Unmanned transportation and investment route planning method and device based on multiple factors

Abstract

The invention belongs to the technical field of unmanned aerial vehicles, and particularly discloses an unmanned transportation and delivery route planning method and device based on multiple factors, and the method comprises the steps: determining the task demand information of a transportation and delivery task; performing unmanned aerial vehicle matching according to the task demand information to obtain an unmanned aerial vehicle score, and selecting a target unmanned aerial vehicle according to the unmanned aerial vehicle score to execute the transportation and delivery task; obtaining a target path by using a path planning algorithm, and scoring the target path to obtain a target path score; and determining an environmental factor score according to the path voyage and the path points, and performing weighted calculation on the unmanned aerial vehicle score, the target path score and the environmental factor score to obtain a comprehensive score of the target path for reference by a decision maker. According to the invention, the accuracy and efficiency of route planning can be improved.

Description

Technical Field

[0001] The present application belongs to the field of unmanned aerial vehicle technology, and more specifically, relates to a method and device for unmanned delivery route planning based on multiple factors. Background Art

[0002] With the continuous maturity and rapid development of drone technology, drones have the advantages of convenience, speed and high speed. At present, drone applications are gaining more and more attention in the field of transportation. Modern times are in urgent need of safer and more efficient high-value urban logistics methods. Most of the volume of drones that can be used for urban logistics transportation is used to accommodate goods. They weigh only about 15kg and can reach a speed of 40km / h. The routes do not conflict with ground traffic. They can complete transportation tasks independently and are remotely monitored by professionals. They are suitable for completing transportation tasks within the city. To a certain extent, they can reduce the pressure of ground traffic, optimize the work structure of logistics delivery personnel, and meet the high-value, small-volume, and time-sensitive transportation needs of urban residents for express delivery, takeout, and medicines.

[0003] With the increasingly complex application environment and the increasing diversity of transportation and delivery needs, unmanned transportation and delivery business has been affected by more and more factors. Factors such as the restrictions on airspace management and the complexity of route planning have led to manual drone path planning not only being difficult and inefficient, but also having poor accuracy in path planning.

[0004] Therefore, how to improve the accuracy and efficiency of unmanned delivery route planning is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the defects of the prior art, the purpose of this application is to provide a method and device for unmanned delivery route planning based on multiple factors, aiming to solve the problems of difficult and inefficient path planning in unmanned delivery business.

[0006] In a first aspect, the present application provides a method for unmanned delivery route planning based on multiple factors, including:

[0007] Determine the mission requirement information of the delivery mission;

[0008] Matching drones according to the mission requirement information to obtain drone scores, and selecting target drones according to the drone scores to perform the delivery mission;

[0009] Obtaining a target path using a path planning algorithm, and scoring the target path to obtain a target path score;

[0010] The environmental factor score is determined according to the path distance and path points, and the drone score, target path score and environmental factor score are weighted to obtain a comprehensive score of the target path for reference by decision makers.

[0011] Optionally, obtaining the target path by using a path planning algorithm, and scoring the target path to obtain a target path score, includes:

[0012] Use the path planning algorithm to obtain the shortest and safest path for the delivery task;

[0013] Scoring the shortest path and the safest path respectively to obtain a shortest path score and a safest path score;

[0014] The weighted calculation of the drone score, the target path score and the environmental factor score includes:

[0015] The drone score, the shortest path score and the environmental factor score are weighted to obtain a comprehensive score for the shortest path, and the drone score, the safest path score and the environmental factor score are weighted to obtain a comprehensive score for the safest path.

[0016] Optionally, the mission requirement information includes the latitude and longitude of the starting point, the latitude and longitude of the end point, the material type, the material quantity and the material weight;

[0017] The step of matching drones according to the mission requirement information to obtain drone scores includes:

[0018] Select a matching drone type according to the material type;

[0019] According to the quantity and weight of supplies, matching drones with a load greater than the load requirement from the types of drones as candidate drones;

[0020] The maximum range of the commissioning mission is determined according to the starting point longitude and latitude, a drone with a range greater than the maximum range is selected from the candidate drones as the target drone, and the target drone is scored.

[0021] Optionally, the method for obtaining the shortest path score includes:

[0022] Bypassing the no-fly zone of the delivery mission and ignoring the dangerous area to perform path planning to obtain the shortest path;

[0023] The dangerous range of the dangerous area on the shortest path is determined, and the shortest path score is determined according to the ratio of the dangerous range to the shortest path.

[0024] Optionally, the method for obtaining the safest path score includes:

[0025] Path planning is performed by bypassing the no-fly zone and the dangerous area of ​​the delivery mission to obtain the safest path;

[0026] The safest path score is determined according to the ratio of the shortest path to the safest path.

[0027] Optionally, the environmental factors include weather conditions, wind speed, rainfall in one hour, and visibility;

[0028] Methods for obtaining environmental factor scores include:

[0029] Obtaining a weather condition score, a wind speed score, a one-hour rainfall score, and a visibility score on the target path;

[0030] The weather condition score, wind speed score, one-hour rainfall score and visibility score are combined to obtain the environmental factor score.

[0031] Optionally, the target path planning method includes:

[0032] Read the map to be planned and initialize the algorithm parameters to determine the initial position and target position of the drone;

[0033] Create a path set, an OPEN list of plannable path sets, and a CLOSE list of closed sets. Initially, the OPEN list includes the initial position of the drone, and the CLOSE list is empty.

[0034] Checking the nodes in the OPEN list and selecting the minimum node in the OPEN list according to the cost function;

[0035] The selected minimum node is deleted from the OPEN list and added to the CLOSE list to determine whether it is the same as the target position. If it is the same, the iteration ends. If it is different, the OPEN list continues to be expanded with the current node as the new starting position.

[0036] Output the set of planned paths in the CLOSE list, detect whether there are obstacles in the path, if so, continue to detect, if there are no obstacles, delete redundant nodes and perform path smoothing, until the path detection is completed to obtain the target path.

[0037] The present application also provides an unmanned delivery route planning device based on multiple factors, including:

[0038] Task determination module, used to determine the task requirement information of the transportation task;

[0039] A drone scoring module is used to match drones according to the task requirement information to obtain drone scores, and select a target drone according to the drone scores to perform the delivery task;

[0040] A path scoring module is used to obtain a target path using a path planning algorithm, and to score the target path to obtain a target path score;

[0041] The comprehensive scoring module is used to determine the environmental factor score according to the path distance and path points, and to perform weighted calculation on the drone score, the target path score and the environmental factor score to obtain the comprehensive score of the target path for reference by decision makers.

[0042] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0044] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0045] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0046] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0047] (1) This application uses a path planning algorithm to obtain the target path, which can effectively avoid obstacles and adverse environmental factors, ensuring the safety and efficiency of the drone when performing tasks; by weighted calculation of the drone score, target path score and environmental factor score, it can comprehensively evaluate the feasibility and superiority of the target path. Through a systematic scoring and matching process, it can quickly screen out the most suitable drone and path, reducing manual intervention and decision-making time, and improving the accuracy and efficiency of path planning.

[0048] (2) This application can be dynamically adjusted according to different mission requirements and environmental changes, enhancing the adaptability of drones in complex environments. Through quantitative scoring and comprehensive evaluation, decision makers can make more appropriate choices based on data, thereby improving the success rate of mission execution and resource utilization efficiency.

[0049] (3) This application selects matching drone types based on the types of materials to ensure that the selected drones have the functions and characteristics suitable for transporting specific materials. It selects candidate drones with a load capacity greater than the required load capacity based on the quantity and weight of the materials to ensure that the selected drones can complete the transportation mission safely and effectively. It determines the maximum range and selects drones with a range greater than the maximum range from the candidate drones to ensure that the drones can complete the mission without restrictions and avoid mission failure due to insufficient range. Finally, the process of scoring the target drones can quantify the performance and adaptability of the drones. Through a systematic matching and scoring process, it can effectively reduce the risk of path planning and mission execution failure due to drone planning errors, thereby improving the success rate and efficiency of path planning, and further improving the success rate and efficiency of the overall transportation mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of a multi-factor based unmanned delivery route planning method provided in an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of the process of establishing a route planning model provided in an embodiment of the present application;

[0052] Figure 3 It is a flowchart of a process for deleting redundant nodes provided in an embodiment of the present application;

[0053] Figure 4 is a flow chart of the variable weight A* algorithm provided in the embodiment of the present application;

[0054] Figure 5 This is a complete framework diagram of a multi-factor based unmanned delivery route planning method provided by this application;

[0055] Figure 6 It is a structural schematic diagram of an unmanned delivery route planning device based on multiple factors provided in an embodiment of the present application;

[0056] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0059] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0060] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0061] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0062] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0063] The present application provides a method for unmanned delivery route planning based on multiple factors, including:

[0064] S101. Determine the task requirement information of the transport task;

[0065] S102. Matching drones according to the mission requirement information to obtain drone scores, and selecting target drones to perform the delivery mission according to the drone scores;

[0066] S103. Obtain the target path using the path planning algorithm, and score the target path to obtain a target path score;

[0067] S104. Determine the environmental factor score based on the path distance and path points, perform weighted calculation on the drone score, the target path score, and the environmental factor score, and obtain a comprehensive score of the target path for reference by decision makers.

[0068] Specifically, in the above S101, the specific requirements of the delivery mission are first collected and analyzed, including the type, quantity, weight, and location information of the starting and ending points of the materials. Clarifying these basic requirements is the prerequisite for successfully selecting a drone to ensure that the drone's transportation capacity is consistent with the mission requirements.

[0069] Then, based on the collected mission requirement information, the system will scan available drone resources through S102 to evaluate the matching degree between each drone and the mission. The drone scoring process will consider multiple factors, such as: whether the drone's load capacity meets the weight requirements of the materials; whether the type of drone matches the type of materials (such as medicines, food, heavy equipment, etc.); whether the drone's range can meet the mission requirements (can perform a single mission or a round-trip mission, etc.); through compatibility scoring, a target drone is finally selected to improve the efficiency and safety of the delivery mission.

[0070] Further, through S103, the target path is obtained by using the path planning algorithm, and the target path is scored. After the target UAV is selected, the path planning algorithm is used to generate the optimal path for it. The algorithm will consider the following factors: obstacles along the way, terrain characteristics, space restrictions, etc. The path scoring will evaluate the feasibility and efficiency of the generated path to ensure that the UAV can safely reach the destination in the shortest time.

[0071] Finally, S104 determines the environmental factor score based on the path distance and path points. The drone score, path score and environmental factor score are comprehensively evaluated. The environmental factor score takes into account the potential impact of external conditions such as weather, wind speed, humidity, etc. on drone flight. All scores are weighted to obtain a comprehensive score of the target path, which provides a valuable reference for decision makers, helps to select the optimal strategy, and improves the overall implementation effect of the task.

[0072] This application obtains the target path by utilizing a path planning algorithm, which can effectively avoid obstacles and adverse environmental factors, and ensure the safety and efficiency of the drone when performing tasks; through weighted calculation of the drone score, the target path score and the environmental factor score, the feasibility and superiority of the target path can be comprehensively evaluated, and through a systematic scoring and matching process, the most suitable drone and path can be quickly screened out, reducing manual intervention and decision-making time, and improving the accuracy and efficiency of path planning.

[0073] Optionally, the mission requirement information includes the latitude and longitude of the starting point, the latitude and longitude of the end point, the material type, the material quantity and the material weight;

[0074] The step of matching drones according to the mission requirement information to obtain drone scores includes:

[0075] Select a matching drone type according to the material type;

[0076] According to the quantity and weight of supplies, matching drones with a greater load-carrying capacity from the types of drones as candidate drones;

[0077] The maximum range of the commissioning mission is determined according to the starting point longitude and latitude, a drone with a range greater than the maximum range is selected from the candidate drones as the target drone, and the target drone is scored.

[0078] Specifically, in this embodiment, a suitable drone is selected according to the details of the mission requirements. According to the type of transported materials, a matching drone type is selected; according to the weight of the materials, a drone with a greater load capacity is selected; according to the starting point of the transport, the required maximum range is calculated, and according to this range, a drone with a greater range is selected from suitable drones; finally, the drone matching degree is scored, and the most suitable drone is selected according to the score. The scoring calculation formula is as follows:

[0079]

[0080] Among them, P1 is the drone matching score, L d is the maximum range of the drone, L n is the route distance; W d is the maximum payload of the drone, W n It's the cargo load.

[0081] Optionally, obtaining the target path by using a path planning algorithm, and scoring the target path to obtain a target path score, includes:

[0082] Use the path planning algorithm to obtain the shortest and safest path for the delivery task;

[0083] Scoring the shortest path and the safest path respectively to obtain a shortest path score and a safest path score;

[0084] The weighted calculation of the drone score, the target path score and the environmental factor score includes:

[0085] The drone score, the shortest path score and the environmental factor score are weighted to obtain a comprehensive score for the shortest path, and the drone score, the safest path score and the environmental factor score are weighted to obtain a comprehensive score for the safest path.

[0086] Optionally, the method for obtaining the shortest path score includes:

[0087] Bypassing the no-fly zone of the delivery mission and ignoring the dangerous area to perform path planning to obtain the shortest path;

[0088] The dangerous range of the dangerous area on the shortest path is determined, and the shortest path score is determined according to the ratio of the dangerous range to the shortest path.

[0089] Optionally, the method for obtaining the safest path score includes:

[0090] Path planning is performed by bypassing the no-fly zone and the dangerous area of ​​the delivery mission to obtain the safest path;

[0091] The safest path score is determined according to the ratio of the shortest path to the safest path.

[0092] Furthermore, according to the no-fly zones and danger zones, the improved A* algorithm is used to calculate the shortest path and the safest path to complete the task, and the paths are scored.

[0093] It should be noted that the improved A* algorithm in this embodiment is a path search algorithm optimized on the basis of the traditional A algorithm, aiming to improve search efficiency and path quality. The key elements of the improved A* algorithm may include: heuristic function optimization, node expansion strategy, path smoothing, dynamic obstacle processing, multi-objective optimization, and memorized search.

[0094] The specific scoring rules are as follows:

[0095] Shortest path: bypass the no-fly zone, ignore the danger zone to plan the path, and calculate the path distance L duan After that, calculate the path distance L in the danger zone wei , the shortest path solution score is P duan , the calculation formula is:

[0096]

[0097] The safest path: Plan the path around no-fly zones and dangerous areas and calculate the path distance L an , the safest path solution score is P an , the calculation formula is:

[0098]

[0099] Optionally, the environmental factors include weather conditions, wind speed, rainfall in one hour, and visibility;

[0100] The methods for obtaining environmental factor scores include:

[0101] Obtaining a weather condition score, a wind speed score, a one-hour rainfall score, and a visibility score on the target path;

[0102] The weather condition score, wind speed score, one-hour rainfall score and visibility score are combined to obtain the environmental factor score.

[0103] Specifically, according to the calculated flight distance and path points, the weather conditions are checked and the environment is scored; the environmental scoring criteria are composed of four elements: weather conditions, wind speed, one-hour rainfall, and visibility. The calculation formula is as follows:

[0104] P tian =P qing +P feng +P yu +P jian

[0105] Among them, P qing , P feng , P yu , P jian They are weather conditions, wind speed, one-hour rainfall, and visibility score, with the maximum score for each factor being 25%.

[0106] Finally, the comprehensive score is calculated based on the weighted calculation of the above scores. The comprehensive score calculation formula is:

[0107]

[0108] Among them, P lujing L duan , L an Obtain comprehensive scores for different paths for reference by decision makers.

[0109] This application can comprehensively evaluate the flight conditions of drones on a specific path by taking into account a variety of environmental factors to ensure the scientific nature of the decision. Environmental factor scoring can help decision makers identify potential flight risks, adjust mission plans in a timely manner, and reduce the possibility of accidents. In path planning, the comprehensive environmental factor score can serve as an important reference to improve the accuracy and efficiency of drone route planning, thereby increasing the success rate of the mission.

[0110] Optionally, the target path planning method includes:

[0111] Read the map to be planned and initialize the algorithm parameters to determine the initial position and target position of the drone;

[0112] Create a path set, an OPEN list of plannable path sets, and a CLOSE list of closed sets. Initially, the OPEN list includes the initial position of the drone, and the CLOSE list is empty.

[0113] Checking the nodes in the OPEN list and selecting the minimum node in the OPEN list according to the cost function;

[0114] The selected minimum node is deleted from the OPEN list and added to the CLOSE list to determine whether it is the same as the target position. If it is the same, the iteration ends. If it is different, the OPEN list continues to be expanded with the current node as the new starting position.

[0115] Output the set of planned paths in the CLOSE list, detect whether there are obstacles in the path, if so, continue to detect, if there are no obstacles, delete redundant nodes and perform path smoothing, until the path detection is completed to obtain the target path.

[0116] Specifically, refer to Figure 2 , set the grid map, and select several existing routes as route obstacles in the area. Since the rectangular coordinates have large values, they affect the convergence speed and accuracy of the model, and the storage space will double accordingly. Therefore, the coordinate values ​​are normalized, and the boundary coordinates are introduced into the corresponding flight position coordinates, so that all coordinate values ​​are limited to [0, 1].

[0117] Since the heuristic function and cost function of the traditional A* algorithm have the same weight, the drone search time is relatively long. Different drone delivery strategies will lead to different drone path planning results.

[0118] In the A* algorithm, the heuristic function and the cost function are calculated using Euclidean distance. Since the square root calculation cost is relatively high, this paper improves the Euclidean distance to Manhattan distance for calculation. The purpose of this is to reduce the calculation cost. It can also maintain the same search method as the Euclidean distance, which increases the calculation speed of the heuristic function and the cost function, thereby improving the calculation efficiency of the A* algorithm. The calculation formula is as follows:

[0119] g(n)=|(x n -x s )+(y n -y s )|

[0120] h(n)=|(x n -x g )+(y n -y g )|

[0121] g(n)=|(x n -x s )+(y n -y s )+(z n -z s )|

[0122] h(n)=|(x n -x g )+(y n -y g )+(z n -z g )|

[0123] Due to the different weights of the heuristic function and the cost function in the adaptive variable weight algorithm, the adaptive variable weight A* algorithm will have too many redundant nodes compared to the A* algorithm. Therefore, it is necessary to perform global secondary optimization on the path generated by the adaptive variable weight A* algorithm, refer to Figure 3 , the optimization process is as follows:

[0124] After taking the second derivative of the path point set, an inflection point set is generated. Starting from the starting position, it is determined whether there are obstacles between any consecutive points A, B, and C in the inflection point set. If there are no obstacles, the AC inflection points are directly connected. Then, it is determined whether there are obstacles between A, C, and D in the inflection point set. If there are no obstacles, the redundant node C is removed. If there are obstacles, the inflection point C is retained. From the starting point to the target point, it is determined in turn whether the inflection points can be deleted, and the inflection point set is generated as the final path.

[0125] Through the above description, we can get the overall process of the variable weight A* algorithm as follows, the flow chart is as follows Figure 4 As shown:

[0126] Step 1: Read the input map to be planned;

[0127] Step 2: Initialize algorithm parameters and determine the initial position and target position of the drone;

[0128] Step 3: Create a path set, an OPEN list of plannable path sets, and a CLOSE list of closed sets. At the beginning of the algorithm iteration, the OPEN list only contains the initial position of the drone, and the CLOSE list of closed sets is empty.

[0129] Step 4: Check the nodes stored in the OPEN list. If there is only the initial position in the list, it means that the drone has no feasible path. If there are other nodes in the path set OPEN list, use the cost function f(x) to select the minimum node in the OPEN set.

[0130] Step 5: Delete the minimum node in the cost function set from the OPEN set and add it to the CLOSE list to determine whether the point is the same as the target position. If the point is the same as the target position, it means that the iteration has reached the target point and the iteration process ends. If the current node is not the same as the target position, the current node is used as the current starting position, the OPEN list is continued to be expanded, the feasible path points of the drone are added to the CLOSE list, and the iteration process is continued in step 4.

[0131] Step 6: Output the planned path set in the CLOSE list, detect whether there are obstacles on the current path, and continue to detect if there are obstacles on the current path. If there are no obstacles on the current path, delete the redundant turning points, save the redundant node set after removal, and perform path smoothing.

[0132] Step 7: Output the final path.

[0133] Reference Figure 5 , Figure 5 This is a complete framework diagram of an unmanned delivery route planning method based on multiple factors provided by this application.

[0134] Includes: transportation demand details, drone matching, path planning algorithm, weather environment score, path score and comprehensive score;

[0135] The details of the transportation requirements include the latitude and longitude of the starting point, the longitude and longitude of the end point, the material type, the material quantity, and the material weight;

[0136] UAV matching includes range matching, payload matching and material type matching;

[0137] Path planning algorithms include obtaining the shortest and safest paths based on no-fly zones and danger zones;

[0138] The meteorological score is obtained based on the meteorological environment, the shortest path score is obtained based on the shortest path, the safest path score is obtained based on the safest path, and the matching score is obtained based on the drone matching results;

[0139] The above scores are combined to obtain a comprehensive path score.

[0140] Reference Figure 6 The present application also provides an unmanned delivery route planning device based on multiple factors, including:

[0141] Task determination module 610, used to determine task requirement information of the transport task;

[0142] A drone scoring module 620 is used to match drones according to the task requirement information to obtain drone scores, and select a target drone according to the drone scores to perform the delivery task;

[0143] A path scoring module 630 is used to obtain a target path using a path planning algorithm, and to score the target path to obtain a target path score;

[0144] The comprehensive scoring module 640 is used to determine the environmental factor score according to the path distance and the path points, and to perform weighted calculation on the drone score, the target path score and the environmental factor score to obtain the comprehensive score of the target path for reference by the decision maker.

[0145] Optionally, the path scoring module is specifically used for:

[0146] Use the path planning algorithm to obtain the shortest and safest path for the delivery task;

[0147] Scoring the shortest path and the safest path respectively to obtain a shortest path score and a safest path score;

[0148] The drone score, the shortest path score and the environmental factor score are weighted to obtain a comprehensive score for the shortest path, and the drone score, the safest path score and the environmental factor score are weighted to obtain a comprehensive score for the safest path.

[0149] Optionally, the mission requirement information includes the latitude and longitude of the starting point, the latitude and longitude of the end point, the material type, the material quantity and the material weight;

[0150] The drone scoring module is specifically used for:

[0151] Select a matching drone type according to the material type;

[0152] According to the quantity and weight of supplies, matching drones with a load greater than the load requirement from the types of drones as candidate drones;

[0153] The maximum range of the commissioning mission is determined according to the starting point longitude and latitude, a drone with a range greater than the maximum range is selected from the candidate drones as the target drone, and the target drone is scored.

[0154] Optionally, the method for obtaining the shortest path score includes:

[0155] Bypassing the no-fly zone of the delivery mission and ignoring the dangerous area to perform path planning to obtain the shortest path;

[0156] The dangerous range of the dangerous area on the shortest path is determined, and the shortest path score is determined according to the ratio of the dangerous range to the shortest path.

[0157] Optionally, the method for obtaining the safest path score includes:

[0158] Path planning is performed by bypassing the no-fly zone and the dangerous area of ​​the delivery mission to obtain the safest path;

[0159] The safest path score is determined according to the ratio of the shortest path to the safest path.

[0160] Optionally, the environmental factors include weather conditions, wind speed, rainfall in one hour, and visibility;

[0161] Methods for obtaining environmental factor scores include:

[0162] Obtaining a weather condition score, a wind speed score, a one-hour rainfall score, and a visibility score on the target path;

[0163] The weather condition score, wind speed score, one-hour rainfall score and visibility score are combined to obtain the environmental factor score.

[0164] Optionally, the target path planning method includes:

[0165] Read the map to be planned and initialize the algorithm parameters to determine the initial position and target position of the drone;

[0166] Create a path set, an OPEN list of plannable path sets, and a CLOSE list of closed sets. Initially, the OPEN list includes the initial position of the drone, and the CLOSE list is empty.

[0167] Checking the nodes in the OPEN list and selecting the minimum node in the OPEN list according to the cost function;

[0168] The selected minimum node is deleted from the OPEN list and added to the CLOSE list to determine whether it is the same as the target position. If it is the same, the iteration ends. If it is different, the OPEN list continues to be expanded with the current node as the new starting position.

[0169] Output the set of planned paths in the CLOSE list, detect whether there are obstacles in the path, if so, continue to detect, if there are no obstacles, delete redundant nodes and perform path smoothing, until the path detection is completed to obtain the target path.

[0170] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0171] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0172] Reference Figure 7 Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, which may include: a processor (processor) 710, a communication interface (Communications Interface) 720, a memory (memory) 730 and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call the logic instructions in the memory 730 to execute the method in the above embodiment.

[0173] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0174] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0175] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0176] It is understandable that the processor in the embodiment of the present application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0177] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0178] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0179] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0180] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A multi-factor based unmanned delivery route planning method, characterized in that: include: Determine the mission requirement information of the delivery mission; Matching drones according to the mission requirement information to obtain drone scores, and selecting target drones according to the drone scores to perform the delivery mission; Obtaining a target path using a path planning algorithm, and scoring the target path to obtain a target path score; The environmental factor score is determined according to the path distance and path points, and the drone score, target path score and environmental factor score are weighted to obtain a comprehensive score of the target path for reference by decision makers.

2. The unmanned delivery route planning method according to claim 1 is characterized in that: The method of obtaining a target path by using a path planning algorithm and scoring the target path to obtain a target path score includes: Use the path planning algorithm to obtain the shortest and safest path for the delivery task; Scoring the shortest path and the safest path respectively to obtain a shortest path score and a safest path score; The weighted calculation of the drone score, the target path score and the environmental factor score includes: The drone score, the shortest path score and the environmental factor score are weighted to obtain a comprehensive score for the shortest path, and the drone score, the safest path score and the environmental factor score are weighted to obtain a comprehensive score for the safest path.

3. The unmanned delivery route planning method according to claim 1 is characterized in that: The mission requirement information includes the starting point longitude and latitude, the end point longitude and latitude, the material type, the material quantity and the material weight; The step of matching drones according to the mission requirement information to obtain drone scores includes: Select a matching drone type according to the material type; According to the quantity and weight of supplies, matching drones with a greater load-carrying capacity from the types of drones as candidate drones; The maximum range of the commissioning mission is determined according to the starting point longitude and latitude, a drone with a range greater than the maximum range is selected from the candidate drones as the target drone, and the target drone is scored.

4. The unmanned delivery route planning method according to claim 2 is characterized in that: The method for obtaining the shortest path score includes: Bypassing the no-fly zone of the delivery mission and ignoring the dangerous area to perform path planning to obtain the shortest path; The dangerous range of the dangerous area on the shortest path is determined, and the shortest path score is determined according to the ratio of the dangerous range to the shortest path.

5. The unmanned delivery route planning method according to claim 2 is characterized in that: The method for obtaining the safest path score includes: Path planning is performed by bypassing the no-fly zone and the dangerous area of ​​the delivery mission to obtain the safest path; The safest path score is determined according to the ratio of the shortest path to the safest path.

6. The unmanned delivery route planning method according to claim 1 is characterized in that: The environmental factors include weather conditions, wind speed, rainfall in one hour, and visibility; Methods for obtaining environmental factor scores include: Obtaining a weather condition score, a wind speed score, a one-hour rainfall score, and a visibility score on the target path; The weather condition score, wind speed score, one-hour rainfall score and visibility score are combined to obtain the environmental factor score.

7. The unmanned delivery route planning method according to claim 1 is characterized in that: The target path planning method includes: Read the map to be planned and initialize the algorithm parameters to determine the initial position and target position of the drone; Create a path set, an OPEN list of plannable path sets, and a CLOSE list of closed sets. Initially, the OPEN list includes the initial position of the drone, and the CLOSE list is empty. Checking the nodes in the OPEN list and selecting the minimum node in the OPEN list according to the cost function; The selected minimum node is deleted from the OPEN list and added to the CLOSE list to determine whether it is the same as the target position. If it is the same, the iteration ends. If it is different, the OPEN list continues to be expanded with the current node as the new starting position. Output the set of planned paths in the CLOSE list, detect whether there are obstacles in the path, if so, continue to detect, if there are no obstacles, delete redundant nodes and perform path smoothing, until the path detection is completed to obtain the target path.

8. An unmanned delivery route planning device based on multiple factors, characterized in that: include: Task determination module, used to determine the task requirement information of the transportation task; A drone scoring module is used to match drones according to the task requirement information to obtain drone scores, and select a target drone according to the drone scores to perform the delivery task; A path scoring module is used to obtain a target path using a path planning algorithm, and to score the target path to obtain a target path score; The comprehensive scoring module is used to determine the environmental factor score according to the path distance and path points, and to perform weighted calculation on the drone score, the target path score and the environmental factor score to obtain the comprehensive score of the target path for reference by decision makers.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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