual needs.

[0199] In some embodiments of the present application, when the drone approaches a target location, determining whether the target location meets the landing criteria includes:

[0200] Detect the ground flatness and obstacles at the target location; when the ground flatness at the target location is lower than a preset flatness threshold, or there are obstacles that affect the landing of the drone, determine that the target location does not meet the landing standard.

[0201] In some embodiments of the present application, when the target position does not meet the landing standard, selecting a new landing point according to the positioning coordinates in the coordinate system and the real-time environmental information includes:

[0202] Preset a number of candidate landing points within a range of radius R with the coordinates of the target position as the origin, and obtain the coordinates of the candidate landing points;

[0203] Detect the ground flatness and obstacles of each candidate landing point, calculate the suitability score of each candidate landing point based on the ground flatness and obstacles, and select the candidate landing point with the highest suitability score as the new landing point.

[0204] It is understandable that the beneficial effects of this embodiment are mainly reflected in the intelligent decision-making and optimization of the drone during the automatic landing process. The following is a detailed analysis of these beneficial effects:

[0205] 1. Improve flight safety:

[0206] When the drone approaches the target location, the system can automatically detect the ground flatness and obstacles at the target location. If the ground flatness of the target location is lower than the preset threshold, or there are obstacles that affect the landing of the drone, the system will determine that the location is not suitable for landing. This intelligent judgment mechanism can effectively avoid accidents caused by drones choosing inappropriate landing locations, greatly improving flight safety.

[0207] 2. Optimize landing strategy:

[0208] When the target location does not meet the landing criteria, the system can select a new landing point based on the positioning coordinates in the coordinate system and real-time environmental information. By presetting several candidate landing points within a range of radius R with the target location as the origin, and detecting the ground flatness and obstacles of each candidate landing point, the system can calculate the suitability score of each candidate landing point and select the point with the highest score as the new landing point. This strategy not only ensures that the drone can find a safe landing location, but also optimizes the landing process and improves the efficiency and success rate of landing.

[0209] 3. Enhance system autonomy:

[0210] During the entire process, the drone can automatically determine the suitability of the landing location without human intervention and autonomously select a new landing point when necessary. This highly automated function not only enhances the system autonomy of the drone, but also reduces dependence on operators, allowing the drone to fly and land autonomously in a wider range of scenarios and more complex environments.

[0211] 4. Improve user experience:

[0212] Through intelligent judgment and autonomous decision-making, drones can find a suitable landing location more quickly and accurately while ensuring safety. This not only improves the efficiency of drones, but also reduces delays and losses caused by human operating errors or inaccurate judgments, thereby improving user satisfaction and experience.

[0213] In short, the above beneficial effects are mainly reflected in improving flight safety, optimizing landing strategies, enhancing system autonomy and improving user experience, etc. These effects make the drone more intelligent, efficient and safer during automatic landing.

[0214] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0215] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0216] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0217] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for positioning coordinates and controlling a drone, characterized in that: include: Assess the load capacity of humans and machines, and match logistics tasks based on the load capacity assessment results; A coordinate system is established according to the initial position and target position of the UAV, and a path is planned according to the initial position, target position and historical path data of the UAV to obtain an initial path; The initial path is adjusted according to the historical path data to obtain an optimal route, and the UAV travels according to the optimal route; When the UAV approaches the target location, determining whether the target location meets the landing criteria; When the target position does not meet the landing criteria, a new landing point is selected according to the positioning coordinates in the coordinate system and the real-time environmental information; When the target position meets the landing standard, it is determined whether to make precise adjustments to the remaining route; when the determination result is that the remaining route is to be precisely adjusted, the remaining route is precisely adjusted according to the positioning coordinates in the coordinate system and the real-time environmental information.

2. The method for positioning coordinates and controlling a drone according to claim 1, characterized in that: The flight stability of the drone is evaluated based on the drone's maximum take-off weight, maximum load, flight speed, flight altitude, battery range, power-to-load ratio, hovering power and flight power. The load capacity of the drone is evaluated based on the flight stability. The maximum take-off weight, maximum load, flight speed, flight altitude, battery range, power-to-load ratio, hovering power and flight power of the drone are calculated according to the following formula: Wp=MTW-Wd-Wb-Wf; Among them, MTW is the maximum take-off weight; Wd is the weight of the drone itself; Wp is the payload weight; Wb is the battery weight; Wf is the fuel weight; Tend is the endurance time; Cbat is the capacity of the battery; Iavg is the average current consumption of the drone; D is the endurance distance; Vavg is the average flight speed of the drone; Ttot is the total thrust of the drone; Phov is the hovering power; Vhov is the downdraft speed when hovering; η is the total efficiency of the system.

3. The method for positioning coordinates and controlling a drone according to claim 2, characterized in that: The flight stability of the drone is evaluated based on the drone's maximum take-off weight, maximum payload, flight speed, flight altitude, battery range, power-to-load ratio, hovering power, and flight power, including: The maximum take-off weight, maximum load, flight speed, flight altitude, battery endurance, power-to-load ratio, hovering power and flight power are standardized. The formula for the standardized processing is: Wherein, x is the parameter value to be standardized; xmax is the maximum value of the parameter value to be standardized; xmin is the minimum value of the parameter value to be standardized; The flight stability score is calculated according to the following formula: A=a·φMTW+b·φPay+c·φSpe+d·φAlt+e·φRan+f·φPLR+g·φHor+h·φFli; Among them, A is the flight stability score; a, b, c, d, e, f, g, and h are weight coefficients; фMTW is the standardized score of maximum take-off weight; фPay is the standardized score of maximum payload; фSpe is the standardized score of flight speed; фAlt is the standardized score of flight altitude; фRan is the standardized score of battery range; фPLR is the standardized score of power-to-load ratio; фHor is the standardized score of hovering power; фFli is the standardized score of flight power.

4. The method for positioning coordinates and controlling a drone according to claim 3, characterized in that: When evaluating the payload capacity of a drone based on the flight stability described above, this includes: The payload capacity score is calculated based on the flight stability score, the normalized score of the maximum payload, and the normalized score of the battery range. The calculation formula is: Z=k1·φPay+k2·A+k3·φRan; Among them, Z is the load capacity score; k1, k2, k3 are weight coefficients.

5. The method for controlling a drone by positioning coordinates according to claim 4, characterized in that: Obtaining material requirements for logistics tasks, including task load requirements, task distance requirements, and task time requirements; The task load requirement, task distance requirement and task time requirement are standardized to obtain a standardized score of the task load requirement, a standardized score of the task distance requirement and a standardized score of the task time requirement. The formula for the standardized processing is: Wherein, x is the parameter value to be standardized; xmax is the maximum value of the parameter value to be standardized; xmin is the minimum value of the parameter value to be standardized; A task scoring model is established according to the material requirements of the logistics task. The task scoring model is: B=w1·λPay+w2·λDis+w3·λTim; Where B is the task score; λPay is the standardized score of the task load requirement; λDis is the standardized score of the task distance requirement; λTim is the standardized score of the task time requirement; w1, w2, w3 are weight coefficients; When Z ≥ B, the judgment result is that the current drone matches the current logistics task; When Z<B, the judgment result is that the current drone does not match the current logistics task.

6. The method for positioning coordinates and controlling a drone according to claim 1, characterized in that: A coordinate system is established according to the initial position and target position of the UAV, and a path is planned according to the initial position, target position and historical path data of the UAV to obtain an initial path; The initial path is adjusted according to the historical path data to obtain an optimal route, and the UAV drives according to the optimal route, including: The initial position of the drone is taken as the coordinate starting point, the target position of the drone is taken as the coordinate target point, and a three-dimensional coordinate system is established according to the coordinate starting point and the coordinate target point. In the three-dimensional coordinate system, the straight line connecting the coordinate starting point and the coordinate target point is the basic path; The area with the midpoint of the basic path as the center and the basic path as the diameter is the obstacle identification area; obtaining the historical path data of the obstacle identification area, wherein the historical path data includes the historical flight trajectory and obstacle information; Obstacle heights in the obstacle information are obtained and converted into obstacle height coordinates in the three-dimensional coordinate system; flight safety and flight efficiency of the UAV in the obstacle identification area are analyzed according to historical flight trajectories and obstacle height coordinates, and the basic path is adjusted according to the flight safety and flight efficiency to obtain an initial path.

7. The method for positioning coordinates and controlling a drone according to claim 6, characterized in that: According to the historical flight trajectory and the obstacle height coordinates, the flight safety and flight efficiency of the UAV in the obstacle identification area are analyzed, and the basic path is adjusted according to the flight safety and flight efficiency to obtain the initial path, including: Identify all obstacles in the obstacle identification area and obtain location information and height coordinates of all obstacles; Combined with historical flight trajectories, all candidate flight paths of the UAV in the obstacle identification area are obtained; Performing a safety assessment on each candidate flight path, wherein the safety assessment is to assess whether the minimum safety distance between the UAV and obstacles during flight meets a preset safety standard; When the minimum safety distance between the UAV and all obstacles in a candidate flight path is not less than a preset safety standard, the candidate flight path is determined to be a feasible flight path; At this time, the basic path is adjusted to the feasible flight path; When there are multiple feasible flight paths, flight efficiency evaluation is performed on all feasible flight paths, and the optimal route is selected based on the flight efficiency evaluation results.

8. The method for controlling a drone by positioning coordinates according to claim 7, characterized in that: When there are multiple feasible flight paths, flight efficiency evaluation is performed on all feasible flight paths, and the optimal route is selected based on the flight efficiency evaluation results, including: Obtain flight time, flight distance and flight energy consumption data for all feasible flight paths; Inputting the flight time, flight distance and flight energy consumption data into a preset flight efficiency evaluation model for processing to obtain flight efficiency evaluation values ​​of all feasible flight paths; All flight efficiency evaluation values ​​are sorted from large to small, and the feasible flight path with the highest flight efficiency evaluation value is selected as the optimal route.

9. The method for controlling a drone by positioning coordinates according to claim 1, characterized in that: When the UAV approaches the target location, it determines whether the target location meets the landing criteria, including: Detect the ground flatness and obstacles at the target location; when the ground flatness at the target location is lower than a preset flatness threshold, or there are obstacles that affect the landing of the drone, determine that the target location does not meet the landing standard.

10. The method for controlling a drone by positioning coordinates according to claim 9, characterized in that: When the target position does not meet the landing standard, a new landing point is selected according to the positioning coordinates in the coordinate system and the real-time environmental information, including: Preset a number of candidate landing points within a range of radius R with the coordinates of the target position as the origin, and obtain the coordinates of the candidate landing points; Detect the ground flatness and obstacles of each candidate landing point, calculate the suitability score of each candidate landing point based on the ground flatness and obstacles, and select the candidate landing point with the highest suitability score as the new landing point.

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