Intelligent task planning method for unmanned aerial vehicle logistics distribution

Abstract

The invention relates to the technical field of unmanned aerial vehicle logistics distribution, in particular to an intelligent task planning method for unmanned aerial vehicle logistics distribution, and the method comprises the steps: carrying out the real-time collection of environment data and logistics data, analyzing the task demands of logistics distribution, generating a distribution task list according to the task demands, and transmitting the distribution task list to a server; distributing the distribution tasks to a plurality of unmanned aerial vehicles; determining the position of the unmanned aerial vehicle by using a positioning sensor, determining the flight path of the unmanned aerial vehicle by using a path planning algorithm, and optimizing the path and task allocation of each unmanned aerial vehicle in real time according to the actual environment change; determining the task planning stability of unmanned aerial vehicle logistics distribution based on the linear distance between the position of the unmanned aerial vehicle and the preset position; if the task planning stability does not meet the requirement, the data transmission rate of a positioning sensor is adjusted; and if the robustness does not meet the requirement, adjusting the acquisition frequency of the logistics data. According to the invention, the task planning stability of unmanned aerial vehicle logistics distribution is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle logistics distribution, and in particular to an intelligent task planning method for unmanned aerial vehicle logistics distribution. Background Art

[0002] In the existing technology, with the rapid development of e-commerce and logistics industries, people have put forward higher requirements for the efficiency and service quality of logistics distribution. In the face of growing distribution needs, traditional logistics distribution methods have gradually exposed many limitations. For example, in the case of urban traffic congestion, the driving speed of ground delivery vehicles is greatly reduced, and the delivery time is extended, resulting in the failure of goods to be delivered to customers in time; moreover, in some remote areas or areas with inconvenient transportation, traditional delivery methods are difficult to cover and cannot meet the logistics needs of local residents. At the same time, the continuous maturity of drone technology has brought new solutions to the field of logistics distribution. Drones have the advantages of strong maneuverability, no restrictions on ground traffic, and the ability to reach the destination quickly, which can effectively make up for the shortcomings of traditional logistics distribution methods. However, there are many challenges to achieving efficient and reliable drone logistics distribution.

[0003] Chinese Patent Publication No.: CN116415873A discloses a UAV mission planning method for inland river ship logistics distribution, the steps are as follows: (1) collect the planned navigation data of ships with logistics distribution needs, classify the trajectory data according to the upstream and downstream directions of the inland river ships, and divide the ship trajectory into multiple trajectory time subsequences at equal time intervals; (2) according to the ship position, time, and navigation speed data, use the trajectory time subsequences in step (1) to calculate the spatiotemporal distances between different ship trajectory subsequences for the upstream and downstream directions, generate multiple spatiotemporal distance matrices of different ship trajectory subsequences, and calculate two spatiotemporal distance matrices in turn. The similarity between the ship trajectory sequences; (3) based on the similarity between the trajectory sequences obtained in step (2), cluster the ship trajectories, and obtain the ship trajectory classification according to the clustering results; (4) according to the inland ship trajectory clustering results, determine the ship trajectory information that meets the drone delivery range under the same clustering category, define the decision variables of drone logistics delivery, and combine the drone's own characteristics and ship logistics delivery task requirements to build a multi-drone logistics delivery task planning model that considers the shortest path length; (5) solve the multi-drone logistics delivery task planning model established in step (4) to obtain the logistics delivery task allocation and flight path optimal plan for each drone. It can be seen that the drone task planning method for inland ship logistics delivery has the problem that the sensor in the drone gradually ages after long-term use, resulting in a decrease in sensor accuracy, inaccurate positioning, and thus a decrease in the stability of the drone logistics delivery task planning. Summary of the invention

[0004] To this end, the present invention provides an intelligent task planning method for drone logistics delivery, which is used to overcome the problem in the prior art that the accuracy of the sensors is reduced due to the gradual aging of the internal electronic components of the sensors in the drone after long-term use, resulting in inaccurate positioning and thus causing the stability of the task planning of drone logistics delivery to decrease.

[0005] To achieve the above-mentioned purpose, the present invention provides an intelligent task planning method for unmanned aerial vehicle logistics distribution, comprising: respectively collecting environmental data and logistics data in real time, analyzing the task requirements of logistics distribution, generating a distribution task list according to the task requirements, and assigning the distribution tasks to several unmanned aerial vehicles; using a positioning sensor to determine the position of the unmanned aerial vehicle, using a path planning algorithm to determine the flight path of the unmanned aerial vehicle, and optimizing the path and task allocation of each unmanned aerial vehicle in real time according to actual environmental changes; respectively obtaining the position of a single unmanned aerial vehicle and the predetermined position of the unmanned aerial vehicle; determining the task planning stability of the unmanned aerial vehicle logistics distribution based on the straight-line distance between the position of the unmanned aerial vehicle and the predetermined position; if the task planning stability does not meet the requirements, adjusting the data transmission rate of the positioning sensor, or determining whether the robustness of the task planning meets the requirements based on the error rate of the unmanned aerial vehicle path planning; if the robustness does not meet the requirements, adjusting the collection frequency of the logistics data, or adjusting the rotation speed of the propeller in the unmanned aerial vehicle based on the average delay time of the unmanned aerial vehicle logistics distribution.

[0006] Further, determining the mission planning stability of the UAV logistics delivery includes: Comparing the straight-line distance between the position of the drone and the predetermined position with a preset first distance; If the straight-line distance between the position of the drone and the predetermined position is greater than the preset first distance, it is determined that the mission planning stability of the drone logistics delivery does not meet the requirements.

[0007] Further, determining the robustness of the task planning includes: Comparing the straight-line distance between the position of the drone and the predetermined position with the preset first distance and the preset second distance respectively; If the straight-line distance between the position of the UAV and the predetermined position is greater than the preset first distance and less than or equal to the preset second distance, it is preliminarily determined that the robustness of the mission planning does not meet the requirements, and whether the robustness of the mission planning meets the requirements is determined based on the error rate of the UAV path planning.

[0008] Further, adjusting the data transmission rate of the positioning sensor includes: Comparing the straight-line distance between the position of the drone and the predetermined position with the preset second distance; If the straight-line distance between the position of the drone and the predetermined position is greater than a preset second distance, the data transmission rate of the positioning sensor is increased.

[0009] Furthermore, the increase range of the data transmission rate of the positioning sensor is determined according to the difference between the straight-line distance between the position of the drone and the predetermined position and a preset second distance.

[0010] Furthermore, the collection frequency of the logistics data is adjusted, including: Compare the error rate of the drone path planning with the preset first error rate and the preset second error rate respectively; If the error rate of the drone path planning is greater than the preset first error rate, it is determined that the robustness of the mission planning does not meet the requirements; If the error rate of the drone path planning is greater than the preset first error rate and less than or equal to the preset second error rate, reducing the frequency of collecting logistics data; If the error rate of the drone path planning is greater than the preset second error rate, it is preliminarily determined that the environmental stability of the task planning does not meet the requirements, and whether the environmental stability of the task planning meets the requirements is determined based on the average delay time of the drone logistics delivery.

[0011] Furthermore, the error rate of the drone path planning is the ratio of the number of drone path planning errors to the total number of path planning errors.

[0012] Furthermore, the reduction degree of the collection frequency of the logistics data is determined according to the difference between the error rate of the drone path planning and the preset first error rate.

[0013] Furthermore, the rotation speed of the propeller in the drone is adjusted, including: Compare the average delay time of drone logistics delivery with the preset delay time; If the average delay time of the drone logistics delivery is greater than the preset delay time, it is determined that the environmental stability of the mission planning does not meet the requirements, and the rotation speed of the propeller in the drone is increased.

[0014] Furthermore, the increase in the rotation speed of the propeller in the drone is determined according to the difference between the average delay time of the drone logistics delivery and the preset delay time.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that the method of the present invention adjusts the data transmission rate of the positioning sensor according to the straight-line distance between the position of the drone and the predetermined position. Since the internal electronic components of the sensors in the drone gradually age after long-term use, the accuracy of the sensors decreases, resulting in inaccurate positioning. By increasing the data transmission rate of the positioning sensor, the positioning sensor can transmit the collected data to the processing unit more quickly, so that the drone control system can adjust the position and correct the flight attitude according to the new data more quickly, and adjust the collection frequency of logistics data according to the error rate of the drone path planning. Since the collected data may contain noise, the planning algorithm needs to spend extra time to deal with these problems, and the algorithm may also be affected by data quality problems. The wrong results will be obtained, which will affect the normal operation of the algorithm. By reducing the collection frequency of logistics data, some high-frequency noise can be filtered out and the amount of data with high-frequency noise can be reduced, thereby reducing the interference of noise on the data to a certain extent. The rotation speed of the propeller in the drone is adjusted according to the average delay time of drone logistics delivery. Since strong winds may be encountered during drone delivery, the flight speed of the drone is affected and the overall delivery time will be significantly delayed. By increasing the rotation speed of the propeller in the drone, the propeller can push more air, thereby generating greater thrust and more effectively generating the torque required to restore the drone to a stable posture, which is conducive to maintaining or increasing the flight speed, reducing delivery time delays, and improving the stability of drone logistics delivery task planning.

[0016] Furthermore, the method of the present invention adjusts the data transmission rate of the positioning sensor by setting a preset first distance and a preset second distance. Since the internal electronic components of the sensors in the drone gradually age after long-term use, the accuracy of the sensors decreases, resulting in inaccurate positioning. By increasing the data transmission rate of the positioning sensor, the positioning sensor can transmit the collected data to the processing unit more quickly, allowing the drone control system to adjust its position and correct its flight attitude more quickly based on the new data, further improving the mission planning stability of the drone logistics distribution.

[0017] Furthermore, the method of the present invention adjusts the frequency of collecting logistics data by setting a preset first error rate and a preset second error rate. Since the collected data may contain noise, the planning algorithm needs to spend extra time to deal with these problems. The algorithm may also obtain erroneous results due to data quality issues, affecting the normal operation of the algorithm. By reducing the frequency of collecting logistics data, some high-frequency noise can be filtered out and the amount of data with high-frequency noise can be reduced, thereby reducing the interference of noise on the data to a certain extent, and further improving the mission planning stability of drone logistics distribution.

[0018] Furthermore, the method of the present invention adjusts the rotation speed of the propeller in the drone by setting a preset delay time. Since strong winds may be encountered during the drone delivery process, the flight speed of the drone will be affected and the overall delivery time will be significantly delayed. By increasing the rotation speed of the propeller in the drone, the propeller can push more air, thereby generating greater thrust and more effectively generating the torque required to restore the drone to a stable posture, which is conducive to maintaining or increasing the flight speed, reducing delivery time delays, and further improving the mission planning stability of drone logistics delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is an overall flow chart of an intelligent task planning method for drone logistics delivery according to an embodiment of the present invention; Figure 2 It is a logic flow chart of an intelligent task planning method for unmanned aerial vehicle logistics distribution according to an embodiment of the present invention; Figure 3 A specific flow chart of the process of adjusting the data transmission rate of the positioning sensor in the intelligent task planning method for unmanned aerial vehicle logistics distribution according to an embodiment of the present invention; Figure 4 The present invention is a specific flow chart of the process of adjusting the frequency of collecting logistics data in the intelligent task planning method for unmanned aerial vehicle logistics distribution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively an overall flow chart of an intelligent task planning method for unmanned aerial vehicle logistics delivery according to an embodiment of the present invention, a logic flow chart, a specific flow chart of a process for adjusting the data transmission rate of a positioning sensor, and a specific flow chart of a process for adjusting the collection frequency of logistics data. An intelligent task planning method for unmanned aerial vehicle logistics delivery according to the present invention comprises: Step S1, respectively collecting environmental data and logistics data in real time, analyzing the task requirements of logistics distribution, generating a distribution task list according to the task requirements, and assigning the distribution tasks to a number of drones; Step S2, using a positioning sensor to determine the position of the drone, using a path planning algorithm to determine the flight path of the drone, and optimizing the path and task allocation of each drone in real time according to actual environmental changes; Step S3, respectively obtaining the position of a single UAV and the predetermined position of the UAV; Step S4, determining the mission planning stability of the UAV logistics delivery based on the straight-line distance between the UAV's position and the predetermined position; Step S5, if the mission planning stability does not meet the requirements, the data transmission rate of the positioning sensor is adjusted, or, based on the error rate of the UAV path planning, it is determined whether the robustness of the mission planning meets the requirements; Step S6: If the robustness does not meet the requirements, the frequency of collecting logistics data is adjusted, or the rotation speed of the propeller in the drone is adjusted based on the average delay time of the drone logistics delivery.

[0023] Specifically, environmental data includes temperature, humidity, and wind speed.

[0024] Specifically, logistics data includes cargo demand, drone status, and traffic conditions.

[0025] Specifically, the task requirements of logistics distribution include the quantity of goods, the delivery location of goods, and the delivery time of goods.

[0026] Specifically, the delivery task list includes the identifier of each delivery task, the creation time of the delivery task, and the delivery information of the goods.

[0027] Specifically, path planning algorithms include Dijkstra algorithm, A* algorithm, and ant colony algorithm.

[0028] Specifically, the data transmission rate of the positioning sensor is adjusted by increasing the sampling frequency of the positioning sensor or optimizing the data transmission protocol (such as using a more efficient communication protocol).

[0029] Specifically, the rotation speed of the propeller in the drone is regulated by increasing the input voltage of the motor or adjusting the duty cycle of the PWM signal to increase the motor speed.

[0030] Specifically, part of the high frequency noise is filtered through a low pass filter.

[0031] Specifically, robustness is measured by the error rate of drone path planning. By comparing the error rates before and after adjustment, it can be proved whether the accuracy has been improved. If the error rate is significantly reduced, it means that the accuracy has been improved.

[0032] In implementation, the method of the present invention adjusts the data transmission rate of the positioning sensor according to the straight-line distance between the position of the drone and the predetermined position. Since the internal electronic components of the sensors in the drone gradually age after long-term use, the accuracy of the sensors decreases, resulting in inaccurate positioning. By increasing the data transmission rate of the positioning sensor, the positioning sensor can transmit the collected data to the processing unit more quickly, allowing the drone control system to adjust the position and correct the flight attitude according to the new data more quickly. The collection frequency of logistics data is adjusted according to the error rate of the drone path planning. Since the collected data may contain noise, the planning algorithm needs to spend extra time to deal with these problems, and the algorithm may also obtain erroneous results due to data quality problems. , affecting the normal operation of the algorithm. By reducing the collection frequency of logistics data, some high-frequency noise can be filtered out and the amount of data with high-frequency noise can be reduced, thereby reducing the interference of noise on the data to a certain extent. The rotation speed of the propeller in the drone is adjusted according to the average delay time of drone logistics delivery. Since strong winds may be encountered during drone delivery, the flight speed of the drone is affected and the overall delivery time will be significantly delayed. By increasing the rotation speed of the propeller in the drone, the propeller can push more air, thereby generating greater thrust and more effectively generating the torque required to restore the drone to a stable posture, which is conducive to maintaining or increasing the flight speed, reducing delivery time delays, and improving the mission planning stability of drone logistics delivery.

[0033] Specifically, determining the mission planning stability of the UAV logistics delivery includes: Obtaining the distance between the actual center of gravity position of a single UAV and the predetermined center of gravity position of the UAV; Comparing the straight-line distance between the position of the drone and the predetermined position with a preset first distance; If the straight-line distance between the position of the drone and the predetermined position is greater than the preset first distance, it is determined that the mission planning stability of the drone logistics delivery does not meet the requirements.

[0034] Specifically, the robustness of the mission planning is determined, including: Comparing the straight-line distance between the position of the drone and the predetermined position with the preset first distance and the preset second distance respectively; If the straight-line distance between the position of the UAV and the predetermined position is greater than the preset first distance and less than or equal to the preset second distance, it is preliminarily determined that the robustness of the mission planning does not meet the requirements, and whether the robustness of the mission planning meets the requirements is determined based on the error rate of the UAV path planning.

[0035] It can be understood that the three intervals divided by the preset first distance and the preset second distance correspond to three situations respectively: The first interval is when the straight-line distance between the position of the drone and the predetermined position is less than or equal to the preset first distance, and the corresponding situation is: determining that the stability of the mission planning of the drone logistics delivery meets the requirements; The second interval is when the straight-line distance between the drone's position and the predetermined position is greater than the preset first distance and less than or equal to the preset second distance. The corresponding situation is: due to the possible noise in the collected data, the planning algorithm needs to spend extra time to deal with these problems. It is also possible that the algorithm obtains wrong results due to data quality problems, affecting the normal operation of the algorithm. The third interval is when the straight-line distance between the drone's position and the predetermined position is greater than the preset second distance. The corresponding situation is: due to the gradual aging of the internal electronic components of the drone's sensors after long-term use, the accuracy of the sensors decreases, resulting in inaccurate positioning.

[0036] In practice, the preset first distance is generally selected in the range of [0.4m, 0.6m], and the preset second distance is generally selected in the range of [0.7m, 0.9m].

[0037] Preferably, the preferred embodiment of the preset first distance is 0.5 m, and the preferred embodiment of the preset second distance is 0.8 m.

[0038] Specifically, the straight-line distance between the position of the drone and the predetermined position is the distance between the actual center of gravity position of a single drone and the predetermined center of gravity position of the drone.

[0039] In implementation, the method of the present invention determines the mission planning stability of UAV logistics delivery by setting a preset first distance and a preset second distance, thereby reducing the impact of the decreased accuracy of mission planning for UAV logistics delivery due to inaccurate determination of the mission planning stability for UAV logistics delivery, and further improving the mission planning stability for UAV logistics delivery.

[0040] Specifically, adjusting the data transmission rate of the positioning sensor includes: Comparing the straight-line distance between the position of the drone and the predetermined position with the preset second distance; If the straight-line distance between the position of the drone and the predetermined position is greater than a preset second distance, the data transmission rate of the positioning sensor is increased.

[0041] Specifically, the increase range of the data transmission rate of the positioning sensor is determined according to the difference between the straight-line distance between the position of the drone and the predetermined position and the preset second distance.

[0042] Specifically, when the difference between the straight-line distance between the drone's position and the predetermined position and the preset second distance is within 0.3m, the data transmission rate of the positioning sensor increases to 1.1 times the original value; when the difference between the straight-line distance between the drone's position and the predetermined position and the preset second distance exceeds 0.3m, on the basis of being increased to 1.1 times the original value, the data transmission rate of the positioning sensor increases by 50Kbps for every 0.1m exceeding. For example, the difference between the straight-line distance between the drone's position and the predetermined position and the preset second distance is 0.5m. The current data transmission rate of the positioning sensor is 200Kbps, and the increased data transmission rate of the positioning sensor is 200×1.1+50×2=320Kbps.

[0043] In implementation, the method of the present invention adjusts the data transmission rate of the positioning sensor by setting a preset first distance and a preset second distance. Since the internal electronic components of the sensors in the drone gradually age after long-term use, the accuracy of the sensors decreases, resulting in inaccurate positioning. By increasing the data transmission rate of the positioning sensor, the positioning sensor can transmit the collected data to the processing unit more quickly, allowing the drone control system to adjust the position and correct the flight attitude more quickly according to the new data, further improving the mission planning stability of the drone logistics distribution.

[0044] Specifically, the frequency of collecting the logistics data is adjusted, including: The number of UAV path planning errors and the total number of path planning are obtained respectively, and the error rate of UAV path planning is calculated; Comparing the error rate of the drone path planning with a preset first error rate and a preset second error rate respectively; If the error rate of the drone path planning is greater than the preset first error rate, it is determined that the robustness of the mission planning does not meet the requirements; If the error rate of the drone path planning is greater than the preset first error rate and less than or equal to the preset second error rate, reducing the frequency of collecting logistics data; If the error rate of the drone path planning is greater than the preset second error rate, it is preliminarily determined that the environmental stability of the task planning does not meet the requirements, and whether the environmental stability of the task planning meets the requirements is determined based on the average delay time of the drone logistics delivery.

[0045] It can be understood that the three intervals divided by the preset first error rate and the preset second error rate correspond to three situations respectively: The first interval is that the error rate of the drone path planning is less than or equal to the preset first error rate, and the corresponding situation is: determining that the robustness of the mission planning meets the requirements; The second interval is when the error rate of the drone path planning is greater than the preset first error rate and less than or equal to the preset second error rate. The corresponding situation is: due to the possible noise in the collected data, the planning algorithm needs to spend extra time to deal with these problems, and the algorithm may obtain wrong results due to data quality problems, affecting the normal operation of the algorithm; The third interval is when the error rate of drone path planning is greater than the preset second error rate. The corresponding situation is: due to strong winds that may be encountered during the drone delivery process, the flight speed of the drone is affected and the overall delivery time will be significantly delayed.

[0046] In practice, the preset first error rate is generally selected in the range of [1%, 3%], and the preset second error rate is generally selected in the range of [4%, 6%].

[0047] Preferably, the preferred embodiment of the preset first error rate is 2%, and the preferred embodiment of the preset second error rate is 5%.

[0048] In implementation, the method of the present invention determines the robustness of task planning by setting a preset first error rate and a preset second error rate, thereby reducing the impact of the decreased stability of task planning for drone logistics delivery due to inaccurate determination of the robustness of task planning, and further improving the stability of task planning for drone logistics delivery.

[0049] Specifically, the error rate of the drone path planning is the ratio of the number of drone path planning errors to the total number of path planning errors.

[0050] Specifically, the reduction degree of the collection frequency of the logistics data is determined according to the difference between the error rate of the drone path planning and the preset first error rate.

[0051] Specifically, when the difference between the error rate of UAV path planning and the preset first error rate is within 2%, the frequency of logistics data collection is reduced to 0.9 times the original; when the difference between the error rate of UAV path planning and the preset first error rate exceeds 2%, on the basis of being reduced to 0.9 times the original, the frequency of logistics data collection is reduced by 1 time / second for every 1% exceeding it. For example, the difference between the error rate of UAV path planning and the preset first error rate is 4%, the current frequency of logistics data collection is 10 times / second, and the frequency of logistics data collection after reduction is 10×0.9-1×2=7 times / second.

[0052] In implementation, the method of the present invention adjusts the collection frequency of logistics data by setting a preset first error rate and a preset second error rate. Since the collected data may contain noise, the planning algorithm needs to spend extra time to deal with these problems. The algorithm may also obtain erroneous results due to data quality issues, affecting the normal operation of the algorithm. By reducing the collection frequency of logistics data, some high-frequency noise can be filtered out and the amount of data with high-frequency noise can be reduced, thereby reducing the interference of noise on data to a certain extent and further improving the mission planning stability of drone logistics distribution.

[0053] Specifically, the rotation speed of the propeller in the drone is adjusted, including: Obtain the delay time of several drone logistics deliveries in the same environment and at the same distance, and calculate the average delay time of drone logistics deliveries; Comparing the average delay time of the drone logistics delivery with the preset delay time; If the average delay time of the drone logistics delivery is greater than the preset delay time, it is determined that the environmental stability of the mission planning does not meet the requirements, and the rotation speed of the propeller in the drone is increased.

[0054] It can be understood that the two intervals divided by the preset delay time correspond to two situations: The first interval is when the average delay of drone logistics delivery is less than or equal to the preset delay, and the corresponding situation is: it is determined that the environmental stability of the mission planning meets the requirements; The second interval is when the average delay time of drone logistics delivery is greater than the preset delay time. The corresponding situation is: due to strong winds that may be encountered during the drone delivery process, the flight speed of the drone is affected and the overall delivery time will be significantly delayed.

[0055] In practice, the preset delay time is generally selected in the range of [9min, 11min].

[0056] Preferably, the preferred embodiment of the preset delay time length is 10 minutes.

[0057] Specifically, the average delay time of drone logistics delivery is the ratio of the total delay time of several drone logistics deliveries in the same environment and the same distance to the number of drones.

[0058] During implementation, the method of the present invention determines the environmental stability of task planning by setting a preset delay period, thereby reducing the impact of the decreased stability of task planning for drone logistics delivery due to inaccurate determination of the environmental stability of task planning, and further improving the stability of task planning for drone logistics delivery.

[0059] Specifically, the increase in the rotation speed of the propeller in the drone is determined according to the difference between the average delay time of the drone logistics delivery and the preset delay time.

[0060] Specifically, when the difference between the average delay time of drone logistics delivery and the preset delay time is within 3 minutes, the rotation speed of the propeller in the drone is increased to 1.2 times the original speed; when the difference between the average delay time of drone logistics delivery and the preset delay time exceeds 3 minutes, on the basis of increasing to 1.2 times the original speed, the rotation speed of the propeller in the drone is increased by 500r / min for every 2 minutes exceeding the delay time. For example, the difference between the average delay time of drone logistics delivery and the preset delay time is 7 minutes. The current rotation speed of the propeller in the drone is 5000r / min. The increased rotation speed of the propeller in the drone is 5000r / min.

[0061] In implementation, the method of the present invention adjusts the rotation speed of the propeller in the drone by setting a preset delay time. Since strong winds may be encountered during drone delivery, the flight speed of the drone will be affected and the overall delivery time will be significantly delayed. By increasing the rotation speed of the propeller in the drone, the propeller can push more air, thereby generating greater thrust and more effectively generating the torque required to restore the drone to a stable posture, which is conducive to maintaining or increasing the flight speed, reducing delivery time delays, and further improving the mission planning stability of drone logistics delivery.

[0062] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent task planning method for drone logistics distribution, characterized in that: include: Collect environmental data and logistics data in real time, analyze the task requirements of logistics distribution, generate a distribution task list according to the task requirements, and assign the distribution tasks to several drones; Use positioning sensors to determine the position of the drone, use path planning algorithms to determine the flight path of the drone, and optimize the path and task allocation of each drone in real time according to actual environmental changes; Get the location of a single drone and the predetermined location of the drone respectively; Determine the stability of the mission planning of UAV logistics delivery based on the straight-line distance between the UAV's position and the predetermined location; If the mission planning stability does not meet the requirements, the data transmission rate of the positioning sensor is adjusted, or, based on the error rate of the UAV path planning, it is determined whether the robustness of the mission planning meets the requirements; If the robustness does not meet the requirements, the frequency of logistics data collection is adjusted, or the rotation speed of the propeller in the drone is adjusted based on the average delay time of the drone logistics delivery.

2. The intelligent task planning method for drone logistics distribution according to claim 1 is characterized in that: Determine the mission planning stability of the UAV logistics delivery, including: Comparing the straight-line distance between the position of the drone and the predetermined position with a preset first distance; If the straight-line distance between the position of the drone and the predetermined position is greater than the preset first distance, it is determined that the mission planning stability of the drone logistics delivery does not meet the requirements.

3. The intelligent task planning method for drone logistics distribution according to claim 2 is characterized in that: Determining the robustness of the mission plan, including: Comparing the straight-line distance between the position of the drone and the predetermined position with the preset first distance and the preset second distance respectively; If the straight-line distance between the position of the UAV and the predetermined position is greater than the preset first distance and less than or equal to the preset second distance, it is preliminarily determined that the robustness of the mission planning does not meet the requirements, and whether the robustness of the mission planning meets the requirements is determined based on the error rate of the UAV path planning.

4. The intelligent task planning method for drone logistics distribution according to claim 3 is characterized in that: The data transmission rate of the positioning sensor is adjusted, including: Comparing the straight-line distance between the position of the drone and the predetermined position with the preset second distance; If the straight-line distance between the position of the drone and the predetermined position is greater than a preset second distance, the data transmission rate of the positioning sensor is increased.

5. The intelligent task planning method for drone logistics distribution according to claim 4 is characterized in that: The increase range of the data transmission rate of the positioning sensor is determined according to the difference between the straight-line distance between the position of the drone and the predetermined position and the preset second distance.

6. The intelligent task planning method for drone logistics distribution according to claim 5 is characterized in that: The frequency of collecting the logistics data is adjusted, including: Compare the error rate of the drone path planning with the preset first error rate and the preset second error rate respectively; If the error rate of the drone path planning is greater than the preset first error rate, it is determined that the robustness of the mission planning does not meet the requirements; If the error rate of the drone path planning is greater than the preset first error rate and less than or equal to the preset second error rate, reducing the frequency of collecting logistics data; If the error rate of the drone path planning is greater than the preset second error rate, it is preliminarily determined that the environmental stability of the task planning does not meet the requirements, and whether the environmental stability of the task planning meets the requirements is determined based on the average delay time of the drone logistics delivery.

7. The intelligent task planning method for drone logistics distribution according to claim 6 is characterized in that: The error rate of the drone path planning is the ratio of the number of drone path planning errors to the total number of path planning errors.

8. The intelligent task planning method for drone logistics distribution according to claim 7 is characterized in that: The reduction degree of the collection frequency of the logistics data is determined according to the difference between the error rate of the drone path planning and the preset first error rate.

9. The intelligent task planning method for drone logistics distribution according to claim 8 is characterized in that: Adjust the rotation speed of the propeller in the drone, including: Compare the average delay time of drone logistics delivery with the preset delay time; If the average delay time of the drone logistics delivery is greater than the preset delay time, it is determined that the environmental stability of the mission planning does not meet the requirements, and the rotation speed of the propeller in the drone is increased.

10. The intelligent task planning method for drone logistics distribution according to claim 9 is characterized in that: The increase in the rotation speed of the propeller in the drone is determined according to the difference between the average delay time of the drone logistics delivery and the preset delay time.

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