CN114448490A - Path planning and spectrum resource allocation method and system for multiple unmanned aerial vehicles - Google Patents

Abstract

The invention discloses a method and a system for path planning and spectrum resource allocation of multiple unmanned aerial vehicles, wherein the method comprises the following steps: simulating ground terminal distribution and hot spot distribution to obtain a plurality of clusters; according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle, the unmanned aerial vehicles are configured for the ground terminals, and the network coverage rate and the data acquisition efficiency of the emergency communication system are improved by performing cooperative cooperation on network coverage of different disaster areas and the unmanned aerial vehicles on the same layer; based on the interference parameters of other unmanned aerial vehicles and the channel gain between the ground terminals, the data volume currently received by each unmanned aerial vehicle is calculated, and according to the data volume currently received by each unmanned aerial vehicle, the multi-dimensional influence parameters are combined to obtain the maximum arrangement income as a target, the COBSO intelligent algorithm is utilized to distribute frequency resources for each unmanned aerial vehicle, the track of the unmanned aerial vehicle is optimized, the deployment quantity of the unmanned aerial vehicle is optimized, and the utilization rate of limited bandwidth resources and the life cycle of the system are improved.

Description

A method and system for path planning and spectrum resource allocation for multiple unmanned aerial vehicles

technical field

The invention relates to the fields of unmanned aerial vehicle application, emergency communication and mobile edge computing, in particular to a method and system for path planning and spectrum resource allocation for multiple unmanned aerial vehicles.

Background technique

With the development of science and technology, people can use mobile phones, computers and other intelligent terminal devices to communicate with others anytime, anywhere. There is no time and space barriers for communication between people. Information, the main way to connect with the world. However, when natural or man-made disasters such as earthquakes, fires, tsunamis, and wars occur, the communication infrastructure on the ground will suffer huge damage or even be completely destroyed, which will greatly hinder the timely rescue of rescuers, which will greatly reduce the Threats to the safety of life and property of trapped people. Thanks to the increasingly mature manufacturing process and stable performance of UAVs, UAVs have gradually developed from military to civilian use and have been applied to all aspects of people's production and life, especially in the field of auxiliary emergency communications. Concentrate on the following points:

(1) The Flying Ad hoc network (FANET) composed of multiple UAVs has strong adaptability and scalability. It can carry various sensing devices, such as sensors, cameras, etc. for environmental detection; it can also be equipped with communication devices such as wireless signal transceivers as a base station in the air to transmit information in the form of relays to ensure effective communication between ground personnel . In addition, due to its high-altitude flight characteristics, the present invention can consider that the communication between UAVs and between UAVs and ground users is line-of-sight transmission. In this case, the quality of information transmission can be well guaranteed.

(2) UAVs have the characteristics of ready-to-fly, deployment flexibility and high mobility make UAVs able to face many complex situations calmly.

(2) In FANET, if one node fails, the UAV swarm can quickly deploy another UAV to replace the failed UAV, which reflects the strong robustness of UAV FANET. In addition, due to its deployment at high altitude, the impact of secondary disasters, such as aftershocks, on the system can be effectively avoided.

From the current development and research status, the UAV-assisted communication system is mainly divided into the following three directions: UAV-assisted communication coverage, UAV-assisted relay transmission, UAV-assisted information dissemination and data collection . However, the current UAV-assisted emergency communication system still has the following shortcomings: (1) The flight speed of the UAV swarm has not been fully considered. For example, it should be dynamically adjusted according to the density of ground service terminals. Although the trajectory optimization of the UAV has been carried out, the optimization results are more for more accurate positioning and collision avoidance, and may be lacking in the guarantee of communication quality. (2) Although TDMA technology is used to propose a novel channel access mechanism, the size of each time slot is basically fixed. In practical application scenarios, better performance indicators, such as channel utilization, communication delay, etc., can be obtained by dynamically adjusting the size of the time slot according to the actual task request.

SUMMARY OF THE INVENTION

Therefore, the technical problem to be solved by the present invention is to overcome the defects in the prior art that full coverage of the disaster-affected area network and reasonable allocation of frequency resources cannot be achieved, thereby providing a multi-UAV path planning and spectrum resource allocation method and system.

To achieve the above object, the present invention provides the following technical solutions:

In a first aspect, an embodiment of the present invention provides a method for path planning and spectrum resource allocation for multiple UAVs, including the following: using a preset simulation method to simulate the distribution of post-disaster ground terminals and the distribution of hotspots, respectively, to obtain a plurality of hotspots as The ground terminals in the center are clustered; according to the distance between each drone and each cluster and the number of ground terminals that each drone can access, configure drones for each ground terminal; use TDMA technology to achieve unmanned According to the channel access of the drone, the current amount of data received by each drone is calculated based on the interference parameters of other drones and the channel gain with the ground terminal. According to the current amount of data received by each drone, combined with Multi-dimensional influence parameters, with the goal of obtaining the maximum layout benefit, using the COBSO intelligent algorithm to allocate frequency resources for each UAV, optimize the UAV trajectory and deployment quantity.

In one embodiment, a preset simulation method is used to simulate the distribution of post-disaster ground terminals and the distribution of hotspots, respectively, to obtain a plurality of processes of clustering of ground terminals centered on hotspots, including: using a Thomas cluster process to simulate the distribution of post-disaster ground terminals , using the Poisson point process to simulate the distribution of hotspots, and obtain multiple clusters of ground terminals centered on the hotspots.

In one embodiment, according to the distance between each drone and each cluster and the number of ground terminals that each drone can access, the process of configuring the drones for each ground terminal includes: according to each The distance between the drone and each cluster and the number of ground terminals that each drone can access, deploy a cluster drone for each cluster in turn, and use auxiliary drones for the unclustered ground terminals, current Cluster UAVs cannot effectively cover ground terminals with time-sensitive data.

In one embodiment, the process of deploying a cluster drone for a single cluster includes: judging whether the current cluster is associated with the cluster drone; when the current cluster is not associated with the cluster drone, finding the current coverage of the drone All drones within the range are sorted from near to far in order of distance; the cluster drones that are closest to the current cluster and whose number of connected ground terminals has not reached the upper limit are associated with the current cluster; When the number of all cluster drones connected to the ground terminal reaches the upper limit, a new cluster drone is allocated to the current cluster.

In one embodiment, the process of using TDMA technology to realize the channel access of the unmanned aerial vehicle includes: when the channel state between the ground terminal and the unmanned aerial vehicle satisfies the signal-to-noise ratio condition, the ground terminal and the unmanned aerial vehicle establish communication, And the UAV uses TDMA technology to communicate with each ground terminal in the cluster under its jurisdiction.

In one embodiment, the process of calculating the amount of data currently received by each UAV based on the interference parameters of other UAVs and the channel gain between the UAV and the ground terminal includes: The channel gain with each ground terminal under its jurisdiction, the interference parameters of the UAV to the channel by other UAVs in the current time slot, and the instantaneous reachability rate of the UAV in the current time slot; The instantaneous reach rate of the UAV in the current time slot, the time slot size, the data packet generation time of the ground terminal, and the data packet expiration time are calculated to obtain the current amount of data received by the UAV.

In an embodiment, the multi-dimensional influence parameters include: the data packet size of each ground terminal, the degree of delay sensitivity, the current position of the UAV, and the remaining spectrum resources.

In a second aspect, an embodiment of the present invention provides a multi-UAV path planning and spectrum resource allocation system, including: a simulation distribution module, configured to use a preset simulation method to simulate the distribution of post-disaster ground terminals and the distribution of hotspots, respectively, to obtain Multiple clusters of ground terminals centered on hotspots; the UAV deployment module is used to calculate the number of ground terminals for each UAV based on the distance between each UAV and each cluster and the number of ground terminals that each UAV can access. The ground terminal is equipped with the UAV; the spectrum resource allocation module is used to realize the channel access of the UAV by using TDMA technology. According to the current amount of data received by each drone, combined with multi-dimensional influence parameters, with the goal of obtaining the maximum layout benefit, the COBSO intelligent algorithm is used to allocate frequency resources to each drone, optimize Human-machine trajectories and number of deployments.

In a third aspect, an embodiment of the present invention provides a computer device, including: at least one processor, and a memory connected in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by at least one processor. One processor executes, so that at least one processor executes the method for multi-UAV path planning and spectrum resource allocation according to the first aspect of the embodiment of the present invention.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the multi-UAV path planning and execution of the first aspect of the embodiment of the present invention. Spectrum resource allocation method.

The technical scheme of the present invention has the following advantages:

1. The method and system for path planning and spectrum resource allocation for multiple UAVs provided by the present invention use a preset simulation method to simulate the distribution of post-disaster ground terminals and the distribution of hotspots, respectively, to obtain a plurality of clusters of ground terminals centered on hotspots ;According to the distance between each drone and each cluster and the number of ground terminals that each drone can access, configure drones for each ground terminal. The cooperation between UAVs significantly improves the network coverage and data collection efficiency of the emergency communication system; TDMA technology is used to realize the channel access of UAVs, based on the interference parameters of other UAVs, and the connection with ground terminals. According to the current amount of data received by each UAV, combined with the multi-dimensional influence parameters, in order to obtain the maximum layout benefit, the COBSO intelligent algorithm is used for each UAV. UAVs allocate frequency resources, optimize UAV trajectories and deployment numbers, thereby improving the utilization of limited bandwidth resources and the life cycle of the system.

2. The method and system for multi-UAV path planning and spectrum resource allocation provided by the present invention, set up cluster UAVs and auxiliary UAVs, and use mobile edge computing and optimal UAV deployment to achieve reliable emergency response in post-disaster areas Communication and data collection, thereby reducing the delay of the entire communication system, reducing the loss of delay-sensitive data, accurately locating the position of the trapped people, and assisting the effective rescue of the rescuers; by considering the data packet size and delay requirements of the ground terminal , through the calculation of intelligent algorithms, the optimal UAV flight path and the reasonable allocation of limited spectrum resources are obtained, so as to realize the optimal scheduling of spectrum resources, improve resource utilization, and improve the operation efficiency and life cycle of the entire emergency communication system.

Description of drawings

In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

FIG. 1 is a flowchart of a specific example of a method for path planning and spectrum resource allocation for multiple UAVs provided by an embodiment of the present invention;

FIG. 2 is a flowchart of another specific example of a multi-UAV path planning and spectrum resource allocation method provided by an embodiment of the present invention;

3 is a three-layer network architecture provided by an embodiment of the present invention;

4 is a flowchart of another specific example of a method for multi-UAV path planning and spectrum resource allocation provided by an embodiment of the present invention;

5 is a composition diagram of another specific example of a multi-UAV path planning and spectrum resource allocation system provided by an embodiment of the present invention;

FIG. 6 is a composition diagram of a specific example of a computer device provided by an embodiment of the present invention.

Detailed ways

The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation or a specific orientation. construction and operation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed to indicate or imply relative importance.

In the description of the present invention, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense, unless otherwise expressly specified and limited, for example, it may be a fixed connection or a detachable connection connection, or integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal connection of two components, which can be a wireless connection or a wired connection connect. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.

In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

Example 1

An embodiment of the present invention provides a method for path planning and spectrum resource allocation for multiple UAVs, as shown in FIG. 1 , including the following:

Step S11 : using a preset simulation method to simulate the distribution of post-disaster ground terminals and the distribution of hotspots, respectively, to obtain a plurality of clusters of ground terminals centered on the hotspots.

Specifically, in the embodiment of the present invention, the present invention uses Poisson Point Process (PPP) to simulate the location distribution of hotspots. The hotspots include: densely populated areas, such as schools, hospitals, etc.; Therefore, the density of ground terminals between disaster areas will also vary accordingly. The Thomas Clustering Process (TCP) is used to describe the location distribution of ground terminals. Ground terminals are communicable devices carried by trapped people. The hot spots are cluster centers for clustering. In TCP, all ground terminals will be independently distributed around hotspots (cluster centers) according to the same Gaussian distribution, where unlike cellular networks with overlapping service areas and complex cellular networks, embodiments of the present invention assume that these clusters are scattered and non-overlapping.

Specifically, in the embodiment of the present invention, the distribution of all cluster centers is represented by PPP with independent and identical distribution and a distribution density of ^λhs ; other ground terminals around the hotspot are distributed around the hotspot by TCP. Since the ground terminal modeled by TCP is more discrete than MCP ((Model Core Potential)), its radiation range will be larger. Moreover, MCP needs to set the coverage radius in advance, which is unpredictable in the actual disaster relief process. In addition, in the post-disaster rescue process, although the hotspot is the center and the cluster is mainly used to serve the hotspot area, in order to serve more areas and find more trapped people, the embodiment of the present invention uses the PCP-based protocol. TCP to represent all ground terminals scattered around the hotspot.

Step S12: According to the distance between each drone and each cluster and the number of ground terminals that each drone can access, configure drones for each ground terminal.

Specifically, the implementation process of step S12 includes, according to the distance between each drone and each cluster and the number of ground terminals that each drone can access, sequentially deploying a cluster drone for each cluster, using auxiliary UAVs cover the ground terminals that are not clustered, and the current clustered UAVs cannot effectively cover the ground terminals with time-sensitive data.

Specifically, the ground terminals in the embodiments of the present invention are divided into ground terminals that clustered drones can reach in time, drones that cannot reach the clustered data-sensitive ground terminals in time, and ground terminals that are not clustered , for three ground terminals, using different deployment methods to deploy UAVs for each ground terminal.

Specifically, each cluster has a drone that is responsible for message forwarding and data collection in the cluster. Clusters and drones can cooperate to complete the network coverage and data collection in the responsible area. The UAV is named cluster UAV; the UAV that provides communication coverage and data collection for non-clustered ground terminals is named auxiliary UAV.

Specifically, due to the limited bandwidth resources and energy constraints of UAVs, the number of ground terminals that each UAV can access is limited. When the ground terminals in the cluster are too dense, it will inevitably affect the service of the overall system. quality. When the cluster UAV is overloaded, the auxiliary UAV will assist the cluster UAV to complete the terminal access, and provide communication coverage and data collection for the experimental sensitive data ground terminal that cannot reach the clustered UAV in time. It is possible to ensure the communication coverage of the entire disaster area and the timeliness of data collection.

Specifically, cluster UAVs can communicate and cooperate, and their positions are constantly changing to adapt to different communication needs. The information collected by the cluster drones is connected to the server carried by the emergency communication vehicle in multiple or direct ways through other cluster drones or auxiliary drones.

Specifically, in order to achieve as many successful data packets collection as possible, it is also necessary to reduce the number of UAVs arranged as little as possible, and when too many terminals access at the same time, serious competition for spectrum resources will occur, thus In the case of affecting normal data collection, as shown in Figure 2, the process of deploying cluster UAVs for a single cluster includes steps S21 to S23, and the procedures for executing steps S21 to S23 are shown in Table 1, as follows:

Step S21: Determine whether the current cluster is associated with a cluster drone.

Step S22: When the current cluster is not associated with a cluster drone, find all the cluster drones currently within the coverage of the drone, and sort them from near to far in order of distance.

Step S23: Associate the cluster drones that are closest to the current cluster and whose number of connected ground terminals has not reached the upper limit with the current cluster; when the number of all cluster drones connected to the ground terminals reaches the upper limit, Then assign a new cluster drone to the current cluster.

Table 1

Specifically, based on the above method, as shown in FIG. 3 , the technical solution of the embodiment of the present invention is actually a new type of multi-unmanned aerial vehicle coordinated three-layer emergency communication network architecture, and the first layer of the three-layer network architecture is a hotspot , Ground terminal distribution, the second layer is the cluster UAV layer, and the third layer is the auxiliary UAV layer. Through the network coverage of different disaster-affected areas and the cooperation between multiple UAVs on the same layer, the emergency communication is significantly improved. System network coverage and data collection efficiency.

Step S13: Use TDMA technology to realize the channel access of the UAV, and calculate the current amount of data received by each UAV based on the interference parameters of other UAVs and the channel gain with the ground terminal. The amount of data currently received by the man-machine, combined with multi-dimensional influence parameters, aims to obtain the maximum layout benefit, and uses the COBSO intelligent algorithm to allocate frequency resources to each UAV, optimize the UAV trajectory and deployment quantity.

Specifically, in the communication between the UAV and the ground terminal, the embodiment of the present invention considers the air-to-ground channel dominated by line-of-sight transmission (LoS), and adopts the random access mechanism in the MAC layer. When the UAV achieves communication coverage, the ground terminal can communicate with the UAV only when the signal-to-noise ratio (SNR) of the ground terminal receiver must be greater than the threshold.

Specifically, in a cruise period T, due to the movement of the UAV, each ground terminal must transmit data to the corresponding UAV within a limited connection time, so the UAV of the embodiment of the present invention also utilizes TDMA The technology communicates with each ground terminal in the cluster under its jurisdiction, and adopts TDMA technology to divide the time T into N equal time slots. When the communication requirements are met, the ground terminal will be able to connect to the UAV and will be allocated the corresponding spectrum resources.

Specifically, as shown in FIG. 4 , the process of calculating the amount of data currently received by each UAV based on the interference parameters of other UAVs and the channel gain with the ground terminal includes steps S31 to S32, as follows :

Step S31: According to the channel gain between the UAV and each ground terminal under its jurisdiction in the current time slot, and the interference parameters of the UAV to the channel by other UAVs in the current time slot, calculate the current time slot. Instantaneous reachability of drones inside.

The calculation formula of the channel gain between the UAV and the single ground terminal under its jurisdiction in the current time slot is as follows:

g(r,q)=γ ₀ γ|r ² | (1)

Among them, g(r, q) represents the channel gain between the UAV and the ground terminal, r represents the distance between the UAV and the ground terminal, q represents the spatial coordinates of the UAV, and γ is the small value in the determined distribution. Scale fading, the distribution obeys a Gamma distribution, and γ ₀ represents the channel power gain at a reference distance of 1m.

Because the channel between the UAV and the ground terminal used in the line-of-sight transmission model is orthogonal to the channel between the UAV and another ground terminal. In this way, there will be no interference between the channel between the UAV and the ground terminal, and the channel between the UAV and another ground terminal. Therefore, the interference between the channels is no longer considered, and only the interference between other channels is considered. If the man-machine interferes with this channel, the calculation formula of the interference parameter of the UAV on this channel by other UAVs in the current time slot is:

In the formula, P _u' is the transmit power of the u'th UAV, _Lu,u' (n)=P _u' ||d _u,u' || ^-α represents the uth UAV and the The gain of the channel between the u'th UAV, d _{u, u'} is the distance between the uth UAV and the u'th UAV, and n is the nth time slot.

Then according to formula (1) and formula (2), it can be calculated that the instantaneous reachability rate of the UAV in the nth time slot is:

In the formula, b _g (n) represents the spectrum resources obtained by ground terminal g in time slot n; p _g transmit power of ground terminal g; C _g,u (n) represents in time slot n, ground terminal g and no Whether the connection between man-machine u is 1, otherwise it is 0; σ ² represents the noise power.

Step S32: Calculate the current amount of data received by the UAV according to the instantaneous reachability of the UAV in the current time slot, the time slot size, the data packet generation time of the ground terminal, and the data packet expiration time.

Specifically, the embodiment of the present invention is based on the spectrum resource allocation strategy of time division multiplexing. By analyzing the data packet size, delay sensitivity, current UAV position and remaining spectrum resources of each ground terminal, The available spectrum resources in the slot are dynamically allocated instead of fixedly allocated, and the current amount of data S _g received by each UAV is:

和

分别表示数据包的生成时间和过期时间，

表示无人机开始接收地面终端数据的时间。where δ represents the time slot size,

and

represent the generation time and expiration time of the data packet, respectively,

Indicates the time when the drone starts to receive data from the ground terminal.

Specifically, the embodiment of the present invention uses the COBSO intelligent algorithm to allocate frequency resources to each UAV, optimize the Human-machine trajectory and deployment quantity, wherein, in a specific embodiment, the multi-dimensional influencing parameters include: data packet size, delay sensitivity of each ground terminal, current UAV position and remaining spectrum resources. The execution program of COBSO intelligent algorithm is shown in Table 2.

The main innovation points of the COBSO intelligent algorithm used in the embodiment of the present invention are:

(1) Population initialization mechanism based on crossover operation

T(m)=floor[α ₁ *In(α ₂ +m)] (5)

where α ₁ and α ₂ are scaling constants, and m is the current iteration number. T(m) is the current counter. When the iteration counter is greater than this value, the cross-initialization operation is performed.

(2) Adaptive step size update method

where M _max is the maximum number of iterations, o is a constant, and ub _d and lb _d are the upper and lower boundaries of the d-dimensional variable, respectively.

Example 2

An embodiment of the present invention provides a multi-UAV path planning and spectrum resource allocation system, as shown in FIG. 5 , including:

The simulation distribution module is used to simulate the distribution of post-disaster ground terminals and the distribution of hot spots respectively by using a preset simulation method to obtain a plurality of clusters of ground terminals centered on the hot spots; this module executes the method described in step S11 in Embodiment 1 , and will not be repeated here.

The UAV deployment module is used to configure UAVs for each ground terminal according to the distance between each UAV and each cluster and the number of ground terminals that each UAV can access; this module implements the embodiment The method described in step S12 in 1 will not be repeated here.

The spectrum resource allocation module is used to realize the channel access of UAVs by using TDMA technology. Based on the interference parameters of other UAVs and the channel gain between them and the ground terminal, it calculates the current amount of data received by each UAV. According to the current amount of data received by each UAV, combined with multi-dimensional influence parameters, with the goal of obtaining the maximum layout benefit, the COBSO intelligent algorithm is used to allocate frequency resources to each UAV; this module executes the steps in Embodiment 1 The method described in S13 will not be repeated here.

Example 3

An embodiment of the present invention provides a computer device, as shown in FIG. 6 , including: at least one processor 401 , such as a CPU (Central Processing Unit, central processing unit), at least one communication interface 403 , a memory 404 , and at least one communication bus 402 . Among them, the communication bus 402 is used to realize the connection and communication between these components. The communication interface 403 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Ramdom Access Memory, volatile random access memory), or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 404 can optionally also be at least one storage device located away from the aforementioned processor 401 . The processor 401 may execute the method for multi-UAV path planning and spectrum resource allocation in Embodiment 1. A set of program codes are stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the path planning and spectrum resource allocation method for multi-UAVs in Embodiment 1.

The communication bus 402 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus or the like. The communication bus 402 can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one line is shown in FIG. 6, but it does not mean that there is only one bus or one type of bus.

The memory 404 may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviation: RAM); the memory may also include non-volatile memory (English: non-volatile memory) memory), such as flash memory (English: flash memory), hard disk (English: hard diskdrive, abbreviation: HDD) or solid-state drive (English: solid-state drive, abbreviation: SSD); the memory 404 may also include the above types of combination of memory.

The processor 401 may be a central processing unit (English: central processing unit, abbreviation: CPU), a network processor (English: network processor, abbreviation: NP), or a combination of CPU and NP.

The processor 401 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (English: application-specific integrated circuit, abbreviation: ASIC), a programmable logic device (English: programmable logic device, abbreviation: PLD) or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviation: CPLD), a field programmable gate array (English: field-programmable gate array, abbreviation: FPGA), a general array logic (English: generic arraylogic , abbreviation: GAL) or any combination thereof.

Optionally, memory 404 is also used to store program instructions. The processor 401 may invoke program instructions to implement the method for path planning and spectrum resource allocation for multiple UAVs in Embodiment 1 of the present application.

Embodiments of the present invention further provide a computer-readable storage medium, where computer-executable instructions are stored on the computer-readable storage medium, and the computer-executable instructions can execute the method for multi-UAV path planning and spectrum resource allocation in Embodiment 1 . The storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (Flash Memory), a hard disk (Hard DiskDrive, Abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memory.

Obviously, the above-mentioned embodiments are only examples for clear description, and are not intended to limit the implementation manner. For those of ordinary skill in the art, changes or modifications in other different forms can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. However, the obvious changes or changes derived from this are still within the protection scope of the present invention.

Claims (10)

1. a path planning and spectrum resource allocation method of many unmanned aerial vehicles, is characterized in that, comprises as follows: Using the preset simulation method to simulate the distribution of post-disaster ground terminals and the distribution of hotspots, respectively, to obtain a plurality of clusters of ground terminals centered on the hotspots; Configure UAVs for each ground terminal according to the distance between each UAV and each cluster and the number of ground terminals each UAV can access; Using TDMA technology to realize the channel access of UAVs, based on the interference parameters of other UAVs and the channel gain between the UAV and the ground terminal, calculate the current amount of data received by each UAV. The amount of received data, combined with multi-dimensional influence parameters, aims to obtain the maximum layout benefit, and uses the COBSO intelligent algorithm to allocate frequency resources for each UAV, optimize the UAV trajectory and deployment quantity. 2. The method for multi-UAV path planning and spectrum resource allocation according to claim 1, wherein the preset simulation method is used to simulate the distribution of post-disaster ground terminals and the distribution of hotspots respectively, and obtain a plurality of hotspots. The process of clustering for centered ground terminals, including: The distribution of post-disaster ground terminals is simulated by Thomas cluster process, and the distribution of hot spots is simulated by Poisson point process, and several clusters of ground terminals centered on hot spots are obtained. 3. the path planning and spectrum resource allocation method of multiple drones according to claim 1, is characterized in that, described according to the distance of each drone and each cluster and each drone can access The number of ground terminals, the process of configuring the drone for each ground terminal, including: According to the distance between each drone and each cluster and the number of ground terminals that each drone can access, deploy a cluster drone for each cluster in turn, and use auxiliary drones for the unclustered ground. Terminals and the current cluster of UAVs cannot effectively cover the ground terminals with time-sensitive data. 4. The path planning and spectrum resource allocation method of multiple drones according to claim 1, is characterized in that, the process of deploying cluster drones for a single cluster, comprising: Determine whether the current cluster is associated with cluster drones; When the current cluster is not associated with a cluster of drones, find all the drones currently within the coverage of the drones, and sort them from near to far in order of distance; Associate the cluster drones that are closest to the current cluster and whose number of connected ground terminals has not reached the upper limit with the current cluster; when the number of all cluster drones connected to the ground terminals reaches the upper limit, the current cluster is Cluster assigns new cluster drones. 5. The path planning and spectrum resource allocation method of multiple unmanned aerial vehicles according to claim 1, is characterized in that, described utilizing TDMA technology to realize the process of the channel access of unmanned aerial vehicle, comprising: When the channel state between the ground terminal and the UAV satisfies the signal-to-noise ratio condition, the ground terminal establishes communication with the UAV, and the UAV uses the TDMA technology to communicate with each ground terminal in the cluster under its jurisdiction. 6. The path planning and spectrum resource allocation method of multiple unmanned aerial vehicles according to claim 1, characterized in that, based on the interference parameters of other unmanned aerial vehicles, and the channel gain between the ground terminal, calculate each The process of how much data the drone is currently receiving, including: According to the channel gain between the UAV and each ground terminal under its jurisdiction in the current time slot, and the interference parameters of the UAV to the channel by other UAVs in the current time slot, it is calculated that there is no one in the current time slot. The instantaneous reach rate of the machine; According to the instantaneous reach rate of the UAV in the current time slot, the size of the time slot, the data packet generation time of the ground terminal, and the data packet expiration time, the current amount of data received by the UAV is calculated. 7. The method for multi-UAV path planning and spectrum resource allocation according to claim 1, wherein the multi-dimensional influence parameters comprise: The packet size, delay sensitivity, current UAV position and remaining spectrum resources of each ground terminal. 8. A path planning and spectrum resource allocation system for multiple unmanned aerial vehicles, comprising: The simulation distribution module is used to simulate the distribution of post-disaster ground terminals and the distribution of hotspots by using a preset simulation method to obtain a plurality of clusters of ground terminals centered on the hotspots; The UAV deployment module is used to configure UAVs for each ground terminal according to the distance between each UAV and each cluster and the number of ground terminals each UAV can access; The spectrum resource allocation module is used to realize the channel access of UAVs by using TDMA technology. Based on the interference parameters of other UAVs and the channel gain between them and the ground terminal, calculate the current amount of data received by each UAV. According to the current amount of data received by each UAV, combined with multi-dimensional influence parameters, with the goal of obtaining the maximum layout benefit, the COBSO intelligent algorithm is used to allocate frequency resources to each UAV, optimize the UAV trajectory and deployment quantity. 9. A computer device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, The instructions are executed by the at least one processor, so that the at least one processor executes the multi-UAV path planning and spectrum resource allocation method according to any one of claims 1-7. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the multi-function method according to any one of claims 1-7. Human-machine path planning and spectrum resource allocation method.

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