UAV dispatching method, system, readable storage medium and computer device

Abstract

The present invention provides a method, system, readable storage medium and computer equipment for scheduling unmanned aerial vehicles. The method includes dividing a flight area to obtain a plurality of spherical areas; designing link stability indicators of a plurality of unmanned aerial vehicles, obtaining link information transmission index values ​​between a plurality of unmanned aerial vehicles; optimizing a channel model; selecting corresponding links based on link information transmission index values ​​and the optimized channel model, obtaining a hypergraph link model between a plurality of unmanned aerial vehicles based on a disjoint multipath model, and constructing a conflict graph through the intersection relationship between a plurality of unmanned aerial vehicles; obtaining the weight of each flight path according to a plurality of paths in the hypergraph link model and the conflict graph, and scheduling a plurality of unmanned aerial vehicles based on an optimization algorithm and the weight of each flight path. The present invention can reduce the energy consumption of unmanned aerial vehicles, avoid conflicts in information transmission, and solve the problems of scheduling of unmanned aerial vehicle networks and scheduling mismatch of unmanned aerial vehicle network changes.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control technology, and in particular to a method, system, readable storage medium and computer equipment for dispatching unmanned aerial vehicles. Background Art

[0002] With the rapid development of emergency communication technology, drones have been widely used in many fields. With the advantages of high mobility, low cost and strong all-terrain adaptability, drones can flexibly enter disaster areas that humans could not enter before, speeding up rescue work and improving the efficiency of rescue work.

[0003] Due to the limited energy and computing power of a single drone, it is difficult to handle multiple rescue tasks at the same time, which affects the rescue work. Therefore, in order to overcome the above problems in the drone-assisted emergency communication network, a large number of small drones are coordinated to form a drone network to handle multiple rescue tasks at the same time, thereby accelerating the efficiency of post-disaster rescue.

[0004] In the existing technology, drones collect a large amount of information from the surrounding environment in real time through high-definition cameras or sensors, and analyze and process the collected information at the same time. This process will increase the energy consumption of drones, thereby reducing the drone's flight time. In addition, due to the high-speed mobility of drones, the topology of drone networks changes rapidly. Traditional routing and resource allocation solutions are difficult to meet the highly dynamic network topology and cannot provide real-time services for disaster-stricken personnel and rescue personnel. In addition, the resource scheduling decisions of drone networks are affected by post-disaster events, which will cause the current network topology state to not match, resulting in conflicts or collisions in multiple link transmission processes. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a drone scheduling method, system, readable storage medium and computer device to solve the deficiencies in the above-mentioned prior art.

[0006] In a first aspect, the present invention provides a method for dispatching a drone, the method comprising:

[0007] Dividing the flight area to obtain a plurality of spherical areas, wherein each of the spherical areas contains a plurality of drones, simulating movement trajectories of the plurality of drones in the plurality of spherical areas, and generating movement models of the plurality of drones based on the movement trajectories;

[0008] Based on the mobility model, obtaining distances between the plurality of drones and flight speeds of the plurality of drones, and obtaining channel models and link stability models of the plurality of drones;

[0009] Designing link stability indicators of the plurality of drones according to the distances between the plurality of drones and the flight speeds of the plurality of drones, and judging whether the link stability model meets the preset requirements based on the link stability indicators, and if so, obtaining link information transmission indicator values ​​between the plurality of drones;

[0010] Obtaining occupied bandwidths of several transmission paths in the channel model, estimating channel utilization of link transmission information of the channel model based on the occupied bandwidths, designing path stability optimization indicators of several transmission paths, and jointly optimizing the channel model based on the channel utilization and the stability optimization indicator;

[0011] Selecting corresponding links based on the link information transmission index value and the optimized channel model to perform information transmission between the plurality of drones in the plurality of spherical areas, and obtaining a disjoint multipath model of the plurality of drones in the plurality of spherical areas, obtaining a hypergraph link model between the plurality of drones based on the disjoint multipath model, and constructing a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones;

[0012] The weight of each flight path is obtained according to the multiple paths in the hypergraph link model and the conflict graph, and the plurality of drones in a plurality of spherical areas are scheduled based on the optimization algorithm and the weight of each flight path.

[0013] Compared with the prior art, the beneficial effects of the present invention are: by judging whether the link stability model meets the requirements by judging the link stability index, the link information transmission index value between drones is obtained, and the channel model is optimized by the channel utilization and stability optimization index, so as to obtain the optimal way of information transmission between drones, so as to reduce the energy consumption of drones, and avoid the problem of conflicts in multiple link transmission processes. By obtaining the weight of each flight path through the hypergraph link model and the conflict graph and passing through several spherical areas, the scheduling of a drone network composed of several drones can be met, thereby avoiding the problem of mismatch and difficulty in scheduling of drone network scheduling caused by changes in the drone network topology.

[0014] Furthermore, before the step of judging whether the link stability model meets preset requirements based on the link stability indicator, the method further includes:

[0015] Evaluating the link quality between a plurality of the drones in the adjacent spherical area by receiving signal strength;

[0016] The path stability of the links of several of the drones is evaluated based on the mobility model and the link quality.

[0017] Furthermore, the expression of the link quality is:

[0018]

[0019] In the formula, represents the link quality, represents the total number of samples of the received signal strength, Indicates a value of the received signal strength;

[0020] The expression of the path stability of the link is:

[0021]

[0022] In the formula, a value representing the path stability of the link, represents the first weight factor, represents the second weight factor, Indicates that in the spherical area Internal drone The flight speed of .

[0023] Furthermore, the calculation expression for estimating the channel utilization of the link transmission information of the channel model based on the occupied bandwidth is:

[0024]

[0025] In the formula, represents the channel utilization rate, Indicates the total bandwidth, represents the occupied bandwidth, Represents a spherical area , Indicates the number of transmission information samples, Indicates the sampling interval.

[0026] Furthermore, the step of obtaining a hypergraph link model between the plurality of drones based on the disjoint multipath model, and constructing a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones includes:

[0027] Obtaining inter-node paths of a plurality of the drones in the disjoint multi-path model, and generating hypergraph nodes based on the inter-node paths;

[0028] Generate a hypergraph link model between a number of the drones according to the hypergraph nodes;

[0029] Based on the inter-node paths of the several drones, it is determined whether the flight paths between the several drones intersect. If so, several intersection points are obtained, and a conflict graph is constructed according to the intersection points.

[0030] Furthermore, the step of obtaining the weight of each flight path according to the multiple paths in the hypergraph link model and the conflict graph includes:

[0031] Extracting the hyperedges in the hypergraph link model and the vertices of the conflict graph, and obtaining the weights of the hyperedges and the weights of the vertices;

[0032] The weight of each of the flight paths is obtained according to the weight of the hyperedge and the weight of the vertex.

[0033] Furthermore, the step of scheduling the plurality of drones in the plurality of spherical areas based on the optimization algorithm and the weight of each flight path includes:

[0034] Initializing a number of the drones in a number of the spherical areas, and determining initial flight directions of the drones;

[0035] Based on the initial flight direction and the weight of each flight path, the flight directions of the plurality of drones are updated in real time to obtain dispatch routes of the plurality of drones;

[0036] Evaluate the fitness of the scheduling route according to the flight speed and flight duration to obtain a task queue of the plurality of drones;

[0037] Based on the dispatch route of the drone, the flying position of the drone in all time slots is obtained, and the energy consumption of several drones is obtained in real time;

[0038] The task queue is optimized based on the locations where the drone flies in all time slots and the energy consumption of the drone.

[0039] In a second aspect, the present invention further provides a drone dispatching system, the system comprising:

[0040] A division module is used to divide the flight area into a plurality of spherical areas, wherein each of the plurality of spherical areas contains a plurality of drones, simulate the movement trajectories of the plurality of drones in the plurality of spherical areas, and generate movement models of the plurality of drones based on the movement trajectories;

[0041] An acquisition module, used for acquiring the distances between the plurality of drones and the flight speeds of the plurality of drones based on the mobility model, and acquiring the channel models and link stability models of the plurality of drones;

[0042] A design module, used for designing link stability indicators of the plurality of drones according to the distances between the plurality of drones and the flight speeds of the plurality of drones, and judging whether the link stability model meets the preset requirements based on the link stability indicators, and if so, obtaining link information transmission indicator values ​​between the plurality of drones;

[0043] An acquisition design module is used to acquire occupied bandwidths of several transmission paths in the channel model, estimate channel utilization of link transmission information of the channel model based on the occupied bandwidth, design path stability optimization indicators of several transmission paths, and jointly optimize the channel model based on the channel utilization and the stability optimization indicator;

[0044] A selection module is used to select a corresponding link based on the link information transmission index value and the optimized channel model to perform information transmission between the plurality of drones in the plurality of spherical areas, and obtain a disjoint multipath model of the plurality of drones in the plurality of spherical areas, obtain a hypergraph link model between the plurality of drones based on the disjoint multipath model, and construct a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones;

[0045] The scheduling module is used to obtain the weight of each flight path according to the multiple paths in the hypergraph link model and the conflict graph, and to schedule a number of the drones in a number of spherical areas based on the optimization algorithm and the weight of each flight path.

[0046] In a third aspect, the present invention further provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the drone scheduling method as described above.

[0047] In a fourth aspect, the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the drone scheduling method as described above when executing the computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of a UAV scheduling method in a first embodiment of the present invention;

[0049] Figure 2 is a path model diagram of the UAV in the first embodiment of the present invention, Figure 2 (a) a disjoint multipath model in the first embodiment of the present invention, Figure 2 (b) is a hypergraph link model in the first embodiment of the present invention;

[0050] Figure 3is a structural block diagram of a drone dispatching system in a second embodiment of the present invention;

[0051] Figure 4 It is a structural block diagram of a computer device in the third embodiment of the present invention.

[0052] Description of main component symbols:

[0053] 11. Divide module; 12. Obtain module; 13. Design module; 14. Obtain design module; 15. Select module; 16. Scheduling module;

[0054] 10. Memory; 20. Processor; 30. Computer program.

[0055] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0056] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0057] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0059] Embodiment 1

[0060] See also Figure 1 , which shows a UAV dispatching method in a first embodiment of the present invention, and the method includes steps S1 to S6:

[0061] S1, dividing the flight area to obtain a plurality of spherical areas, wherein each of the plurality of spherical areas contains a plurality of drones, simulating movement trajectories of the plurality of drones in the plurality of spherical areas, and generating movement models of the plurality of drones based on the movement trajectories;

[0062] It can be understood that by locating the disaster area through satellite, the flight area of ​​the drone can be obtained, and the flight area can be divided into several spherical areas. In addition, the spherical area where the drone aerial base station is located is used as the central area. The drone aerial base station can provide network coverage and communication services for the ground, and other drones can collect real-time information about the disaster area.

[0063] In addition, it is worth mentioning that the three-dimensional Gauss-Markov mobility model is used to describe the movement trajectory of the UAV, thereby generating the mobility models of several UAVs. In order to describe the real-time position of the UAV, the flight cycle of the UAV is divided into several equal discrete time slots. If the discrete time slot is small enough, it can be considered that the position of each UAV is roughly unchanged within this interval.

[0064] S2, obtaining the distances between the plurality of drones and the flight speeds of the plurality of drones based on the mobility model, and obtaining the channel models and link stability models of the plurality of drones;

[0065] It can be understood that according to the movement model of the drone, the real-time flight speed of the drone and the distance between the drones can be obtained. In order to describe the link stability between the drones, the channel model and link stability model of the drone are first obtained.

[0066] S3, designing link stability indicators of the plurality of drones according to the distances between the plurality of drones and the flight speeds of the plurality of drones, and judging whether the link stability model meets the preset requirements based on the link stability indicators, and if so, obtaining link information transmission indicator values ​​between the plurality of drones;

[0067] It can be understood that according to the mobile model of the UAV, the real-time flight speed of the UAV and the distance between the UAVs can be obtained. In order to comprehensively consider the link quality and relative speed between the UAVs, the link stability index of the UAV is designed according to the real-time flight speed of the UAV and the distance between the UAVs. The received signal strength indication is used to indicate whether the link stability model meets the preset requirements. This is because compared with the signal-to-noise ratio, packet delivery rate and bit error rate, the received signal strength indication can more quickly and accurately judge the link quality between UAVs and the stability of the link stability model.

[0068] Specifically, the step S3 includes steps S31 to S32:

[0069] S31, evaluating the link quality between a plurality of the drones in the adjacent spherical area by receiving signal strength;

[0070] The expression for evaluating the link quality is:

[0071]

[0072] In the formula, represents the link quality, represents the total number of samples of the received signal strength, Indicates The received signal strength value is known from this. The smaller the value of, the better the link quality is, and the easier it is to use for transmitting post-disaster information;

[0073] S32, evaluating the path stability of the links of the plurality of drones according to the mobility model and the link quality;

[0074] The expression of the path stability of the link is:

[0075]

[0076] In the formula, a value representing the path stability of the link, represents the first weight factor, represents the second weight factor, Indicates that in the spherical area Internal drone The flight speed of It should be noted that the link quality and flight speed between drones are combined to represent the path stability of the link. It can be seen that The larger the value of, the better the path stability of the link.

[0077] S4, obtaining occupied bandwidths of several transmission paths in the channel model, estimating channel utilization of link transmission information of the channel model based on the occupied bandwidths, designing path stability optimization indicators of several transmission paths, and jointly optimizing the channel model based on the channel utilization and the stability optimization indicator;

[0078] The calculation expression for estimating the channel utilization rate of the link transmission information of the channel model based on the occupied bandwidth is:

[0079]

[0080] In the formula, represents the channel utilization rate, Indicates the total bandwidth, represents the occupied bandwidth, Represents a spherical area , Indicates the number of transmission information samples, represents the sampling interval;

[0081] It is understandable that when the UAV transmits information, the selection of channels should be considered, so the channel model needs to be optimized. Otherwise, inappropriate channel selection will cause excessive interference between UAVs, affecting the transmission efficiency of post-disaster real-time information. The channel utilization of the link transmission information of the channel model is calculated through the calculation expression of channel utilization, so that it can be judged whether there are idle channels in the occupied channels in the channel model. There are several paths for transmitting information in the channel model. The more channels are occupied, the lower the channel utilization is. By setting the path stability optimization index of the transmission path, that is, designing the corresponding number of paths according to the number of UAVs, the channel model is jointly optimized according to the channel utilization and path stability optimization index. Therefore, the flight area is divided into several spherical areas, and the aerial base station is located in the middle spherical area. Therefore, by jointly optimizing the channel model, the path stability between the aerial base station and the UAV can be made, so that the transmission of the UAV will not conflict.

[0082] S5, selecting a corresponding link based on the link information transmission index value and the optimized channel model to perform information transmission between the plurality of drones in the plurality of spherical areas, and obtaining a disjoint multipath model of the plurality of drones in the plurality of spherical areas, obtaining a hypergraph link model between the plurality of drones based on the disjoint multipath model, and constructing a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones;

[0083] Specifically, the step S5 includes steps S51 to S53:

[0084] S51, obtaining inter-node paths of a plurality of the drones in the disjoint multi-path model, and generating hypergraph nodes based on the inter-node paths;

[0085] S52, generating a hypergraph link model between a plurality of the drones according to the hypergraph nodes;

[0086] S53, determining whether the flight paths of the plurality of drones intersect based on the inter-node paths of the plurality of drones, and if so, obtaining a plurality of intersection points, and constructing a conflict graph according to the intersection points;

[0087] It can be understood that in order to minimize the impact of link failure or local failure on the UAV, the link most suitable for UAV information transmission is selected according to the link information transmission index value and the optimized channel model. After obtaining the transmission link of the UAV, the disjoint multipath model of the UAV is generated according to the transmission link, and the disjoint multipath model is modeled using a hypergraph to obtain a hypergraph link model. For details, please refer to Figure 2 , Figure 2 (a) includes two paths, namely a1, a2, a3, a4, a8, a1, a5, a6, a7, a8, Figure 2 In (b), c1 and c2 represent hyperedges, and then a conflict graph is constructed based on the intersections in the movement models of several drones.

[0088] S6, obtaining a weight of each flight path according to the multiple paths in the hypergraph link model and the conflict graph, and scheduling a plurality of the drones in a plurality of spherical areas based on the optimization algorithm and the weight of each flight path;

[0089] Specifically, the step S6 includes steps S61 to S67:

[0090] S61, extracting the hyperedges in the hypergraph link model and the vertices of the conflict graph, and obtaining the weights of the hyperedges and the weights of the vertices;

[0091] S62, obtaining a weight of each of the flight paths according to the weight of the hyperedge and the weight of the vertex;

[0092] It can be understood that the hyperedge represents the path of the drone from the source node to the destination node, and the stability of each path represents the weight of each hyperedge. Therefore, it can be concluded that the weight of the flight path is determined according to the stability of the drone's flight path.

[0093] S63, initializing a number of the drones in the spherical area, and determining initial flight directions of the drones;

[0094] S64, updating the flight directions of the plurality of drones in real time based on the initial flight direction and the weight of each flight path to obtain dispatch routes of the plurality of drones;

[0095] It can be understood that by initializing the drones in the spherical area, assigning tasks to each drone, determining the initial flight direction of the drone, and then updating the flight direction of the drone in real time according to the weight of each flight path, the scheduling routes of all drones can be obtained.

[0096] S65, evaluating the fitness of the scheduling route according to the flight speed and the flight duration to obtain a task queue of the plurality of drones;

[0097] It can be understood that after obtaining the dispatch route of the drone, the flight speed and flight duration of each drone on the flight path are evaluated. If the flight speed and flight duration of the path meet the requirements, it is proved that the drone is suitable for the flight path, and a task queue for each drone is generated according to the flight path of each drone.

[0098] S66, obtaining the flying positions of the drones in all time slots based on the dispatching routes of the drones, and obtaining the energy consumption of several of the drones in real time;

[0099] S67, optimizing the task queue according to the flight positions of the drone in all time slots and the energy consumption of the drone;

[0100] It can be understood that by obtaining the real-time position of each drone and the real-time energy consumption of the drone, the task queue is optimized according to the real-time energy consumption of the drone at each position. That is, when the energy consumption of the drone is almost exhausted, the task of the drone is changed and it is allowed to return for charging, thereby effectively utilizing the battery energy of the drone and avoiding the situation where the energy is consumed and the task cannot be completed.

[0101] In summary, the UAV scheduling method in the above-mentioned embodiment of the present invention determines whether the link stability model meets the requirements by judging the link stability index to obtain the link information transmission index value between UAVs, and optimizes the channel model through channel utilization and stability optimization index, so as to obtain the optimal mode of information transmission between UAVs, so as to reduce the energy consumption of UAVs, and avoid the problem of conflict in multiple link transmission processes. The weight of each flight path is obtained through the hypergraph link model and the conflict graph, and through several spherical areas, it can meet the scheduling of a UAV network composed of several UAVs, thereby avoiding the problem of mismatch and difficulty in scheduling of UAV network scheduling caused by changes in the UAV network topology.

[0102] Embodiment 2

[0103] See also Figure 3 , which shows a UAV dispatching system in a second embodiment of the present invention, and the system includes:

[0104] A division module 11 is used to divide the flight area into a plurality of spherical areas, wherein each of the plurality of spherical areas contains a plurality of drones, simulate the movement trajectories of the plurality of drones in the plurality of spherical areas, and generate movement models of the plurality of drones based on the movement trajectories;

[0105] An acquisition module 12, configured to acquire the distances between the plurality of drones and the flight speeds of the plurality of drones based on the mobility model, and acquire channel models and link stability models of the plurality of drones;

[0106] The design module 13 is used to design link stability indicators of the plurality of drones according to the distances between the plurality of drones and the flight speeds of the plurality of drones, and judge whether the link stability model meets the preset requirements based on the link stability indicators, and if so, obtain link information transmission indicator values ​​between the plurality of drones;

[0107] The acquisition design module 14 is used to obtain the occupied bandwidths of several transmission paths in the channel model, estimate the channel utilization of the link transmission information of the channel model based on the occupied bandwidth, and design the path stability optimization indexes of several transmission paths, and jointly optimize the channel model based on the channel utilization and the stability optimization index, wherein the calculation expression for estimating the channel utilization of the link transmission information of the channel model based on the occupied bandwidth is:

[0108]

[0109] In the formula, represents the channel utilization rate, Indicates the total bandwidth, represents the occupied bandwidth, Represents a spherical area , Indicates the number of transmission information samples, represents the sampling interval;

[0110] A selection module 15 is used to select a corresponding link based on the link information transmission index value and the optimized channel model to perform information transmission between the plurality of drones in the plurality of spherical areas, and obtain a disjoint multipath model of the plurality of drones in the plurality of spherical areas, obtain a hypergraph link model between the plurality of drones based on the disjoint multipath model, and construct a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones;

[0111] The scheduling module 16 is used to obtain the weight of each flight path according to the multiple paths in the hypergraph link model and the conflict graph, and to schedule several drones in several spherical areas based on the optimization algorithm and the weight of each flight path.

[0112] In some optional embodiments, the design module 13 includes:

[0113] A first evaluation unit, configured to evaluate link quality between a plurality of the UAVs in the adjacent spherical area by receiving signal strength;

[0114] A second evaluation unit, configured to evaluate the path stability of the links of the plurality of drones according to the mobility model and the link quality;

[0115] The link quality expression is:

[0116]

[0117] In the formula, represents the link quality, represents the total number of samples of the received signal strength, Indicates a value of the received signal strength;

[0118] The expression of the path stability of the link is:

[0119]

[0120] In the formula, a value representing the path stability of the link, represents the first weight factor, represents the second weight factor, Indicates that in the spherical area Internal drone The flight speed of .

[0121] In some optional embodiments, the selection module 15 includes:

[0122] An acquisition generation unit is used to obtain the inter-node paths of the plurality of drones in the disjoint multi-path model, and generate a hypergraph node based on the inter-node paths;

[0123] A generating unit, configured to generate a hypergraph link model between a plurality of the drones according to the hypergraph nodes;

[0124] The construction unit is used to determine whether the flight paths between the several drones intersect based on the node paths between the several drones, and if so, obtain several intersection points and construct a conflict graph based on the intersection points.

[0125] In some optional embodiments, the scheduling module 16 includes:

[0126] An extraction unit, used to extract the hyperedges in the hypergraph link model and the vertices of the conflict graph, and obtain the weights of the hyperedges and the weights of the vertices;

[0127] A first acquisition unit, configured to obtain a weight of each of the flight paths according to the weight of the hyperedge and the weight of the vertex;

[0128] An initialization unit, used to initialize a number of the UAVs in a number of the spherical areas and determine initial flight directions of the UAVs;

[0129] An updating unit, configured to update the flight directions of the plurality of drones in real time based on the initial flight direction and the weight of each flight path, so as to obtain the dispatching routes of the plurality of drones;

[0130] A third evaluation unit is used to evaluate the fitness of the scheduling route according to the flight speed and the flight duration to obtain a task queue of the plurality of drones;

[0131] A second acquisition unit is used to acquire the flying position of the drone in all time slots based on the dispatch route of the drone, and acquire the energy consumption of several drones in real time;

[0132] The optimization unit is used to optimize the task queue according to the flying positions of the drone in all time slots and the energy consumption of the drone.

[0133] The functions or operation steps implemented when the above modules and units are executed are generally the same as those in the above method embodiments, and will not be repeated here.

[0134] The drone dispatching system provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0135] Embodiment 3

[0136] See also Figure 4 , shown is a computer device in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned drone scheduling method is implemented.

[0137] The memory 10 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer device, such as a hard disk of the computer device. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart MediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of a computer device and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer device, but also to temporarily store data that has been output or is to be output.

[0138] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0139] It should be pointed out that Figure 4 The structure shown does not constitute a limitation on the computer device. In other embodiments, the computer device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0140] An embodiment of the present invention further provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the drone scheduling method as described above.

[0141] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0142] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0143] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0144] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0145] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for dispatching a drone, characterized in that: The method comprises: Dividing the flight area to obtain a plurality of spherical areas, wherein each of the spherical areas contains a plurality of drones, simulating movement trajectories of the plurality of drones in the plurality of spherical areas, and generating movement models of the plurality of drones based on the movement trajectories; Based on the mobility model, obtaining distances between the plurality of drones and flight speeds of the plurality of drones, and obtaining channel models and link stability models of the plurality of drones; Designing link stability indicators of the plurality of drones according to the distances between the plurality of drones and the flight speeds of the plurality of drones, and judging whether the link stability model meets the preset requirements based on the link stability indicators, and if so, obtaining link information transmission indicator values ​​between the plurality of drones; Obtaining occupied bandwidths of several transmission paths in the channel model, estimating channel utilization of link transmission information of the channel model based on the occupied bandwidths, designing path stability optimization indicators of several transmission paths, and jointly optimizing the channel model based on the channel utilization and the stability optimization indicator; Selecting corresponding links based on the link information transmission index value and the optimized channel model to perform information transmission between the plurality of drones in the plurality of spherical areas, and obtaining a disjoint multipath model of the plurality of drones in the plurality of spherical areas, obtaining a hypergraph link model between the plurality of drones based on the disjoint multipath model, and constructing a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones; The step of obtaining a hypergraph link model between the plurality of drones based on the disjoint multipath model, and constructing a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones comprises: Obtaining inter-node paths of a plurality of the drones in the disjoint multi-path model, and generating hypergraph nodes based on the inter-node paths; Generate a hypergraph link model between a number of the drones according to the hypergraph nodes; Determine whether the flight paths of the plurality of drones intersect based on the inter-node paths of the plurality of drones, and if so, obtain a plurality of intersection points, and construct a conflict graph based on the intersection points; The weight of each flight path is obtained according to the multiple paths in the hypergraph link model and the conflict graph, and the plurality of drones in a plurality of spherical areas are scheduled based on the optimization algorithm and the weight of each flight path.

2. The method for dispatching unmanned aerial vehicles according to claim 1, characterized in that: Before the step of judging whether the link stability model meets the preset requirements based on the link stability indicator, the method further includes: Evaluating the link quality between a plurality of the drones in the adjacent spherical area by receiving signal strength; The path stability of the links of several of the drones is evaluated based on the mobility model and the link quality.

3. The method for dispatching unmanned aerial vehicles according to claim 2, characterized in that: The expression of the link quality is: In the formula, represents the link quality, represents the total number of samples of the received signal strength, Indicates a value of the received signal strength; The expression of the path stability of the link is: In the formula, a value representing the path stability of the link, represents the first weight factor, represents the second weight factor, Indicates that in the spherical area Internal drone The flight speed of .

4. The method for dispatching unmanned aerial vehicles according to claim 1, characterized in that: The calculation expression for estimating the channel utilization rate of the link transmission information of the channel model based on the occupied bandwidth is: In the formula, represents the channel utilization rate, Indicates the total bandwidth, represents the occupied bandwidth, Represents a spherical area , Indicates the number of transmission information samples, Indicates the sampling interval.

5. The method for dispatching unmanned aerial vehicles according to claim 1, characterized in that: The step of obtaining the weight of each flight path according to the multiple paths in the hypergraph link model and the conflict graph comprises: Extracting the hyperedges in the hypergraph link model and the vertices of the conflict graph, and obtaining the weights of the hyperedges and the weights of the vertices; The weight of each of the flight paths is obtained according to the weight of the hyperedge and the weight of the vertex.

6. The method for dispatching unmanned aerial vehicles according to claim 1, characterized in that: The step of scheduling the plurality of drones in the plurality of spherical areas based on the optimization algorithm and the weight of each flight path includes: Initializing a number of the drones in a number of the spherical areas, and determining initial flight directions of the drones; Based on the initial flight direction and the weight of each flight path, the flight directions of the plurality of drones are updated in real time to obtain the dispatching routes of the plurality of drones; Evaluate the fitness of the scheduling route according to the flight speed and the flight duration to obtain a task queue of the plurality of drones; Based on the dispatch route of the drone, the flying position of the drone in all time slots is obtained, and the energy consumption of several drones is obtained in real time; The task queue is optimized based on the locations where the drone flies in all time slots and the energy consumption of the drone.

7. A drone dispatching system, characterized in that: The system comprises: A division module is used to divide the flight area into a plurality of spherical areas, wherein each of the plurality of spherical areas contains a plurality of drones, simulate the movement trajectories of the plurality of drones in the plurality of spherical areas, and generate movement models of the plurality of drones based on the movement trajectories; An acquisition module, used for acquiring the distances between the plurality of drones and the flight speeds of the plurality of drones based on the mobility model, and acquiring the channel models and link stability models of the plurality of drones; A design module, used for designing link stability indicators of the plurality of drones according to the distances between the plurality of drones and the flight speeds of the plurality of drones, and judging whether the link stability model meets the preset requirements based on the link stability indicators, and if so, obtaining link information transmission indicator values ​​between the plurality of drones; An acquisition design module is used to acquire occupied bandwidths of several transmission paths in the channel model, estimate channel utilization of link transmission information of the channel model based on the occupied bandwidth, design path stability optimization indicators of several transmission paths, and jointly optimize the channel model based on the channel utilization and the stability optimization indicator; A selection module is used to select a corresponding link based on the link information transmission index value and the optimized channel model to perform information transmission between the plurality of drones in the plurality of spherical areas, and obtain a disjoint multipath model of the plurality of drones in the plurality of spherical areas, obtain a hypergraph link model between the plurality of drones based on the disjoint multipath model, and construct a conflict graph through the intersection relationship between the plurality of drones in the mobility models of the plurality of drones; The selection module includes: An acquisition generation unit is used to obtain the inter-node paths of the plurality of drones in the disjoint multi-path model, and generate a hypergraph node based on the inter-node paths; A generating unit, configured to generate a hypergraph link model between a plurality of the drones according to the hypergraph nodes; A construction unit, configured to determine whether the flight paths of the plurality of drones intersect based on the inter-node paths of the plurality of drones, and if so, obtain a plurality of intersection points, and construct a conflict graph according to the intersection points; The scheduling module is used to obtain the weight of each flight path according to the multiple paths in the hypergraph link model and the conflict graph, and to schedule a number of the drones in a number of spherical areas based on the optimization algorithm and the weight of each flight path.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the drone scheduling method as described in any one of claims 1 to 6 is implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the drone scheduling method as described in any one of claims 1 to 6 is implemented.

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