Unmanned aerial vehicle track determination method and device based on relay communication, and electronic equipment

Abstract

The invention discloses an unmanned aerial vehicle track determination method and device based on relay communication and electronic equipment, and relates to the technical field of unmanned aerial vehicle communication, and the unmanned aerial vehicle track determination method comprises the steps: constructing a relay communication model, determining an unmanned aerial vehicle parameter value and a space parameter value based on the relay communication model, and determining an initial velocity vector of an unmanned aerial vehicle, under the condition that a plurality of preset constraint conditions are met, performing iteration on a preset energy efficiency optimal function based on the initial velocity vector, the unmanned aerial vehicle parameter value and the space parameter value to obtain a current velocity vector after each iteration, and when the difference value between the current vector value of the current velocity vector and the last vector value of the last velocity vector after last iteration is smaller than a preset velocity threshold value, iteration is stopped, and the trajectory of the unmanned aerial vehicle is determined based on the current velocity vector. The technical problem that the energy efficiency of an unmanned aerial vehicle relay system is low due to the fact that the track of the unmanned aerial vehicle cannot be determined according to the field condition in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle communication technology, and in particular to a method for determining a trajectory of an unmanned aerial vehicle based on relay communication, a device thereof, and electronic equipment. Background Art

[0002] In the wireless communication industry, the use of drones as relay nodes is becoming increasingly popular. Unlike ground relay nodes, drone relay nodes can significantly enhance communication performance and expand signal coverage areas. They can be flexibly deployed according to actual needs and establish direct line-of-sight links with ground equipment. With the advancement of drone technology, the reduction of drone production and use costs, and the improvement of carrying capacity, more and more drones are used as relay nodes for communication, especially in the field of wireless communications. Using drones as relay nodes can effectively make up for the shortcomings of ground relays.

[0003] However, due to the limited load capacity of drones, after being equipped with the necessary communication devices, there is not enough remaining space to carry sufficient energy. Therefore, the problem faced by the current drone relay system is how to achieve more efficient communication operations with minimal energy consumption, which will bring better cost efficiency and communication performance for dealing with complex terrain or performing sudden communication tasks.

[0004] In the relevant technologies, the efficiency of point-to-point communication between drones and ground nodes is mainly optimized, and the flight trajectory of drones also uses a fixed orbit, which cannot be flexibly adjusted according to the on-site conditions, resulting in the energy efficiency of the drone relay system not being able to achieve the optimal level.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present invention provide a method for determining the trajectory of a UAV based on relay communication, a device thereof, and an electronic device, so as to at least solve the technical problem in the related art that the trajectory of a UAV cannot be determined according to the on-site conditions, resulting in low energy efficiency of the UAV relay system.

[0007] According to one aspect of an embodiment of the present invention, a method for determining a UAV trajectory based on relay communication is provided, comprising: constructing a relay communication model, and determining UAV parameter values ​​and space parameter values ​​based on the relay communication model, wherein the relay communication model is used to represent the communication between a source node, a relay node and a destination node; the relay node is implemented by a UAV; the source node is used to send data to the relay node, and the relay node is used to send data to the destination node; determining an initial velocity vector of the UAV, wherein the initial velocity vector comprises: an initial velocity value and an initial velocity direction; under the condition that multiple preset constraints are met, based on the initial velocity vector, the UAV parameter value and the space parameter value, iterating a preset energy efficiency optimal function to obtain a current velocity vector after each iteration; when the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than a preset velocity threshold, stopping the iteration, and determining the UAV trajectory based on the current velocity vector.

[0008] Furthermore, before constructing the relay communication model, it also includes: dividing the UAV flight time into multiple time slots, wherein the time slot length of each time slot is less than a preset time threshold; constructing an energy consumption function based on the number of time slots, the time slot length, speed variables and UAV parameters; constructing a throughput function based on the number of time slots, the time slot length, bandwidth parameters, channel ratio parameters and space parameters; constructing the preset energy efficiency optimal function based on the energy consumption function and the throughput function.

[0009] Furthermore, before constructing the relay communication model, it also includes: constructing information causal constraints, wherein the information causal constraints are used to indicate that the amount of data sent from the relay node to the destination node is less than or equal to the amount of data sent from the source node to the relay node; constructing UAV flight time constraints, and constructing UAV flight speed constraints; constructing UAV carried energy constraints; and characterizing the information causal constraints, the UAV flight time constraints, the UAV flight speed constraints, and the energy constraints carried by the UAV as the preset constraints.

[0010] Furthermore, the step of iterating the preset energy efficiency optimal function based on the initial velocity vector, the drone parameter value and the space parameter value includes: using a fractional ladder programming algorithm to convert the preset energy efficiency optimal function into an initial objective function; based on relaxation variables, using a continuous convex approximation algorithm to convert the initial objective function into a convex optimization function, and converting non-convex constraints into convex constraints, wherein the non-convex constraints refer to the preset constraints with non-convex properties; under multiple convex constraints, iterating the convex optimization function based on the initial velocity vector, the drone parameter value and the space parameter value.

[0011] Furthermore, a fractional ladder programming algorithm is used to convert the preset energy efficiency optimal function into the initial objective function, including: constructing iteration parameters based on the amount of data sent by the relay node to the destination node in each time slot and the energy consumption of the drone in each time slot; based on the iteration parameters, converting the preset energy efficiency optimal function into the initial objective function.

[0012] Furthermore, based on slack variables, a continuous convex approximation algorithm is used to convert the initial objective function into a convex optimization function, and the step of converting non-convex constraints into convex constraints includes: constructing slack variable constraints based on the slack variables and the data sent from the relay node to the destination node, and characterizing the slack variable constraints as the preset constraints; determining the upper bound of the energy consumption of the UAV; based on the energy consumption upper bound, performing Taylor expansion on the initial objective function and the non-convex constraints at a preset distance variable to obtain the convex optimization function and the convex constraints, wherein the preset distance variable refers to the distance variable between the UAV and the destination node.

[0013] Furthermore, under the multiple convex constraint conditions, the step of iterating the convex optimization function based on the initial velocity vector, the drone parameter value and the space parameter value includes: under the multiple convex constraint conditions, based on the initial velocity vector, the drone parameter value and the space parameter value, using the interior point method to perform a cyclic calculation on the convex optimization function to obtain the current process velocity vector and the current process relaxation variable value; when the difference between the current process relaxation variable value and the last process relaxation variable value calculated last time is less than a preset relaxation threshold, stop the calculation to obtain the current velocity vector of the current iteration.

[0014] According to another aspect of an embodiment of the present invention, a device for determining a UAV trajectory based on relay communication is also provided, comprising: a construction unit, configured to construct a relay communication model, and determine a UAV parameter value and a spatial parameter value based on the relay communication model, wherein the relay communication model is used to represent the communication between a source node, a relay node and a destination node; the relay node is implemented by a UAV; the source node is used to send data to the relay node, and the relay node is used to send data to the destination node; a first determination unit, configured to determine an initial velocity vector of the UAV, wherein the initial velocity vector comprises: an initial velocity value and an initial velocity direction; an iteration unit, configured to iterate a preset energy efficiency optimal function based on the initial velocity vector, the UAV parameter value and the spatial parameter value under the condition that multiple preset constraints are satisfied, to obtain a current velocity vector after each iteration; a second determination unit, configured to stop iteration when the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than a preset velocity threshold, and determine the UAV trajectory based on the current velocity vector.

[0015] Furthermore, the UAV trajectory determination device also includes: a first processing module, used to divide the UAV flight time into multiple time slots before constructing the relay communication model, wherein the time slot length of each time slot is less than a preset time threshold; a first construction module, used to construct an energy consumption function based on the number of time slots, the time slot length, the speed variable and the UAV parameter; a second construction module, used to construct a throughput function based on the number of time slots, the time slot length, the bandwidth parameter, the channel ratio parameter and the space parameter; and based on the energy consumption function and the throughput function, construct the preset energy efficiency optimal function.

[0016] Furthermore, the UAV trajectory determination device also includes: a fourth construction module, used to construct an information causal constraint before constructing the relay communication model, wherein the information causal constraint is used to indicate that the amount of data sent from the relay node to the destination node is less than or equal to the amount of data sent from the source node to the relay node; a fifth construction module, used to construct a UAV flight time constraint and a UAV flight speed constraint; a sixth construction module, used to construct an energy constraint carried by the UAV; and a first characterization module, used to characterize the information causal constraint, the UAV flight time constraint, the UAV flight speed constraint and the energy constraint carried by the UAV as the preset constraint.

[0017] Furthermore, the iteration unit includes: a first conversion module, used to convert the preset energy efficiency optimal function into an initial objective function by adopting a fractional ladder programming algorithm; a second conversion module, used to convert the initial objective function into a convex optimization function based on slack variables and a continuous convex approximation algorithm, and convert non-convex constraints into convex constraints, wherein the non-convex constraints refer to the preset constraints with non-convex properties; a first iteration module, used to iterate the convex optimization function based on the initial velocity vector, the drone parameter value and the space parameter value under multiple convex constraints.

[0018] Furthermore, the first conversion module includes: a first construction submodule, used to construct iteration parameters based on the amount of data sent by the relay node to the destination node in each time slot and the energy consumption of the drone in each time slot; a first conversion submodule, used to convert the preset energy efficiency optimal function into the initial objective function based on the iteration parameters.

[0019] Furthermore, the second conversion module includes: a first characterization submodule, used to construct a slack variable constraint condition based on the slack variable and the data sent from the relay node to the destination node, and characterize the slack variable constraint condition as the preset constraint condition; a first determination submodule, used to determine the upper bound of the energy consumption of the UAV; a first calculation submodule, used to perform Taylor expansion on the initial objective function and the non-convex constraint condition at a preset distance variable based on the energy consumption upper bound, to obtain the convex optimization function and the convex constraint condition, wherein the preset distance variable refers to the distance variable between the UAV and the destination node.

[0020] Furthermore, the first iteration module includes: a second calculation submodule, which is used to perform a cyclic calculation on the convex optimization function using the interior point method under multiple convex constraint conditions based on the initial velocity vector, the drone parameter value and the space parameter value to obtain the current process velocity vector and the current process relaxation variable value; a first processing submodule, which is used to stop the calculation when the difference between the current process relaxation variable value and the last process relaxation variable value calculated last time is less than a preset relaxation threshold value to obtain the current velocity vector of the current iteration.

[0021] According to another aspect of an embodiment of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for determining the trajectory of a drone based on relay communication described above is implemented.

[0022] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned methods for determining the trajectory of a drone based on relay communication.

[0023] In the present invention, a relay communication model is constructed, and based on the relay communication model, the drone parameter value, the space parameter value, and the initial velocity vector of the drone are determined. Under the condition of satisfying multiple preset constraints, the preset energy efficiency optimal function is iterated based on the initial velocity vector, the drone parameter value, and the space parameter value to obtain the current velocity vector after each iteration. When the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than the preset speed threshold, the iteration is stopped, and the drone trajectory is determined based on the current velocity vector, thereby solving the technical problem in the related art that the drone trajectory cannot be determined according to the on-site conditions, resulting in low energy efficiency of the drone relay system.

[0024] In the present invention, a UAV relay communication model can be constructed first according to the on-site situation, so as to iteratively calculate the preset energy efficiency optimal function according to the UAV parameter value, space parameter value, and the initial velocity vector of the determined UAV in the UAV relay communication model, under the condition of satisfying multiple preset constraints, until the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than the preset velocity threshold, and then the iteration is stopped to obtain the final velocity vector. After that, the trajectory of the UAV can be determined according to the final velocity vector. In this way, by optimizing the flight trajectory of the UAV, the energy efficiency of the relay communication system can be optimized, thereby achieving the technical effect of relay communication with the optimal energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 is a flow chart of an optional method for determining a drone trajectory based on relay communication according to an embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of an optional UAV relay system communication model according to an embodiment of the present invention;

[0028] Figure 3 is a schematic diagram of iterative convergence of an optional system simulation algorithm according to an embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a drone trajectory simulated by an optional system according to an embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of speed comparison of an optional system simulation according to an embodiment of the present invention;

[0031] Figure 6 is a schematic diagram of energy efficiency comparison of an optional system simulation according to an embodiment of the present invention;

[0032] Figure 7 is a schematic diagram of an optional drone trajectory determination device based on relay communication according to an embodiment of the present invention;

[0033] Figure 8 It is a hardware structure block diagram of an electronic device (or mobile device) for a method for determining a drone trajectory based on relay communication according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) collected and involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with the relevant laws, regulations and standards of the relevant regions, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set between the system and the relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0037] In the present invention, through the analysis of the UAV relay communication system, it can be known that the load capacity of the UAV is limited. After being equipped with the necessary communication devices, the remaining space is insufficient to carry sufficient energy, and more efficient communication operations cannot be achieved with minimal energy consumption. Therefore, the present invention proposes a UAV trajectory determination method based on relay communication, which can bring better cost efficiency and communication performance for complex terrain or performing sudden communication tasks.

[0038] In the present invention, according to the information causal constraints of the source node and the destination node, the system running time, the maximum and minimum speed limits of the UAV flight, and the energy limit carried by the UAV, a joint optimization algorithm based on fractional ladder programming and continuous convex approximation algorithm is proposed to optimize the UAV flight trajectory, thereby achieving the optimization of the energy efficiency of the relay communication system.

[0039] The present invention is described in detail below in conjunction with various embodiments.

[0040] Embodiment 1

[0041] According to an embodiment of the present invention, an embodiment of a method for determining a UAV trajectory based on relay communication is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0042] Figure 1 is a flow chart of an optional method for determining the trajectory of a drone based on relay communication according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0043] Step S101, construct a relay communication model, and based on the relay communication model, determine the drone parameter value and the space parameter value, wherein the relay communication model is used to represent the communication between the source node, the relay node and the destination node; the relay node is implemented by a drone; the source node is used to send data to the relay node, and the relay node is used to send data to the destination node.

[0044] Optionally, when the source node S needs to transmit data to the destination node D, due to the long distance between S and D, the two cannot communicate directly, and the data must be forwarded through the drone relay node R. Therefore, in order to subsequently determine the flight trajectory of the drone, it is necessary to first establish a relay communication model based on the on-site conditions (such as the conditions of the buildings on site, etc.) to simulate the communication between the source node, the relay node (implemented by a drone) and the destination node through the relay communication model, that is, to simulate the source node sending data to the relay node, and the relay node sending data to the destination node.

[0045] In this embodiment, the location coordinates of the source node S that sends the communication data and the location coordinates of the destination node D that receives the communication data can be determined according to the on-site situation, and the distance L between the source node S and the destination node D can be determined, as well as the fixed altitude H of the drone R, the fixed take-off point q0 of the drone (i.e., the location coordinates of the drone take-off) and the destination qF (i.e., the location coordinates of the drone landing). Then, based on these spatial parameter values ​​(i.e., location coordinates, distance, altitude, etc.), a relay communication model is constructed.

[0046] Figure 2 is a schematic diagram of an optional UAV relay system communication model according to an embodiment of the present invention, such as Figure 2 As shown in FIG. 1 , a UAV relay system communication model is established on a three-dimensional coordinate system xyz (xyz is a coordinate axis in length units). The UAV relay system communication model is a communication system model consisting of a ground node S, a destination node D, and a UAV relay node R. The ground node S and the destination node D are located at points S = (0, 0, 0) and D = (x D ,y D , 0). The drone relay node R takes off from the fixed take-off point q0, arrives at the end point qF and lands, and starts at the initial position (x0, y0, H) with an initial velocity v0 and a final velocity v F Arrival at the final position (x F ,y F , H) Stop. During the flight of the drone, the distance between the drone R and the ground node S is hSR, and the distance between the drone R and the destination node D is hRD.

[0047] In this embodiment, the channel between the drone and the source node S and the destination node D can be composed of a line-of-sight link.

[0048] In this embodiment, when constructing the relay communication model, it is necessary to determine the model of the drone actually used, so that the drone parameter values ​​can be determined based on the model of the drone used in the relay communication model, so that the drone energy consumption can be calculated based on these drone parameter values.

[0049] In this embodiment, the UAV parameter values ​​include but are not limited to: blade profile power and induced power of the UAV in the hovering state, tip speed of the rotor blades, average rotor induced speed in the hovering state, fuselage drag ratio, rotor solidity, rotor disc area, etc.

[0050] In this embodiment, since the rotor-wing UAV has no special requirements for the take-off and landing environment, it can achieve vertical take-off and landing, and can also hover at a fixed point. In some scenarios, it may be more energy-efficient than fixed-wing UAVs. In addition, since the cost of fixed-wing UAVs is relatively high, in this embodiment, a rotor-wing UAV can be used as a relay node in relay communication.

[0051] Step S102, determining the initial velocity vector of the UAV, wherein the initial velocity vector includes: an initial velocity value and an initial velocity direction.

[0052] In step S102, the initial velocity vector of the drone, that is, the initial velocity value and initial velocity direction of the drone when taking off, may be determined first, so that the initial velocity vector may be used as the initial value of the iterative preset energy efficiency optimal function.

[0053] Step S103, when multiple preset constraints are met, the preset energy efficiency optimal function is iterated based on the initial velocity vector, the drone parameter value and the space parameter value to obtain the current velocity vector after each iteration.

[0054] In this embodiment, in order to describe the trajectory optimization problem of the UAV, energy efficiency optimization can be used as the goal, and the UAV speed vector is iterated under the information causal constraints of the source node and the destination node, the system running time, the maximum and minimum speed limits of the UAV flight, and the energy limit carried by the UAV.

[0055] In step S103, under the constraints of multiple preset constraints (including: information causal constraints, system running time, maximum and minimum speed limits of UAV flight, and energy limits carried by UAV, etc.), the preset energy efficiency optimal function (i.e., the objective function with energy efficiency optimization as the goal) can be iteratively calculated according to the initial velocity vector, UAV parameter values, and space parameter values ​​to obtain the current velocity vector after each iteration.

[0056] Step S104, when the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than a preset velocity threshold, stop the iteration and determine the trajectory of the drone based on the current velocity vector.

[0057] Optionally, if the difference between the current vector value of the current velocity vector of the current iteration and the last vector value of the last velocity vector after the last iteration is less than a preset speed threshold (which can be set according to actual conditions), it indicates that the iteration result converges and the iteration can be stopped. Since the speed is a vector, including the speed value and the speed direction, the optimal UAV trajectory can be determined based on the final speed vector of each time slot.

[0058] In an embodiment of the present invention, the preset speed threshold can be a very small value, which is used to determine the difference between the current vector value of the current speed vector and the last vector value of the last speed vector after the last iteration. The smaller the difference, the more stable the speed. If the difference is less than the preset speed threshold, it means that the iteration has reached convergence.

[0059] In summary, a relay communication model can be constructed, and based on the relay communication model, the UAV parameter values, space parameter values, and the initial velocity vector of the UAV can be determined. Under the condition of satisfying multiple preset constraints, the preset energy efficiency optimal function is iterated based on the initial velocity vector, the UAV parameter values, and the space parameter values ​​to obtain the current velocity vector after each iteration. When the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than the preset speed threshold, the iteration is stopped, and based on the current velocity vector, the UAV trajectory is determined, thereby optimizing the UAV flight trajectory and maximizing energy efficiency, thereby solving the technical problem in the related technology that the UAV trajectory cannot be determined according to the on-site conditions, resulting in low energy efficiency of the UAV relay system.

[0060] In order to accurately construct a preset energy efficiency optimal function, in the UAV trajectory determination method based on relay communication provided in Example 1 of the present application, before constructing the relay communication model, the UAV flight time can be divided into multiple time slots, wherein the time slot length of each time slot is less than a preset time threshold; based on the number of time slots, time slot length, speed variable and UAV parameters, an energy consumption function is constructed; based on the number of time slots, time slot length, bandwidth parameters, signal-to-noise ratio parameters and spatial parameters, a throughput function is constructed; based on the energy consumption function and the throughput function, a preset energy efficiency optimal function is constructed.

[0061] In this embodiment, the energy consumption of the drone relay node R consists of two parts: (1) the energy consumption required for the drone to maintain flight and forward propulsion; (2) the static energy consumption of the communication equipment carried by the drone and the drone's own circuit. It should be noted that in actual situations, the energy consumption of communication and static circuits is much smaller than the energy consumption of drone flight propulsion, usually the difference between a few watts and a few hundred watts. Therefore, the energy consumption of communication and static circuits is ignored here.

[0062] Optionally, in order to achieve the optimal energy efficiency of the relay communication system, the throughput of the drone relay communication system and the drone energy consumption model (i.e., the preset energy efficiency optimal function) can be established first, specifically: the drone flight time T (i.e., the system operation time) can be divided into N time slots, and the time slot length of each time slot is represented as δ. When the time slot n=1, R is located at the initial position, and when the time slot n=N+1, R is located at the end position. In this embodiment, when the time slot length δ of each time slot is less than the preset time threshold (i.e., a very small value) (i.e., δ is small enough), the position of the drone within a time slot can be regarded as unchanged, and therefore, the system throughput at the beginning of each time slot can be regarded as the data rate of the entire time slot.

[0063] In this embodiment, since the energy consumption of the acceleration and deceleration of the drone is relatively small compared to the energy consumption of flight propulsion, the energy consumption of the drone taking off and landing as well as acceleration and deceleration is ignored, and the energy consumption of the drone can be expressed as: That is, according to the number of time slots N, the time slot length δ, the speed variable v(n), the drone parameters (P0, P i , U tip 、v c , d0, s, A) and air density ρ, construct the energy consumption function Furthermore, the number of time slots N, the time slot length δ, the bandwidth parameter B, the signal-to-noise ratio parameter (such as γ RD (n)) and spatial parameters (H, p(n)), construct the throughput function

[0064] Afterwards, a preset energy efficiency optimal function (i.e., a function with speed v(n) as a variable and energy efficiency (ratio of throughput to energy consumption) as a target) can be constructed according to the energy consumption function and the throughput function, wherein the preset energy efficiency optimal function can be expressed as:

[0065]

[0066] Where B represents the channel bandwidth, δ represents the length of each time slot, and γ RD (n) represents the signal-to-noise ratio per unit distance, H represents the flight altitude of the UAV, ||p(n)|| represents the distance between the UAV R and the destination node D in the nth time slot, P0 represents the blade profile power of the UAV in the hovering state, Pi represents the induced power, U tip is the tip speed of the rotor blade, v c represents the average rotor induced speed in the hovering state, v(n) represents the UAV velocity vector, d0 represents the fuselage drag ratio, s represents the rotor solidity, ρ represents the air density, and A represents the rotor disc area.

[0067] In order to accurately construct preset constraints, in the UAV trajectory determination method based on relay communication provided in Example 1 of the present application, before constructing the relay communication model, an information causal constraint condition can be constructed, wherein the information causal constraint condition is used to indicate that the amount of data sent from the relay node to the destination node is less than or equal to the amount of data sent from the source node to the relay node; a UAV flight time constraint condition is constructed, and a UAV flight speed constraint condition is constructed; a UAV carried energy constraint condition is constructed; and the information causal constraint condition, the UAV flight time constraint condition, the UAV flight speed constraint condition, and the UAV carried energy constraint condition are characterized as preset constraints.

[0068] In this embodiment, the channel between the drone R and the ground node S is mainly composed of a line-of-sight link, and because the time-varying channel of the drone relay communication system follows the free space path loss model, the Doppler effect caused by the movement of the drone can be completely eliminated. The signal y received by the drone relay R or the destination node D in the nth time slot R (n), y D (n) can be expressed as:

[0069]

[0070] Where β represents the channel power loss per unit distance, and its value depends on antenna gain, carrier frequency, etc.; P S represents the transmission power of the ground node S; d SR (n) represents the distance between the UAV relay R and the ground node S at the nth time slot; α represents the large-scale fading factor, which is generally between 2 and 6; P R represents the transmission power of the relay node R; d RD (n) represents the distance between the drone relay R and the destination node D at the nth time slot; x s (n) represents the signal sent by node S; x R (n) represents the signal sent by the relay node R; z R (n) and z D (n) represents the noise at R and D at the nth time slot node; N represents the number of time slots.

[0071] In this embodiment, since R cannot forward the received data immediately, it needs a time slot to process the data. For the first time slot, R has no data to send to D. Therefore, the amount of data R sent to D in the first time slot is RD (1) = 0; Similarly, for the last time slot, if data is still received from S, it cannot be forwarded to R. Therefore, the amount of data sent from S to R in the Nth time slot is R SR (N) = 0. In addition, since R cannot generate data and can only forward data that has been received from S, the information causality constraint must satisfy the inequality: i is the identifier of the time slot. That is, an information causal constraint condition is constructed, which is used to indicate that the amount of data sent from the relay node to the destination node is less than or equal to the amount of data sent from the source node to the relay node.

[0072] In this embodiment, the maximum flight time of the drone is limited, and the operating time of the relay communication system is equal to the flight time, so a drone flight time constraint is constructed (that is, the drone flight time cannot be greater than the maximum flight time T, which is divided into N time slots, and the duration of each time slot n is δ). Assume that v(n) represents the speed of the drone R in the nth time slot, and q(n) = [x(n), y(n)] represents the horizontal coordinate of the drone R in the nth time slot. When the initial speed of the drone is v(1) = v0, the final speed of the drone needs to satisfy v(N+1) = v F ; When the initial position of the drone is q(1) = [x0, y0], the final position of the drone needs to satisfy q(N+1) = [x F ,y F ]; and, the position of the drone in the next time slot needs to satisfy: q(n+1)=q(n)+v(n)δ.

[0073] In this embodiment, since the maximum flight speed v of the drone max Therefore, it is necessary to construct the UAV flight speed constraint, that is, 0≤||v(n)||≤v max .

[0074] In this embodiment, since the load capacity of the drone R is limited, after being equipped with the necessary communication devices, the remaining space is insufficient to carry sufficient energy. Therefore, the total energy E (electricity, diesel, etc.) carried by the drone is limited, and it is necessary to construct energy constraints carried by the drone.

[0075] Then, the information causal constraints, the UAV flight time constraints, the UAV flight speed constraints and the energy constraints carried by the UAV are characterized as preset constraints.

[0076] That is, the preset constraints can be expressed as:

[0077]

[0078] v(1)=v0, q(1)=[x0, y0] (3);

[0079] v(N+1)=v F , q(N+1)=[x F , yF ] (4);

[0080] q(n+1)=q(n)+v(n)δ (5);

[0081] 0≤||v(n)||≤v max (6);

[0082] Among them, R RD (i) represents the amount of data forwarded by the drone, R SR (i) represents the amount of data sent by the source node received by the drone, v0, v F They represent the initial and final velocities of the drone, [x0, y0], [x F ,y F ] represent the initial horizontal coordinate and the final horizontal coordinate of the UAV, q(n) represents the horizontal coordinate of the UAV in the nth time slot, δ represents the time of each time slot, v(n) represents the flight speed of the UAV, v max Indicates the maximum flight speed of the drone.

[0083] In order to accurately obtain the convex optimization function and convex constraints, in the UAV trajectory determination method based on relay communication provided in Example 1 of the present application, a fractional ladder programming algorithm is used to convert the preset energy efficiency optimal function into an initial objective function; based on the slack variable, a continuous convex approximation algorithm is used to convert the initial objective function into a convex optimization function, and the non-convex constraints are converted into convex constraints, wherein the non-convex constraints refer to preset constraints with non-convex properties; under multiple convex constraints, the convex optimization function is iterated based on the initial velocity vector, the UAV parameter value and the space parameter value.

[0084] In an embodiment of the present invention, since the preset energy efficiency optimal function is in fractional form and is non-convex, and the information causal constraint of the UAV is in logarithmic form and is also a non-convex inequality, the preset energy efficiency optimal function cannot be directly solved. Therefore, it is considered to iteratively solve the preset energy efficiency optimal function through the fractional ladder programming method. For the non-convex constraints that cannot be handled, by introducing slack variables, the initial objective function is converted into a convex optimization function by using a continuous convex approximation algorithm, and the non-convex constraints are converted into convex constraints. It can also be solved by the interior point method (such as the path tracking method, an algorithm for solving convex optimization problems), thereby obtaining the optimal flight trajectory of the UAV.

[0085] In this embodiment, the original non-convex optimization problem (i.e., the preset energy efficiency optimal function constrained by multiple preset constraints) can be split into two sub-problems. Among them, sub-problem 1 is to perform iterative solution of the problem in fractional form by fractional ladder programming; sub-problem 2 is to perform iterative solution by continuous convex approximation algorithm. Specifically: the preset energy efficiency optimal function is converted into the initial objective function by the fractional ladder programming algorithm, and the iteration parameter θ is calculated by the initial value (i.e., the initial velocity vector). Then, the slack variable R is introduced. r (n), decouple the original non-convex optimization problem to obtain sub-problem 2, and convert the initial objective function into a convex optimization function through a continuous convex approximation algorithm, and convert the non-convex preset constraints into convex constraints (i.e., constraints with convex properties (i.e., constraints that facilitate the calculation of the optimal value of the problem)). Then, under multiple convex constraints, the convex optimization function is iterated according to the initial velocity vector, the UAV parameter value, and the space parameter value to solve the velocity. After that, the optimal solution (i.e., the speed of each iteration) obtained from sub-problem 2 is brought into sub-problem 1 to continue iterating until the result converges.

[0086] In order to accurately convert the preset energy efficiency optimal function into the initial objective function, in the drone trajectory determination method based on relay communication provided in Example 1 of the present application, iteration parameters are constructed based on the amount of data sent by the relay node to the destination node in each time slot and the energy consumption of the drone in each time slot; based on the iteration parameters, the preset energy efficiency optimal function is converted into the initial objective function.

[0087] Optionally, the amount of data sent by the drone relay node to the destination node in each time slot (i.e., R RD (n)) and the energy consumption of the drone in each time slot (i.e., P U (n)), construct an iteration parameter (i.e., θ), and then according to the iteration parameter, the preset energy efficiency optimal function can be converted into the initial objective function.

[0088] In the embodiment of the present invention, for the l+1th iteration, the iteration parameter θ l It can be calculated by the following formula:

[0089]

[0090] Where B represents the channel bandwidth, Indicates the amount of data forwarded by the drone in the nth time slot during the first iteration, represents the energy consumption of the UAV in the nth time slot at the first iteration, n represents the time slot into which the UAV flight time is divided, n∈{1,..,N}.

[0091] In {v lWhen {n}, n=1, ..., N} is known (i.e., after the first iteration, the current optimal speed of each time slot n is known), θ l is a constant value, through the iteration parameter θ l , the preset energy efficiency optimal function can be converted into the initial objective function. The initial objective function and constraints can be expressed as:

[0092]

[0093] v(1)=V0, q(1)=[x0, y0] (10);

[0094] v(N+1)=v F , q(N+1)=[x F ,y F ] (11);

[0095] q(n+1)=q(n)+v(n)δ (12);

[0096] 0≤||v(n)||≤v max (13);

[0097] Where n represents the time slots into which the drone flight time is divided, represents the amount of data forwarded by the UAV in the nth time slot of the l+1th iteration, represents the energy consumption of the UAV in the nth time slot of the l+1th iteration, R RD (i) represents the amount of data forwarded by the UAV in the i-th time slot, i∈{2,...,n}, R SR (i) represents the amount of data sent by the source node received by the drone in the i-th time slot, i∈{1,..,n-1}, v0, v F They represent the initial and final velocities of the drone, [x0, y0], [x F ,y F ] represent the initial horizontal coordinate and the final horizontal coordinate of the UAV, q(n) represents the horizontal coordinate of the UAV in the nth time slot, and v max Indicates the maximum flight speed of the drone.

[0098] In this embodiment, by continuously updating the parameter θ l , we can get a better {v * (n)}, but the initial objective function and constraints are still non-convex and cannot be directly solved by convex optimization methods. Therefore, the non-convex problem (i.e., the flight trajectory optimization problem consisting of non-convex optimization function and non-convex constraints) can be further transformed into an approximate convex problem for solution through the continuous convex approximation algorithm.

[0099] In order to improve the accuracy of determining convex optimization functions and convex constraints, in the UAV trajectory determination method based on relay communication provided in Example 1 of the present application, slack variable constraints are constructed based on slack variables and data sent from the relay node to the destination node, and the slack variable constraints are characterized as preset constraints; the upper bound of the energy consumption of the UAV is determined; based on the upper bound of the energy consumption, the initial objective function and the non-convex constraints are Taylor expanded at the preset distance variable to obtain a convex optimization function and convex constraints, wherein the preset distance variable refers to the distance variable between the UAV and the destination node.

[0100] Optionally, the slack variable R r (n) and the data R sent from the relay node to the destination node RD (n), construct slack variable constraints and characterize the slack variable constraints as preset constraints (i.e., slack variables R r (n) is less than or equal to the data R sent by the relay node to the destination node RD (n)), where the slack variable constraints are as follows:

[0101] R r (n)≤R RD (n), n=2,…,N (14);

[0102] in, γ RD (n) represents the signal-to-noise ratio per unit distance, H represents the flight altitude of the drone, and ||p(n)|| represents the distance between the drone R and the destination node D in the nth time slot.

[0103] In this embodiment, when the preset energy efficiency optimal function obtains the optimal solution, R r (n) = R RD (n), if the optimal solution is not obtained, R r (n) is less than R RD (n), therefore, it can be increased by increasing R r (n) Obtain a better target value. At this time, the initial objective function and constraints can be converted to:

[0104]

[0105] R r (n)≤R RD (n), n=2,…,N (17);

[0106] v(1)=v c , q(1) = [x0, y0] (18);

[0107] v(N+1)=v F , q(N+1)=[xF ,y F ] (19);

[0108] 0≤||v(n)||≤v max (20);

[0109] Then, the non-convex function and constraints can be transformed using the continuous convex approximation method. U (n), ||v(n)|| 2 and ||v(n)|| 3 It is a monotonically increasing function within the value range. To process, because Therefore, P U The upper bound of (n) (i.e., P U (n) ub ), which can be expressed as (that is, determining the upper bound of the energy consumption of the drone):

[0110]

[0111] R RD (n) j+1 (i.e., the data sent by the relay node to the destination node in the nth time slot during the j+1th iteration) j (n)|| 2 Doing Taylor expansion around it, we can get the following expression:

[0112]

[0113] Among them, ||p j (n)|| 2 represents the distance variable between the UAV and the destination node at the jth iteration (i.e., the preset distance variable refers to the distance variable between the UAV and the destination node), Represents the intermediate variable in Taylor expansion.

[0114] For the j+1th iteration (i.e., the data sent by the source node to the relay node in the nth time slot during the j+1th iteration), in ||q j (n)|| 2 Doing Taylor expansion around it, we can get the following expression:

[0115]

[0116]

[0117] in, represents the amount of data sent by the source node received by the drone, ||q j (n)|| 2represents the horizontal coordinate of the drone relay in the nth time slot, Represents the intermediate variable in Taylor expansion.

[0118] Afterwards, the non-convex constraints can be transformed into convex constraints as follows:

[0119]

[0120] The final convex optimization function and convex constraints can be expressed as:

[0121]

[0122] v(1)=v0, q(1)=[x0, y0] (29);

[0123] v(N+1)=v F , q(N+1)=[x F ,y F ] (30);

[0124] q(n+1)=q(n)+v(n)δ (31);

[0125] 0≤||v(n)||≤v max (32);

[0126] In order to accurately calculate the current velocity vector of the current iteration, in the UAV trajectory determination method based on relay communication provided in Example 1 of the present application, under multiple convex constraints, based on the initial velocity vector, UAV parameter values ​​and space parameter values, the interior point method is used to perform a cyclic calculation on the convex optimization function to obtain the current process velocity vector and the current process relaxation variable value; when the difference between the current process relaxation variable value and the last process relaxation variable value calculated last time is less than a preset relaxation threshold, the calculation is stopped to obtain the current velocity vector of the current iteration.

[0127] Optionally, under multiple convex constraints, the convex optimization function is cyclically calculated using the interior point method according to the initial velocity vector, UAV parameter values ​​and space parameter values ​​to obtain the current process velocity vector and the current process relaxation variable value. If the difference between the current process relaxation variable value and the last process relaxation variable value obtained by the last calculation is less than a preset relaxation threshold, it indicates that the current calculation has converged, the calculation is stopped, and the current velocity vector of the current iteration is obtained.

[0128] In an embodiment of the present invention, the preset relaxation threshold may be a very small value, which is used to determine the difference between the current process relaxation variable value and the last process relaxation variable value calculated last time. The smaller the difference, the more stable the relaxation variable value tends to be. At this time, the optimal solution of the relaxation variable can be obtained.

[0129] In an embodiment of the present invention, the steps of loop calculation and iteration may include the following:

[0130] Step 1: You can set the number of loop calculations j = 0, the number of iterations l = 0, and initialize So that it satisfies the constraints in the preset energy efficiency optimal function.

[0131] Step 2: Order pass Initialize θ l , convert the preset energy efficiency optimal function into the initial objective function, and execute step 3 to iteratively solve the initial objective function.

[0132] Step 3: Introducing slack variables Make it satisfy the constraints of the initial objective function and take P U The upper bound of (n) is obtained, the initial objective function is transformed into a convex optimization function, and the convex optimization function is iteratively solved by a continuous convex approximation algorithm.

[0133] Step 4: Pass right Perform Taylor expansion to obtain the lower bound, and solve the problem using the interior point method to obtain the optimal solution {v(n) * , R r (n) *}. Let j = j + 1, If the growth value of the slack variable is less than the preset slack threshold ε, output {v(n) *}, go to step 5; otherwise, go to step 4.

[0134] Step 5: Let j = 0, l = l + 1, If the growth value of the iteration parameter is less than the preset speed threshold, execute step 6; otherwise, execute step 2.

[0135] Step 6: Order Output {v*(n)} and the iteration ends.

[0136] In the embodiment of the present invention, the energy efficiency problem of multi-user communication of a full-duplex relay system carried by a drone is solved, and by optimizing the flight trajectory of the drone with full consideration of various constraints, energy efficiency can be maximized, and the flight trajectory can be flexibly adjusted according to the system operation time. The energy efficiency performance is significantly better than that of fixed orbit and half-duplex solutions.

[0137] Another optional specific implementation is described in detail below.

[0138] In an embodiment of the present invention, an energy efficiency optimization scheme based on a spiral-wing UAV relay communication system is proposed. By utilizing a continuous convex approximation algorithm, a non-convex optimization problem can be transformed into a convex optimization problem. The orbital trajectory design of a full-duplex spiral-wing UAV mobile relay maximizes the UAV relay flight energy efficiency while meeting the UAV flight speed and energy constraints and satisfying the communication requirements of ground nodes.

[0139] In this embodiment, since the energy that a drone can carry is limited, it is assumed that the operating time of the entire drone relay communication system is the flight time of the drone, T seconds. Due to severe congestion, data cannot be directly transmitted between S and all Ds, and all data information must be forwarded through the drone relay node R. The data buffer of the relay node R needs to be large enough to ensure that all data sent from S can be stored. Assuming that the drone adopts full-duplex working mode and DF (Decode-and-Forward) protocol, if the drone's flight altitude is too low, it will cause a collision with a building. At the same time, frequent adjustments to the flight altitude will also cause energy loss. Therefore, it is assumed that the drone flies at a fixed altitude of H meters, where H is the safe flight altitude of the drone. Based on this, a system is constructed as follows: Figure 2 The communication model of the drone relay system is shown.

[0140] The following takes the parameter values ​​in the UAV relay system communication model in Table 1 as an example to perform a simulation analysis of the UAV's flight trajectory.

[0141] Table 1

[0142]

[0143] Figure 3 is a schematic diagram of an optional algorithm iterative convergence of system simulation according to an embodiment of the present invention, such as Figure 3 As shown in the figure, with the number of iterations as the horizontal axis and the energy efficiency (bit / J) as the vertical axis, it shows the algorithm iteration convergence curve under different system time and different relay transmission power. For example, when the system time T is 1000s and the relay transmission power P is r =0.01W algorithm iteration convergence curve; system time T is 500s, transmission power P r =0.01W algorithm iteration convergence curve; system time T is 1000s, transmission power P r =0.1W algorithm iteration convergence curve. Assume that the transmission power of the source node S is P S =0.1W, the self-interference generated by full-duplex can be completely eliminated. Figure 3 Different system operation time T, different relay transmission power P r, the algorithm can converge quickly and obtain the optimal value after only three iterations, and has good convergence.

[0144] Figure 4 is a schematic diagram of a drone trajectory simulated by an optional system according to an embodiment of the present invention, such as Figure 4 As shown, with distance x(m) as the horizontal axis and distance y(m) as the vertical axis, it shows different system time T and different transmission power P r The trajectory curve of the UAV under the following conditions, for example, the system time T is 1000s, the transmission power P r = 0.01W UAV trajectory curve; system time T is 1000s, transmission power P r = 0.1W UAV trajectory curve; system time T is 500s, transmission power P r = 0.01W UAV trajectory curve; system time T is 100s, transmission power P r = 0.01W UAV trajectory curve. The circle icon represents the source node S, the star icon represents the destination node D, the triangle icon represents the starting position q0 of the UAV, and the five-pointed star icon represents the ending position qF of the UAV.

[0145] Depend on Figure 4 It can be seen that for different system operation time T and different relay transmission power P r , the drone will adjust the flight trajectory according to the information causal constraint to obtain more data. When the drone relay system runs for T = 100s, due to the short system running time, the rotor drone mainly flies to the destination node to ensure that it reaches the final position within the system running time. S Much larger than the rotor drone relay node P r Therefore, in order to maximize the throughput during flight, the drone will fly towards the destination node. When the system running time T is large enough, the relay transmission power P r The flight trajectories of the same rotorcraft drones are almost identical. r =P S In the case of , the UAV will fly to the destination node first, and after arriving at the destination node, it will hover above the destination node, that is, ||v||=0m / s, to ensure the maximum throughput. r When the error is large, the helicopter drone relay first flies toward the source node, hovers above the source node or flies at a low speed for a period of time after arriving at the source node, and then quickly flies toward the destination node. After arriving at the destination node, it hovers above to ensure maximum throughput.

[0146] Figure 5is a schematic diagram of speed comparison of an optional system simulation according to an embodiment of the present invention, such as Figure 5 As shown in the figure, the flight speed of the UAV at different transmission powers is shown with time (s) as the horizontal axis and speed (m / s) as the vertical axis. For example, P r = The flight speed of the drone when 0.01W; P r = The flight speed of the drone when the transmission power is 0.1W. That is, the flight speed of the drone is different under different transmission powers.

[0147] Depend on Figure 4 and Figure 5 It can be seen that the algorithm in the embodiment of the present invention can flexibly adjust the flight trajectory of the UAV according to the transmission power and the system operation time to improve the throughput and achieve the improvement of system energy efficiency.

[0148] Figure 6 is a schematic diagram of energy efficiency comparison of an optional system simulation according to an embodiment of the present invention, such as Figure 6 As shown in the figure, with time (s) as the horizontal axis and energy efficiency (bit / J) as the vertical axis, the energy efficiency of the straight trajectory and trajectory optimization under different system running times is compared. According to the simulation results, it can be seen that the energy efficiency obtained by trajectory optimization is much higher than that of the UAV relay system with a straight flight trajectory, and as the system running time increases, the energy efficiency advantage of trajectory optimization becomes more obvious compared with the straight trajectory. r When is small, as the system running time increases, the system energy efficiency also increases. During long-term flight, a higher system throughput can be obtained by optimizing the trajectory, thereby improving the energy efficiency of the entire UAV relay system.

[0149] The following is a detailed description in conjunction with another embodiment.

[0150] Embodiment 2

[0151] A drone trajectory determination device based on relay communication provided in this embodiment includes multiple implementation units, each implementation unit corresponds to each implementation step in the above-mentioned embodiment one.

[0152] Figure 7 is a schematic diagram of an optional UAV trajectory determination device based on relay communication according to an embodiment of the present invention, such as Figure 7 As shown, the drone trajectory determination device may include: a construction unit 70, a first determination unit 71, an iteration unit 72, and a second determination unit 73.

[0153] The construction unit 70 is used to construct a relay communication model, and determine the drone parameter value and the space parameter value based on the relay communication model;

[0154] A first determining unit 71, used to determine an initial velocity vector of the UAV;

[0155] An iteration unit 72, configured to iterate a preset energy efficiency optimal function based on an initial velocity vector, a drone parameter value, and a space parameter value under the condition that a plurality of preset constraints are satisfied, to obtain a current velocity vector after each iteration;

[0156] The second determination unit 73 is used to stop iteration and determine the drone trajectory based on the current speed vector when the difference between the current vector value of the current speed vector and the last vector value of the last speed vector after the last iteration is less than a preset speed threshold.

[0157] The above-mentioned UAV trajectory determination device can construct a relay communication model through the construction unit 70, and determine the UAV parameter value and the space parameter value based on the relay communication model, determine the initial velocity vector of the UAV through the first determination unit 71, and iterate the preset energy efficiency optimal function based on the initial velocity vector, the UAV parameter value and the space parameter value under the condition of satisfying multiple preset constraints through the iteration unit 72 to obtain the current velocity vector after each iteration, and stop the iteration through the second determination unit 73 when the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than the preset speed threshold, and determine the UAV trajectory based on the current velocity vector.

[0158] Optionally, the UAV trajectory determination device also includes: a first processing module, used to divide the UAV flight time into multiple time slots before constructing the relay communication model, wherein the time slot length of each time slot is less than a preset time threshold; a first construction module, used to construct an energy consumption function based on the number of time slots, time slot length, speed variable and UAV parameters; a second construction module, used to construct a throughput function based on the number of time slots, time slot length, bandwidth parameters, channel ratio parameters and space parameters; and a preset energy efficiency optimal function is constructed based on the energy consumption function and the throughput function.

[0159] Optionally, the UAV trajectory determination device also includes: a fourth construction module, used to construct information causal constraints before constructing the relay communication model, wherein the information causal constraints are used to indicate that the amount of data sent from the relay node to the destination node is less than or equal to the amount of data sent from the source node to the relay node; a fifth construction module, used to construct UAV flight time constraints and UAV flight speed constraints; a sixth construction module, used to construct energy constraints carried by the UAV; and a first characterization module, used to characterize the information causal constraints, UAV flight time constraints, UAV flight speed constraints, and energy constraints carried by the UAV as preset constraints.

[0160] Optionally, the iteration unit includes: a first conversion module, used to convert a preset energy efficiency optimal function into an initial objective function using a fractional ladder programming algorithm; a second conversion module, used to convert the initial objective function into a convex optimization function based on slack variables and a continuous convex approximation algorithm, and convert non-convex constraints into convex constraints, wherein the non-convex constraints refer to preset constraints with non-convex properties; a first iteration module, used to iterate the convex optimization function based on an initial velocity vector, a drone parameter value, and a space parameter value under multiple convex constraints.

[0161] Optionally, the first conversion module includes: a first construction submodule, used to construct iteration parameters based on the amount of data sent by the relay node to the destination node in each time slot and the energy consumption of the drone in each time slot; a first conversion submodule, used to convert a preset energy efficiency optimal function into an initial objective function based on the iteration parameters.

[0162] Optionally, the second conversion module includes: a first characterization submodule, used to construct slack variable constraints based on slack variables and data sent from the relay node to the destination node, and characterize the slack variable constraints as preset constraints; a first determination submodule, used to determine the upper bound of the energy consumption of the UAV; a first calculation submodule, used to perform Taylor expansion on the initial objective function and non-convex constraints at a preset distance variable based on the upper bound of energy consumption to obtain a convex optimization function and convex constraints, wherein the preset distance variable refers to the distance variable between the UAV and the destination node.

[0163] Optionally, the first iteration module includes: a second calculation submodule, which is used to perform a cyclic calculation on the convex optimization function using the interior point method under multiple convex constraints based on the initial velocity vector, the drone parameter value and the space parameter value to obtain the current process velocity vector and the current process relaxation variable value; a first processing submodule, which is used to stop the calculation and obtain the current velocity vector of the current iteration when the difference between the current process relaxation variable value and the last process relaxation variable value calculated last time is less than a preset relaxation threshold.

[0164] The above-mentioned drone trajectory determination device may also include a processor and a memory. The above-mentioned construction unit 70, the first determination unit 71, the iteration unit 72, the second determination unit 73, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0165] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. The kernel can be set to one or more, and the kernel parameters can be adjusted to stop the iteration when the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than a preset velocity threshold, and determine the trajectory of the drone based on the current velocity vector.

[0166] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0167] According to another aspect of an embodiment of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, any one of the above-mentioned methods for determining the trajectory of a drone based on relay communication is implemented.

[0168] When the computer program product is executed on a data processing device, it is suitable for executing a program that is initialized with the following method steps: constructing a relay communication model, and based on the relay communication model, determining the drone parameter value, the space parameter value, and the initial velocity vector of the drone, iterating the preset energy efficiency optimal function based on the initial velocity vector, the drone parameter value, and the space parameter value while satisfying multiple preset constraints, and obtaining the current velocity vector after each iteration, stopping the iteration when the difference between the current vector value of the current velocity vector and the last vector value of the last velocity vector after the last iteration is less than a preset velocity threshold, and determining the drone trajectory based on the current velocity vector.

[0169] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the above-mentioned method for determining the trajectory of a UAV based on relay communication.

[0170] Figure 8 1 is a hardware structure block diagram of an electronic device (or mobile device) for a method for determining a drone trajectory based on relay communication according to an embodiment of the present invention. Figure 8 As shown, the electronic device may include one or more processors (e.g., Figure 8The processor 802a, processor 802b, ..., processor 802n, etc., which may include but are not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 804 for storing data. In addition, it may also include: a display, an input / output interface (I / 0 interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / 0 interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 8 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 8 More or fewer components as shown, or with Figure 8 Different configurations shown.

[0171] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0172] The embodiments or examples of the present disclosure are not exhaustive, but are only illustrative of some embodiments or examples, and are not intended to be specific limitations on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment or example can be implemented as an independent example, and the steps can be combined arbitrarily. For example, the scheme after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be arbitrarily exchanged. In addition, the optional methods or optional examples in a certain embodiment or example can be combined arbitrarily; in addition, the various embodiments or examples can be combined arbitrarily, for example, some or all steps of different embodiments or examples can be combined arbitrarily, and a certain embodiment or example can be combined arbitrarily with the optional methods or optional examples of other embodiments or examples.

[0173] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0174] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0175] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0176] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0177] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program codes.

[0178] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the trajectory of an unmanned aerial vehicle based on relay communication, characterized in that: include: A relay communication model is constructed, and based on the relay communication model, a drone parameter value and a space parameter value are determined, wherein the relay communication model is used to represent the communication between a source node, a relay node and a destination node; the relay node is implemented by a drone; the source node is used to send data to the relay node, and the relay node is used to send data to the destination node; Determine an initial velocity vector of the UAV, wherein the initial velocity vector includes: an initial velocity value and an initial velocity direction; Under the condition that multiple preset constraints are met, based on the initial velocity vector, the drone parameter value and the space parameter value, a preset energy efficiency optimal function is iterated to obtain a current velocity vector after each iteration; When the difference between the current vector value of the current speed vector and the last vector value of the last speed vector after the last iteration is less than a preset speed threshold, the iteration is stopped, and the trajectory of the drone is determined based on the current speed vector.

2. The method for determining the trajectory of an unmanned aerial vehicle according to claim 1, characterized in that: Before building the relay communication model, it also includes: Dividing the flight duration of the drone into a plurality of time slots, wherein the time slot length of each of the time slots is less than a preset time threshold; constructing an energy consumption function based on the number of time slots, the length of the time slots, the speed variable, and the drone parameters; constructing a throughput function based on the number of time slots, the time slot length, a bandwidth parameter, a signal-to-noise ratio parameter, and a spatial parameter; Based on the energy consumption function and the throughput function, the preset energy efficiency optimal function is constructed.

3. The method for determining the trajectory of an unmanned aerial vehicle according to claim 1, characterized in that: Before building the relay communication model, it also includes: Constructing an information causal constraint condition, wherein the information causal constraint condition is used to indicate that the amount of data sent from the relay node to the destination node is less than or equal to the amount of data sent from the source node to the relay node; Construct the UAV flight time constraint and the UAV flight speed constraint; Construct the energy constraints that the drone can carry; The information causal constraint condition, the UAV flight time constraint condition, the UAV flight speed constraint condition and the energy constraint condition carried by the UAV are characterized as the preset constraint conditions.

4. The method for determining the trajectory of an unmanned aerial vehicle according to claim 1, characterized in that: The step of iterating a preset energy efficiency optimal function based on the initial velocity vector, the drone parameter value, and the space parameter value comprises: Using a fractional ladder programming algorithm, the preset energy efficiency optimal function is converted into an initial objective function; Based on the slack variables, a continuous convex approximation algorithm is used to convert the initial objective function into a convex optimization function, and a non-convex constraint condition is converted into a convex constraint condition, wherein the non-convex constraint condition refers to the preset constraint condition with a non-convex property; Under the multiple convex constraints, the convex optimization function is iterated based on the initial velocity vector, the drone parameter value and the space parameter value.

5. The method for determining the trajectory of an unmanned aerial vehicle according to claim 4, characterized in that: The step of converting the preset energy efficiency optimal function into an initial objective function by using a fractional ladder programming algorithm comprises: Constructing iteration parameters based on the amount of data sent by the relay node to the destination node in each time slot and the energy consumption of the drone in each time slot; Based on the iteration parameter, the preset energy efficiency optimal function is converted into the initial objective function.

6. The method for determining the trajectory of an unmanned aerial vehicle according to claim 4, characterized in that: The steps of converting the initial objective function into a convex optimization function based on slack variables by using a continuous convex approximation algorithm and converting the non-convex constraint condition into a convex constraint condition include: Based on the slack variables and the data sent from the relay node to the destination node, constructing slack variable constraints, and characterizing the slack variable constraints as the preset constraints; Determining an upper bound on energy consumption of the drone; Based on the energy consumption upper bound, the initial objective function and the non-convex constraint condition are Taylor expanded at a preset distance variable to obtain the convex optimization function and the convex constraint condition, wherein the preset distance variable refers to the distance variable between the UAV and the destination node.

7. The method for determining the trajectory of an unmanned aerial vehicle according to claim 4, characterized in that: The step of iterating the convex optimization function based on the initial velocity vector, the drone parameter value and the space parameter value under the multiple convex constraint conditions comprises: Under the multiple convex constraint conditions, based on the initial velocity vector, the drone parameter value and the space parameter value, the convex optimization function is cyclically calculated using the interior point method to obtain the current process velocity vector and the current process slack variable value; When the difference between the current process relaxation variable value and the last process relaxation variable value calculated last time is less than a preset relaxation threshold, the calculation is stopped to obtain the current velocity vector of the current iteration.

8. A drone trajectory determination device based on relay communication, characterized in that: include: A construction unit is used to construct a relay communication model, and determine the drone parameter value and the space parameter value based on the relay communication model, wherein the relay communication model is used to represent the communication between the source node, the relay node and the destination node; the relay node is implemented by a drone; the source node is used to send data to the relay node, and the relay node is used to send data to the destination node; A first determining unit, configured to determine an initial velocity vector of the UAV, wherein the initial velocity vector includes: an initial velocity value and an initial velocity direction; An iterative unit, configured to iterate a preset energy efficiency optimal function based on the initial velocity vector, the drone parameter value, and the space parameter value under the condition that a plurality of preset constraints are satisfied, to obtain a current velocity vector after each iteration; The second determination unit is used to stop iteration when the difference between the current vector value of the current speed vector and the last vector value of the last speed vector after the last iteration is less than a preset speed threshold, and determine the drone trajectory based on the current speed vector.

9. A computer program product, characterized in that It includes a non-volatile computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for determining the trajectory of a drone based on relay communication as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the drone trajectory determination method based on relay communication as described in any one of claims 1 to 7.

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