A multi-UAV communication system based on heterogeneous UAVs and energy consumption optimization method

Abstract

The present invention discloses a multi-UAV communication system based on heterogeneous UAVs, wherein the system includes a computing UAV 0 , communication UAV 1 , a ground power supply center and several terminals; the communication type UAV 1 Respectively with the computing UAV 0 and a plurality of said terminals in communication connection; said communication type UAV 1 Used to collect the task information uploaded by the terminal and send it to the computing UAV 0 Processing; the ground power supply center and the computing UAV 0 Electrically connected, the ground power supply center is used to supply the computing UAV 0 Provide energy. The present invention also discloses a multi-UAV energy consumption optimization method based on heterogeneous UAVs. The present invention aims to solve the problem that the existing UAV-assisted MEC system has a large energy load and cannot sustain endurance.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a multi-UAV communication system based on heterogeneous UAVs and an energy consumption optimization method. Background Art

[0002] Mobile Edge Computing (MEC) is a technology that helps resource-constrained terminals process communication and computing delay tasks. It has received widespread attention from academia and industry in recent years. The core idea of ​​MEC is to move the cloud computing platform in traditional data from the core network to the edge of the mobile network to achieve efficient use of various system resources. MEC effectively integrates wireless network and Internet technologies, adds computing, storage, processing and other functions on the wireless network side, builds an open platform to implant applications, and integrates wireless network and business through information interaction between wireless network and business server, upgrading traditional wireless base stations to intelligent base stations. By moving server resources to the edge side (a general term for devices that provide processing task services to users in the MEC system) very close to users, the corresponding delay of users can be greatly reduced. At the same time, the transmission path when responding to user tasks becomes shorter, and the energy used is greatly reduced. Although base stations can have powerful computing capabilities, they are immobile and expensive. However, communication terminals have certain mobility. Therefore, when some terminals are in areas where the communication capabilities of base stations are weak or cannot be covered, rebuilding new base stations is obviously not the best solution.

[0003] Considering the above situation, more researchers have turned their attention to the unmanned aerial vehicle (UAV) assisted MEC system. The MEC system consists of edge servers and terminals. UAVs are added to the MEC system. Due to the easy deployment and high mobility of UAVs, we usually use UAVs to assume some of the functions of the server, or even directly assume all the functions of the server. For example, when the base station is far away from the terminal, UAVs are used to collect terminal data to ensure the coverage of all users of the MEC system and reduce the loss of user data caused by long-distance transmission; or when there is no base station and the amount of data that the terminal needs to process is small, UAVs are directly used to collect and process terminal data and then return it to the device. The system with UAVs has the advantages of easy deployment, mobile controllability and superior communication links. By adding UAVs with computing power to the MEC system, the problem of insufficient computing resources can be alleviated; the air-to-ground communication link of UAVs can effectively reduce the impact of communication noise caused by ground obstacles; the mobility of UAVs can effectively shorten the communication distance between the terminal and the edge computing server, establish a strong communication link, and improve communication efficiency.

[0004] In a single-drone-assisted MEC system, the task processing of the terminal generally involves first collecting the terminal data, then performing calculations on the UAV or forwarding it to the base station for calculation, and finally returning the calculation results to the terminal. For a single-drone-assisted MEC system that does not rely on a base station, the UAV generates too much energy load due to calculations and flight, which greatly reduces the continuous working capacity of the entire system. In a multi-drone-assisted MEC system, the collaborative work of multiple drones can make the entire system more efficient in processing terminal tasks, but using multiple UAVs at the same time will cause the system to require higher energy overhead. Summary of the invention

[0005] The main purpose of the present invention is to provide a multi-UAV communication system based on heterogeneous UAVs and an energy consumption optimization method, aiming to solve the problem that the existing UAV-assisted MEC system has a large energy load and cannot sustain endurance.

[0006] To achieve the above-mentioned object, the present invention provides a multi-UAV communication system based on heterogeneous UAVs and an energy consumption optimization method, wherein the multi-UAV communication system based on heterogeneous UAVs includes a computing UAV0, a ​​communication UAV1, a ground energy supply center and several terminals;

[0007] The communication type UAV1 is respectively connected to the computing type UAV0 and the plurality of terminals for communication; the communication type UAV1 is used to collect the task information uploaded by the terminal and send it to the computing type UAV0 for processing;

[0008] The ground power supply center is electrically connected to the computing UAV0, and the ground power supply center is used to provide energy to the computing UAV0.

[0009] One of the preferred solutions is that the ground power supply center is provided with a power judgment unit, and the ground power supply center is provided with a power judgment unit, and the power judgment unit is used to judge whether the ground power supply center and the communication type UAV1 have sufficient power.

[0010] A method for optimizing energy consumption of a multi-UAV communication system based on heterogeneous UAVs includes:

[0011] S1. Initialize system parameters;

[0012] S2, setting the initial trajectory of the communication type UAV1, and calculating the optimal communication scheduling of the system, the communication resource allocation of the communication type UAV1, and the computing resource allocation of the computing type UAV0 under the initial trajectory state;

[0013] S3, obtaining new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 by performing secondary calculation on the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0, and respectively comparing the communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 obtained in step S2 and step S3 to see whether they converge, if so, executing step S4, otherwise, executing step S2;

[0014] S4. Calculate the optimal flight trajectory of the communication type UAV1 according to the obtained optimal communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0;

[0015] S5. Calculate the energy consumption value of the current system using the optimal communication scheduling, communication resource allocation of communication type UAV1, computing resource allocation of computing type UAV0 and flight trajectory of communication type UAV1, and determine whether the energy consumption value converges. If so, define the energy consumption value as the minimum energy consumption value. Otherwise, execute step S2.

[0016] In one of the preferred solutions, the system parameters include the computing resource allocation of computing type UAV0, the communication resource allocation of communication type UAV1 and the flight trajectory of communication type UAV1.

[0017] One of the preferred solutions is that in step S2, the Lagrangian duality method is used to calculate the optimal communication scheduling of the system, the communication resource allocation of the communication type UAV1, and the computing resource allocation of the computing type UAV0.

[0018] In one of the preferred solutions, the communication scheduling includes the communication scheduling between the terminal and the communication type UAV1 and the communication scheduling between the computing type UAV0 and the communication type UAV1.

[0019] In one of the preferred solutions, the communication resource allocation of the communication type UAV1 includes the communication power of the communication type UAV1 in each time slot.

[0020] One of the preferred solutions is that the computing resource allocation of the computing type UAV0 includes the computing frequency of UAV0 in each time slot.

[0021] One of the preferred schemes is that in step S3, the sub-gradient descent method is used to perform a secondary calculation on the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 in step S2 to obtain new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0.

[0022] One of the preferred schemes, the step S3 compares whether the communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 obtained in step S2 and step S3 have converged specifically as follows: subtract the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 in step S2 from the new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0, and judge whether the difference is less than the set accuracy value.

[0023] Beneficial effects:

[0024] In the above technical solution of the present invention, the method includes initializing system parameters; setting the initial trajectory of the communication type UAV1, and calculating the optimal communication scheduling, communication resource allocation of the communication type UAV1 and computing resource allocation of the computing type UAV0 of the system under the initial trajectory state; obtaining new communication scheduling, communication resource allocation of the communication type UAV1 and computing resource allocation of the computing type UAV0 by performing secondary calculation on the optimal communication scheduling, communication resource allocation of the communication type UAV1 and computing resource allocation of the computing type UAV0, and respectively comparing the corresponding values ​​obtained in step S2 and step S3. Whether the communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0 converge; according to the obtained optimal communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0, the optimal flight trajectory of the communication type UAV1 is calculated; using the optimal communication scheduling, the communication resource allocation of the communication type UAV1, the computing resource allocation of the computing type UAV0 and the flight trajectory of the communication type UAV1, the energy consumption value of the current system is calculated, and it is determined whether the energy consumption value converges, and if so, the energy consumption value is defined as the minimum energy consumption value. In the present invention, by setting the optimal communication scheduling of the system, the communication resource allocation of the communication type UAV1, the computing resource allocation of the computing type UAV0 and the flight trajectory of the communication type UAV1, the minimum energy consumption of each drone is achieved, which can effectively solve the problem that the existing drone-assisted MEC system has a large energy load and cannot sustain endurance.

[0025] 2. In the present invention, during the entire working cycle, the computing UAV0 is responsible for collecting mission information, and the communication UAV1 is responsible for processing mission information. Parallel processing is adopted between the drones, which can improve the response speed of the entire system, reduce the user's response delay and the flight energy consumption of the drone.

[0026] 3. In the present invention, by combining the multi-UAV communication system based on heterogeneous UAVs with the MEC system, the MEC system can replace the base station to complete edge computing and work for a long time in the absence of a base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0028] Figure 1 This is a schematic diagram of the structure of a multi-UAV communication system based on heterogeneous UAVs according to an embodiment of the present invention;

[0029] Figure 2 It is a schematic diagram of the structure of parallel processing of a multi-UAV communication system based on heterogeneous UAVs according to an embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of a flow chart of power determination of a multi-UAV communication system based on heterogeneous UAVs according to an embodiment of the present invention;

[0031] Figure 4 The present invention is a flowchart of a method for optimizing energy consumption of multiple UAVs based on heterogeneous UAVs according to an embodiment of the present invention.

[0032] Description of Figure Numbers:

[0033] Label name Label name 1 Ground energy supply center 2 terminal 3 <![CDATA[Communication UAV 1]]> 4 <![CDATA[Computational UAV0]]>

[0034] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with the implementation methods and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] It should be noted that all directional indications (such as up, down, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0037] In addition, in the present invention, the descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0038] Furthermore, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in the field. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0039] See also Figure 1-3According to one aspect of the present invention, the present invention provides a multi-UAV communication system based on heterogeneous UAVs and an energy consumption optimization method, wherein the multi-UAV communication system based on heterogeneous UAVs includes: a computing UAV0, a ​​communication UAV1, a ground energy supply center and a plurality of terminals; the communication UAV1 is respectively communicated with the computing UAV0 and the plurality of the terminals; the communication UAV1 is used to collect the task information uploaded by the terminal and send it to the computing UAV0 for processing; the ground energy supply center is electrically connected to the computing UAV0, and the ground energy supply center is used to provide energy to the computing UAV0.

[0040] Specifically, in this embodiment, the computing UAV0 adopts a computing UAV, and the communication UAV1 adopts a communication UAV. The communication UAV1 is not provided with a computing module, and has a smaller mass and lower flight energy consumption than the computing UAV0. The computing UAV0 hovers in a fixed area and is responsible for calculating the mission information transmitted by the communication UAV1. The communication UAV1 takes the computing UAV0 as the center and flies in a circular, elliptical or circular-like trajectory around the computing UAV0, collects mission information from multiple terminals, and transmits it to the computing UAV0 to process the mission information of the terminals. The entire working cycle can be Divided into several parts, during the entire working cycle, the terminal uploads the task information to the communication type UAV1, the communication type UAV1 sends the collected task information to the computing type UAV0, and the computing type UAV0 calculates the task information transmitted by the communication type UAV1 at the same time, thereby realizing parallel processing of information of multiple UAVs, reducing the user's response delay and the flight energy consumption of the UAV, and improving the response speed of the entire system; in the present invention, at least one computing type UAV0 and communication type UAV1 are provided to realize the collection and processing of task information, and specifically multiple computing type UAV0 and communication type UAV1 can be provided according to the on-site conditions, which is not limited by the present invention.

[0041] Specifically, in this embodiment, the ground power supply center is provided with a power judgment unit, and the power judgment unit is used to judge whether the power of the ground power supply center and the communication type UAV1 is sufficient; after the system is powered on, the communication type UAV1 is responsible for collecting the task information of each terminal, and the computing type UAV0 is responsible for processing the task information of each terminal transmitted by the communication type UAV1; the power judgment unit of the ground power supply center is used to judge whether the power of the communication type UAV1 is less than a first power threshold, and if so, the operator is reminded to replace the communication type UAV1, otherwise continue to work; the power judgment unit of the ground power supply center is used to judge whether the power of the ground power supply center is less than a second power threshold, and if so, the operator is reminded to replace the power supply of the ground power supply center, otherwise continue to work.

[0042] Specifically, in this embodiment, the multi-UAV communication system based on heterogeneous UAVs is provided with a communication model, and the communication model includes the communication between the communication type UAV1 and several terminals and between the communication type UAV1 and the computing type UAV0. It is assumed that the channel between the communication type UAV1 and the several terminals and the computing type UAV0 is dominated by the LOS channel and has no small-scale fading. The LOS channel refers to that there is no obstruction between two objects communicating with each other. Therefore, in the nth time slot of the communication type UAV1, the average path fading of the communication between the terminal and the communication type UAV1 can be expressed as:

[0043]

[0044] Where n represents the nth time slot, k represents the kth terminal, β0 is the transmission channel reference gain, (x k ,,y k , 0) is the location coordinate of the terminal, (x[n], y[n], H) is the location coordinate of the drone at the nth time slot, and H is the flight altitude of the drone;

[0045] The communication rate between the terminal and the communication type UAV1 is calculated by the average path fading of the communication between the terminal and the communication type UAV1:

[0046]

[0047] Among them, B k is the transmission bandwidth from the terminal to the communication type UAV1, α k is the communication schedule between the communication type UAV1 and the terminal k, P0 is the unloading power of the terminal, σ 2 is the Gaussian white noise power,

[0048] In the n+1th time slot of the communication type UAV1, the average path fading between the communication type UAV1 and the calculation type UAV0 can be expressed as:

[0049]

[0050] Among them, (x[n], y[n], H) is the position coordinate of the UAV in the nth time slot, and (x0, y0, 0) is the hovering position coordinate of the computing UAV0;

[0051] The communication rate between communication UAV1 and computing UAV0 can be calculated by the average path fading between communication UAV1 and computing UAV0:

[0052]

[0053] Among them, B m is the transmission bandwidth from communication UAV1 to computing UAV0, is the communication scheduling between communication UAV1 and computing UAV0, P k is the communication power between the communication type UAV1 and the terminal k;

[0054] Through the communication model, the average path fading and communication rate between the communication type UAV1 and several terminals and between the communication type UAV1 and the computing type UAV0 are obtained, providing a calculation basis for the subsequent realization of multi-inorganic energy consumption optimization.

[0055] The multi-UAV communication system based on heterogeneous UAVs is provided with a task calculation model. f[n+2] is defined as the calculation frequency of the computing UAV0 in the n+2th time slot. Then, the task calculation amount of the computing UAV0 in the n+2th time slot can be calculated. The formula is:

[0056]

[0057] Among them, f is the calculation frequency of the computing UAV0, C is the number of CPU cycles, and δ is the time slot length;

[0058] By calculating the task calculation amount of computing UAV0 in the n+2th time slot, the energy consumed by computing UAV0 in the n+2th time slot can be obtained. The formula is:

[0059] Among them, E comp is the allowable energy consumption of computing UAV0, is the effective capacitance coefficient;

[0060] The multi-UAV communication system based on heterogeneous UAVs is provided with an energy consumption model. The total energy consumption of the UAVs is divided into four parts, including: the energy consumed by the flight and communication of the communication type UAV1, and the energy consumed by the hovering and calculation of the computing type UAV0; the energy consumption expression formula of each part is as follows:

[0061]

[0062]

[0063]

[0064]

[0065] Among them, E comm is the communication energy consumption of communication type UAV1, E H is the hovering energy consumption of computing UAV0, E fly is the flight energy consumption of the communication type UAV1; N is the number of time slots into which the time period is divided, K is the number of ground terminals, is the hovering power of computing UAV0, C1 and C2 are the correlation coefficients for calculating flight energy consumption, v is the speed of communication UAV1, a is the acceleration of communication UAV1, and g is the acceleration of gravity;

[0066] See also Figure 4 According to another aspect of the present invention, the present invention provides a multi-UAV communication system based on heterogeneous UAVs and an energy consumption optimization method, wherein the multi-UAV energy consumption optimization method based on heterogeneous UAVs includes:

[0067] S1. Initialize system parameters; wherein the system parameters include computing resource allocation of computing UAV0, communication resource allocation of communication UAV1, and flight trajectory of communication UAV1;

[0068] S2, setting the initial trajectory of the communication type UAV1, and calculating the optimal communication scheduling of the system, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0 under the state of the initial trajectory by the Lagrangian dual method; the communication scheduling includes the communication scheduling between the terminal and the communication type UAV1 and the communication scheduling between the computing type UAV0 and the communication type UAV1; the communication resource allocation of the communication type UAV1 includes the communication power of the communication type UAV1 in each time slot; the computing resource allocation of the computing type UAV0 includes the computing frequency of the computing type UAV0 in each time slot;

[0069] S3, using the sub-gradient descent method to perform secondary calculation on the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 in step S2 to obtain new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0, and compare whether the communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 obtained in step S2 and step S3 converge, if so, execute step S4, otherwise, execute step S2;

[0070] S4. Calculate the optimal flight trajectory of the communication type UAV1 using the CVX algorithm according to the obtained optimal communication scheduling, the communication resource allocation of the communication type UAV1, and the computing resource allocation of the computing type UAV0;

[0071] S5. Calculate the energy consumption value of the current system using the optimal communication scheduling, communication resource allocation of communication type UAV1, computing resource allocation of computing type UAV0 and flight trajectory of communication type UAV1, and determine whether the energy consumption value converges. If so, define the energy consumption value as the minimum energy consumption value. Otherwise, execute step S2.

[0072] Specifically, in the present embodiment, in the step S2, given the initial trajectory of the UAV, the optimal communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0 are calculated using the Lagrange dual method; Q is set as the set of the speed, acceleration and position coordinates of the communication type UAV1, and the Lagrange dual method is used to calculate the optimal communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0 in combination with the data obtained in the communication model, the task calculation model and the energy consumption model, as follows:

[0073]

[0074] Among them, the C1 * It means that the optimal unloading data in the nth time slot is greater than the actual unloading data amount between the terminal and the communication type UAV1; k is the size of the task data to be transmitted, K is the terminal set, and N is the time slot set;

[0075]

[0076] Among them, the C2 * It means that the communication type UAV1 can communicate with only one computing type UAV0 in each time slot;

[0077]

[0078] Wherein, C3 indicates that the optimal unloading data in the nth time slot is greater than the actual unloading data amount between the communication type UAV1 and the computing type UAV0;

[0079]

[0080] Wherein, C5 represents the relationship between the amount of data processed by the computing UAV0 and the computing frequency;

[0081]

[0082] Wherein, C7 represents the calculation frequency of the computing UAV0 in each time slot is subject to f max Limitations; max is the maximum calculation frequency of computing UAV0;

[0083]

[0084] Wherein, C8 represents the communication power of the communication type UAV1 in each time slot is affected by P max Restrictions; P max is the maximum communication power of the communication type UAV1;

[0085] Specifically, in the present embodiment, in the step S3, the subgradient descent method is used to update the Lagrange multiplier in the Lagrange dual method, and the Lagrange multiplier is an intermediate variable in the Lagrange dual method. The optimal communication scheduling, communication resource allocation of the communication type UAV1, and computing resource allocation of the computing type UAV0 are recalculated by the new Lagrange multiplier to obtain new communication scheduling, communication resource allocation of the communication type UAV1, and computing resource allocation of the computing type UAV0, and the optimal communication scheduling, communication resource allocation of the communication type UAV1 in step S2 are replaced by the new communication scheduling, communication resource allocation of the communication type UAV1, and computing resource allocation of the computing type UAV0. The computing resource allocation of the allocation and computing type UAV0 is subtracted from the new communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0 respectively to determine whether the obtained data converges, that is, to determine whether the difference is less than the set accuracy value. If it is less than the set accuracy value, the subsequent steps are executed, otherwise the optimal communication scheduling of the system under the state of the initial trajectory, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0 are recalculated. The set accuracy value is 0.001, which is not limited in the present invention and can be set according to actual conditions.

[0086] Specifically, in this embodiment, in step S4, according to the obtained optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0, the optimal flight trajectory of communication type UAV1 in this case is calculated using CVX convex optimization solver, as follows:

[0087]

[0088] Among them, the C1 * It means that the optimal unloading data in the nth time slot is greater than the actual unloading data amount between the terminal and the communication type UAV1;

[0089]

[0090] Wherein, C3 indicates that the optimal unloading data in the nth time slot is greater than the actual unloading data amount between the computing UAV0 and the communication UAV1;

[0091]

[0092]

[0093] Wherein, C9 and C10 represent the relationship between displacement, velocity, acceleration and time slot length;

[0094] C11:q[1]=q[N+1]

[0095] C12:v[1]=v[N+1]

[0096] Wherein, C11 and C12 indicate that the initial position and speed are equal to the final position and speed in the flight cycle; q is the position coordinate of the communication type UAV1;

[0097]

[0098]

[0099] Among them, C13 and C14 represent the maximum speed and acceleration of the drone respectively subject to V max and A max Limitation of V max is the maximum moving speed of the communication type UAV1, A max It is the maximum acceleration of communication type UAV1.

[0100] Specifically, in this embodiment, in step S5, the energy consumption value of the system is calculated by using the optimal communication scheduling, the communication resource allocation of the communication type UAV1, the computing resource allocation of the computing type UAV0, and the flight trajectory of the communication type UAV1, and Q is set as the set of the speed, acceleration, and position coordinates of the communication type UAV1, and the computing type UAV0 can complete the calculation of the data uploaded by the terminal within the time slot n, and the set communication model, task calculation model, and energy consumption model are combined to obtain the minimum energy consumption of the UAV, and the minimum energy consumption is (E fly +E comm +E comp +E H ) min ; The details are as follows:

[0101]

[0102] Wherein, C1 indicates that the optimal unloading data in the nth time slot is greater than the actual unloading data amount between the terminal and the communication type UAV1;

[0103]

[0104] Wherein, C2 indicates that the communication type UAV1 can communicate with only one terminal in each time slot;

[0105]

[0106] Wherein, C3 indicates that the optimal unloading data in the nth time slot is greater than the actual unloading data amount between the communication type UAV1 and the computing type UAV0;

[0107]

[0108] Wherein, C4 indicates that the unloaded data of the kth terminal in the nth time slot will be forwarded in the n+1th time slot;

[0109]

[0110] Wherein, C5 represents the relationship between the amount of data processed by the computing UAV0 and the computing frequency;

[0111]

[0112] The C6 indicates that the total uninstall data of the kth terminal has an upper limit;

[0113]

[0114] Wherein, C7 represents the calculation frequency of the computing UAV0 in each time slot is subject to f max restrictions;

[0115]

[0116] Wherein, C8 represents the communication power of the communication type UAV1 in each time slot is affected by P max restrictions;

[0117]

[0118]

[0119] Wherein, C9 and C10 represent the relationship between displacement, velocity, acceleration and time slot length;

[0120] C11:q[1]=q[N+1]

[0121] C12:v[1]=v[N+1]

[0122] Wherein, C11 and C12 indicate that the initial position and speed are equal to the final position and speed in the flight cycle;

[0123]

[0124]

[0125] C13 and C14 represent the maximum speed and acceleration of the drone, respectively. max and A max restrictions.

[0126] Specifically, in the present embodiment, in the step S5, the energy consumption value of the current system is calculated, and it is also necessary to determine whether the energy consumption value converges, that is, to compare the calculated energy consumption value of the current system with the system energy consumption value calculated last time. When the difference is less than the calculation accuracy value, the calculated energy consumption value of the current system is defined as the minimum energy consumption value, and the optimal communication scheduling, communication resource allocation of communication type UAV1, computing resource allocation of computing type UAV0 and flight trajectory of communication type UAV1 are systematically applied, and the calculation procedure of the current energy consumption value is launched. On the contrary, if the difference between the two energy consumption values ​​does not meet the calculation accuracy value, the calculation is performed again and the step S2 is returned. The calculation accuracy value is 0.001, which is not limited by the present invention and can be set according to actual conditions.

[0127] Specifically, in this embodiment, the optimal communication scheduling of the system is obtained through the above-mentioned multi-UAV energy consumption optimization method based on heterogeneous UAVs, and the computing resource allocation of the computing type UAV0 is unloaded in real time, and the optimal flight trajectory of the communication type UAV1 is set, so as to achieve the minimum energy consumption of the UAV and improve the endurance of the system; for example, the present invention can be applied to disaster-stricken areas where communication base stations have been lost. In this area, base stations are difficult to repair and communications within the area cannot be carried out. When carrying out emergency rescue, the system can be set up in a suitable location, such as on a flat and high rooftop or hill. The computing type UAV0 is directly powered by the ground power supply center to ensure that its computing power will not be directly interrupted. The computing type UAV0 is used to calculate the task information uploaded by the terminal, and the communication type UAV1 is centered on the computing type UAV0 and forms a circular, elliptical or circle-like shape around the computing UAV0. shaped trajectory flight, collect mission information from multiple terminals, the communication type UAV1, as a communication type UAV, does not need to bear the energy burden brought by calculation in the present invention, and its endurance performance can be greatly improved; it only needs to ensure that the power of the ground energy supply center is sufficient, the computing type UAV0 can continue to work, and the UAVs all adopt the optimal communication scheduling, communication resource allocation of the communication type UAV1, computing resource allocation of the computing type UAV0 and the flight trajectory of the communication type UAV1, which can further improve the endurance of the entire system and reduce energy loss; this system can be specifically applied to disaster-stricken areas, and the joint optimization of the trajectory of the communication type UAV1, the selection of the communication link between the communication type UAV1 and the user terminal, the power of the communication type UAV1 and the computing resources of the computing type UAV0 can maximize the system power utilization and improve the endurance of the system.

[0128] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A multi-UAV communication system based on heterogeneous UAVs, characterized in that: It includes computing UAV0, communication UAV1, ground power supply center and several terminals; The communication type UAV1 is respectively connected to the computing type UAV0 and the plurality of terminals for communication; the communication type UAV1 is used to collect the task information uploaded by the terminal and send it to the computing type UAV0 for processing; The ground power supply center is electrically connected to the computing UAV0, and the ground power supply center is used to provide energy to the computing UAV0; The multi-UAV communication system based on heterogeneous UAVs is provided with a communication model, and the communication model includes the communication between the communication type UAV1 and several terminals and between the communication type UAV1 and the computing type UAV0; specifically: calculating the average path fading of the communication between the terminal and the communication type UAV1 in the nth time slot of the communication type UAV1; calculating the communication rate between the terminal and the communication type UAV1 through the average path fading of the communication between the terminal and the communication type UAV1; calculating the average path fading between the communication type UAV1 and the computing type UAV0 in the n+1th time slot of the communication type UAV1; calculating the communication rate between the communication type UAV1 and the computing type UAV0 through the average path fading between the communication type UAV1 and the computing type UAV0; The multi-UAV communication system based on heterogeneous UAVs is provided with a task calculation model; specifically: calculating the task calculation amount of the computing type UAV0 in the n+2th time slot, and obtaining the energy consumed by the computing type UAV0 in the n+2th time slot through the task calculation amount of the computing type UAV0 in the n+2th time slot; The multi-UAV communication system based on heterogeneous UAVs is provided with an energy consumption model, including the energy consumed by the flight and communication of the communication type UAV1, and the energy consumed by the hovering and calculation of the computing type UAV0.

2. The multi-UAV communication system based on heterogeneous UAVs according to claim 1 is characterized in that: The ground power supply center is provided with a power judgment unit, and the power judgment unit is used to judge whether the ground power supply center and the communication type UAV1 have sufficient power.

3. A method for optimizing energy consumption of a multi-UAV communication system based on heterogeneous UAVs according to any one of claims 1 to 2, characterized in that: include: S1. Initialize system parameters; S2, setting the initial trajectory of the communication type UAV1, and calculating the optimal communication scheduling of the system, the communication resource allocation of the communication type UAV1, and the computing resource allocation of the computing type UAV0 under the initial trajectory state; S3, obtaining new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 by performing secondary calculation on the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0, and respectively comparing the communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 obtained in step S2 and step S3 to see whether they converge, if so, executing step S4, otherwise, executing step S2; S4. Calculate the optimal flight trajectory of the communication type UAV1 according to the obtained optimal communication scheduling, the communication resource allocation of the communication type UAV1 and the computing resource allocation of the computing type UAV0; S5. Calculate the energy consumption value of the current system using the optimal communication scheduling, communication resource allocation of communication type UAV1, computing resource allocation of computing type UAV0 and flight trajectory of communication type UAV1, and determine whether the energy consumption value converges. If so, define the energy consumption value as the minimum energy consumption value. Otherwise, execute step S2.

4. The energy consumption optimization method of a multi-UAV communication system based on heterogeneous UAVs according to claim 3 is characterized in that: The system parameters include the computing resource allocation of computing type UAV0, the communication resource allocation of communication type UAV1 and the flight trajectory of communication type UAV1.

5. The energy consumption optimization method of a multi-UAV communication system based on heterogeneous UAVs according to claim 4 is characterized in that: In step S2, the Lagrangian dual method is used to calculate the optimal communication scheduling of the system, the communication resource allocation of the communication type UAV1, and the computing resource allocation of the computing type UAV0.

6. The method for optimizing energy consumption of multiple UAVs based on heterogeneous UAVs according to claim 4 is characterized in that: The communication scheduling includes the communication scheduling between the terminal and the communication type UAV1 and the communication scheduling between the computing type UAV0 and the communication type UAV1.

7. The energy consumption optimization method of a multi-UAV communication system based on heterogeneous UAVs according to claim 4 is characterized in that: The communication resource allocation of the communication type UAV1 includes the communication power of the communication type UAV1 in each time slot.

8. The energy consumption optimization method of a multi-UAV communication system based on heterogeneous UAVs according to claim 4 is characterized in that: The computing resource allocation of the computing type UAV0 includes the computing frequency of UAV0 in each time slot.

9. The energy consumption optimization method of a multi-UAV communication system based on heterogeneous UAVs according to claim 3 is characterized in that: In step S3, the subgradient descent method is used to perform a secondary calculation on the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 in step S2 to obtain new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0.

10. The energy consumption optimization method of a multi-UAV communication system based on heterogeneous UAVs according to claim 9 is characterized in that: The step S3 compares whether the communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 obtained in step S2 and step S3 have converged specifically as follows: subtract the optimal communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0 in step S2 from the new communication scheduling, communication resource allocation of communication type UAV1 and computing resource allocation of computing type UAV0, respectively, and determines whether the difference is less than the set accuracy value.

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