CN110673481A - Method and device for determining attribute data of unmanned aerial vehicle, electronic equipment and storage medium - Google Patents
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Abstract
The method establishes a non-convex problem model based on a flight speed range, a flight height range, the beam width of the unmanned aerial vehicle, a coverage condition and the data transmission rate of a sub-time period, converts the non-convex problem model into a plurality of convex problem models based on target time, and accordingly determines the flight track and the beam width of the unmanned aerial vehicle. The method for determining attribute data of the unmanned aerial vehicle can minimize the time for completing the transmission task of the unmanned aerial vehicle by adjusting the flight track and the antenna beam width of the unmanned aerial vehicle under the condition that the flight speed, the height and the antenna beam width of the unmanned aerial vehicle are limited.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle communication, in particular to a method and a device for determining attribute data of an unmanned aerial vehicle, electronic equipment and a storage medium.
Background
In recent years, unmanned aerial vehicles are widely applied to military and civil fields, and are expected to bring abundant commercial opportunities in the next decade. Among applications of drones, drone-assisted wireless communication has evolved into a very promising technology. Compared with traditional ground communication, unmanned aerial vehicle communication mainly has following three advantages. Firstly, unmanned aerial vehicle can deploy as required, and the flexibility is high, and the operation cost is low. Second, the channel between the drone and the ground typically experiences less scattering than a ground channel, and therefore has a higher probability of forming a line-of-sight link, thereby providing a more reliable communication link for user scheduling and resource allocation. Finally, the drone may dynamically adjust its position in three-dimensional space to improve communication quality, or avoid interference through proper trajectory design. The above advantages foster a large number of new applications, such as cellular data offloading in hot spots, service restoration after infrastructure failures, mobile data relay or customized communication in emergency situations, etc. However, many challenges still need to be addressed before the potential of drone communication can be fully exploited.
The research of the unmanned aerial vehicle auxiliary communication system can be roughly divided into two types according to the mobility of the unmanned aerial vehicle. In the first category, drones serve as stationary aerial communication platforms, providing ubiquitous wireless coverage for ground users. In this scenario, the problem of drone deployment has been extensively studied. These efforts achieve different design goals, such as maximizing outage probability, coverage area, number of users served, throughput, etc., by optimizing the height or horizontal position of the drone. And in the second type, the unmanned aerial vehicle serves as a mobile aerial platform for ground users. Several typical applications include drone assisted relay, information dissemination/data collection. Compared with the first type of research, the type of work obtains better channel quality by optimizing the trajectory of the unmanned aerial vehicle, thereby further improving the system performance.
However, in most of the work of unmanned aerial vehicle communication, the height of the unmanned aerial vehicle is fixed, the flight path of the unmanned aerial vehicle is limited to a horizontal plane, and the mobility of the unmanned aerial vehicle in the vertical direction is not fully utilized. In addition, most work in the research of trajectory optimization of drones assumes that drones are equipped with omni-directional antennas, i.e., signals of equal intensity can be radiated in all directions in three-dimensional space. However, in practice, equal radiation can be achieved only in two dimensions (i.e., horizontal direction), thereby achieving an omni-directional antenna. In modern communication systems, directional antennas with adjustable beamwidth have been widely used in various scenarios. Therefore, to the unmanned aerial vehicle that is equipped with directional antenna, study its orbit design problem and have realistic meaning.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining attribute data of an unmanned aerial vehicle, electronic equipment and a storage medium, and the method and the device can minimize the time for completing the transmission task of the unmanned aerial vehicle by adjusting the flight track and the antenna beam width of the unmanned aerial vehicle under the condition that the flight speed, the height and the antenna beam width of the unmanned aerial vehicle are limited.
In one aspect, an embodiment of the present application provides a method for determining attribute data of an unmanned aerial vehicle, including:
determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle; determining a coverage condition of the unmanned aerial vehicle based on a user corresponding to the unmanned aerial vehicle and a data transmission rate of the sub-time period; establishing a non-convex problem model based on the flight speed range, the flight height range, the beam width of the unmanned aerial vehicle, the coverage condition and the data transmission rate of the sub-time period; converting the non-convex problem model into a plurality of target time-based convex problem models; determining a flight trajectory and a beam width of the drone based on the plurality of convex problem models; the sub-time period is a time period within the preset time, and the target time is less than the preset time.
On the other hand, this application embodiment provides a device for confirming unmanned aerial vehicle attribute data, includes:
the first determining module is used for determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle; the second determination module is used for determining the coverage condition of the unmanned aerial vehicle based on the user corresponding to the unmanned aerial vehicle and the data transmission rate of the sub-time period; the third determination module is used for establishing a non-convex problem model based on the flight speed range, the flight height range, the beam width of the unmanned aerial vehicle, the coverage condition and the data transmission rate of the sub-time period; a fourth determination module to convert the non-convex problem model to a plurality of target time-based convex problem models; a fifth determining module, configured to determine a flight trajectory and a beam width of the drone based on the plurality of convex problem models; the sub-time period is a time period within the preset time, and the target time is less than the preset time.
In another aspect, an embodiment of the present application provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for determining attribute data of a drone.
In another aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by a processor to implement the method for determining attribute data of a drone.
The method, the device, the electronic equipment and the storage medium for determining the attribute data of the unmanned aerial vehicle have the following beneficial effects:
by determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle, the unmanned aerial vehicle is determined based on the coverage condition of a user corresponding to the unmanned aerial vehicle and the data transmission rate of the sub-time period, and a non-convex problem model is established based on the flight speed range, the flight height range, the beam width of the unmanned aerial vehicle, the coverage condition and the data transmission rate of the sub-time period, the non-convex problem model is converted into a plurality of convex problem models based on target time, and therefore the flight track and the beam width of the unmanned aerial vehicle are determined. The method for determining attribute data of the unmanned aerial vehicle can minimize the time for completing the transmission task of the unmanned aerial vehicle by adjusting the flight track and the antenna beam width of the unmanned aerial vehicle under the condition that the flight speed, the height and the antenna beam width of the unmanned aerial vehicle are limited.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for determining attribute data of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 3 is a simulation diagram of a flight trajectory of an unmanned aerial vehicle according to an embodiment of the present application;
fig. 4 is a simulation diagram of the height and beam width of an unmanned aerial vehicle according to an embodiment of the present application;
FIG. 5 is a graph of convergence of an algorithm provided by an embodiment of the present application;
FIG. 6 is a simulation diagram of task completion times for a different scenario provided by an embodiment of the present application;
fig. 7 is a schematic structural diagram of an apparatus for determining attribute data of an unmanned aerial vehicle according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application, including an unmanned aerial vehicle 101 and a user 102. For example, in a drone 101 assisted multicast system, the drone 101 serves as an airborne base station to provide file transfer services to a plurality of ground users 102.
Determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle 101; secondly, determining the coverage condition of the unmanned aerial vehicle 101 and the data transmission rate of a sub-time period, wherein the sub-time period is a time period within the preset time, and the target time is less than the preset time. Based on the flight speed range, the flight altitude range, the beam width of the unmanned aerial vehicle 101, the coverage condition and the data transmission rate of the sub-period of time, a non-convex problem model is established, the non-convex problem model is converted into a plurality of convex problem models based on the target time, and meanwhile, the user 102 is guaranteed to be always in the coverage range of the unmanned aerial vehicle 101, so that the optimal flight trajectory and the beam width of the unmanned aerial vehicle 101 can be determined.
The following describes a specific embodiment of the method for determining attribute data of an unmanned aerial vehicle according to the present application, and fig. 2 is a schematic flowchart of the method for determining attribute data of an unmanned aerial vehicle according to the embodiment of the present application, and the present specification provides the method operation steps according to the embodiment or the flowchart, but may include more or fewer operation steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 2, the method may include:
s201: determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle.
In the embodiment of the application, a specific application function scenario is described, the application scenario is a downlink multicast system, and in the application scenario, an unmanned aerial vehicle serves as a flying base station to transmit a D-bit file to a group of ground users. Here, a three-dimensional cartesian coordinate system is used, assuming the coordinates of each user as
Wherein wk∈R2 ×1Is a horizontal coordinate. For the sake of illustration, the flight time T is divided into N steps of δ by discretizationtAt equal intervals, i.e. T ═ N δt. Where delta istMust be small enough to ensure that the drone position is nearly constant during each sub-period. Thus, the unmanned aerial vehicle trajectory can be approximately expressed as
Wherein q [ n ]]∈R2×1The horizontal coordinate of the unmanned aerial vehicle; h [ n ]]Is the height of the unmanned aerial vehicle. Determining maximum horizontal flying speed V of unmanned aerial vehicleLAnd maximum vertical flying speed VDAnd a maximum flying height hmaxMinimum height hminThen, the limitation condition of the flight of the unmanned aerial vehicle can be determined according to the following formula (1) (2) (3):
in addition, in order for the drone to periodically serve the ground user, the drone needs to return to the initial position after the task is completed, that is, the horizontal coordinate of the drone needs to satisfy formula (4):
q[1]＝q[N],h[1]＝h[N].……(4)
in the embodiment of the application, the unmanned aerial vehicle is provided with a directional antenna with adjustable beam width, and theta n]And psi [ n ]]Representing the azimuth and elevation angles of the antenna, respectively. Assuming equal half-power beamwidths in azimuth and elevation, i.e., 2 Θ n]Wherein
(θ[n],ψ[n]) The corresponding antenna gain in direction may be determined according to equation (5):
wherein,
and g represents the antenna gain of the main and side lobes, respectively.
Due to the fact that
Therefore, let g be 0; the beam width is limited by the minimum and maximum beam widths, and
the beamwidth of the drone may be determined according to equation (6):
s203: determining a coverage condition of the unmanned aerial vehicle based on a user corresponding to the unmanned aerial vehicle and a data transmission rate of the sub-time period; the sub-time period is a time period within the preset time, and the target time is less than the preset time.
In an alternative embodiment, determining the coverage condition of the drone based on the user corresponding to the drone includes determining a user location of the user corresponding to the drone; determining the flight horizontal position of the unmanned aerial vehicle; the coverage condition is determined based on the user position, the flight level position, the flight height range, and the beam width.
In an alternative embodiment, determining the data transmission rate for a sub-period of time comprises determining a channel power gain at a first reference distance; determining channel power gains of the unmanned aerial vehicle and the user based on the channel power gains, the user position, the flight horizontal position and the flight height range; and determining the data transmission rate of the sub-period according to the channel bandwidth, the channel power gain and the power of the unmanned aerial vehicle transmission power and the Gaussian white noise.
Continuing with the above application scenario, it is assumed that each terrestrial user is equipped with an omni-directional antenna with unity gain. As shown in figure 1, the ground area covered by the main lobe of the unmanned aerial vehicle antenna is centered on the horizontal projection of the unmanned aerial vehicle, and the radius is rc[n]＝h[n]tanΘ[n]The circular area of (a). To ensure that all ground users are always within the coverage of the drone, the drone may determine the coverage condition based on the coverage of the corresponding user of the drone according to equation (7):
the air-to-ground communication channel is assumed to be a line-of-sight link, and the doppler effect due to the movement of the drone can be perfectly compensated. According to the free space path loss model, the channel power gain from the drone to the user may be determined according to equation (8):
wherein, beta0Indicates the reference distance d0Channel power gain at 1.
The data transmission rate of the user for the sub-period at time n may be determined according to equation (9):
wherein, B is the channel bandwidth;
p represents the drone transmission power; sigma2Representing the power of additive white gaussian noise at reception.
S205: and establishing a non-convex problem model based on the flight speed range, the flight height range, the beam width of the unmanned aerial vehicle, the coverage condition and the data transmission rate of the sub-time period.
S207: the non-convex problem model is converted into a plurality of target time-based convex problem models.
S209: determining a flight trajectory and a beam width of the drone based on the plurality of convex problem models.
An alternative embodiment of converting a non-convex problem model to a plurality of target time-based convex problem models includes determining a plurality of target times based on the non-convex problem model; the non-convex problem model is converted into a plurality of target time-based convex problem models.
In the embodiment of the application, the flight path of the unmanned aerial vehicle comprises the flight path of the unmanned aerial vehicle in the horizontal direction and the flight path of the unmanned aerial vehicle in the vertical direction.
In the embodiment of the application, the non-convex problem model is converted into a new non-convex problem model based on the target time and a monotonous problem based on the new non-convex problem. Multiple target times may be determined for the monotonic problem using bisection, and the target time-based non-convex problem model is converted to multiple target time-based convex problem models until the monotonic problem achieves an optimal solution. So, can determine the flight track and the beam width of best unmanned aerial vehicle, when unmanned aerial vehicle was nearer apart from the user promptly, unmanned aerial vehicle flies at lower height, and antenna beam width is big. The target time required to transfer the same size file is shorter than in the prior art.
In the embodiment of the present application, the non-convex problem model may be established according to formula (10):
to solve equation (10), the present embodiment introduces two more easily handled problems, and then proves that the optimal solution of equation (10) can be obtained by solving these two new problems. Continuing with the above application scenario, defining the ratio of left-hand throughput to file data of equation (10) as a ratio, then the first introduced problem is to maximize the minimum ratio between users, which can be expressed as equation (11):
let η for a given N*(N) is the optimum value of the formula (11). For any given N, if and only if η*(N) is equal to or greater than 1, the task is completed, and the target time can be determined according to the formula (12):
it is apparent that N is present only in the upper limit of the summation of equation (11b), and thus η*(N) monotonically increasing with respect to N. By using the bisection method, a plurality of target times are determined until the constraint equation (12b) takes an equal sign.
To solve for the flight trajectory and beam width of the drone, equation (11) is equivalently converted to equation (13):
since equation (13) is still a non-convex problem, here the non-concave term is replaced with the global concave lower bound using the SCA technique. Due to the fact that
Is about2[n]And h2[n]+||q[n]-wk||2Such that the function is obtained at { q }r[n],hr[n],Θr[n]The first order Taylor expansion at } is taken as the lower bound, equation (14):
wherein,
for a given point hr[n],Θr[n]H [ n ] can be determined according to equation (15)]tanΘ[n])2The global lower bound of (c) is:
wherein,
according to the SCA principle, equation (13b) is replaced with equation (16):
further, equation (16) is translated into the following SOC constraint:
thus, given point { q }r[n],hr[n],Θr[n]Equation (13) can be approximated as equation (18):
thus, converting the non-convex problem model to a convex problem model based on the target time, equation (18) is a convex problem, and the flight trajectory and beam width of the drone can be determined using a standard convex optimization toolkit, such as CVX.
Simulation experiments are performed based on the method and specific experimental data provided by the embodiment of the application.
To an unmanned aerial vehicle auxiliary multicast communication system, set up user's number and be K ═ 6, unmanned aerial vehicle maximum flight height hmax250m, minimum flying height hmin70m, maximum horizontal flying speed VL50m/s, maximum vertical flying speed VDMaximum beam width of 20m/s
Minimum beamwidth
The maximum transmitting power P of the unmanned aerial vehicle is 0.01W, the bandwidth B is 10MHZ, and the noise power sigma is2＝2×10-11W, channel gain beta at distance 10Sub-interval δ -50t0.2 s. Initializing a trajectory q0[n]To be centered on the user's geometric center wcIs used as the center of a circle,
is a circle of radius, wherein
An initialization height of h0[n]∈[hmin,hmax]Initializing the beamwidth
Referring to fig. 3, fig. 3(a) is a simulation diagram of a flight trajectory of an unmanned aerial vehicle at D42 Mbits according to an embodiment of the present application, and fig. 3(b) is a simulation diagram of a flight trajectory of an unmanned aerial vehicle at D315 Mbits according to an embodiment of the present application. It can be seen that as D increases, the flight trajectory of the flight expands, flying at a position close above the user for better communication quality. Compared with the prior art OMA scheme, the unmanned aerial vehicle in the SO scheme provided by the application dynamically adjusts the height to balance the horizontal track and the beam width, and the spatial degree of freedom is fully utilized.
Referring to fig. 4, fig. 4(a) is a simulation diagram of the height and the beam width of an unmanned aerial vehicle at D42 Mbits according to an embodiment of the present disclosure, and fig. 4(b) is a simulation diagram of the height and the beam width of an unmanned aerial vehicle at D315 Mbits according to an embodiment of the present disclosure. It can be seen that when unmanned aerial vehicle flies nearer apart from some users, unmanned aerial vehicle flies at lower height, and antenna beam width is great this moment, otherwise, its height of unmanned aerial vehicle dynamic adjustment and beam width are in order to satisfy the coverage requirement.
Referring to fig. 5, fig. 5 is a graph illustrating convergence of an algorithm according to an embodiment of the present disclosure. When T is 50s, the algorithm based on the method provided by the embodiment of the present application converges after about 9 times, and the speed is fast.
Referring to fig. 6, fig. 6 is a simulation diagram of task completion time of different schemes according to an embodiment of the present application. As shown in fig. 6, files with different sizes are transmitted, and the task completion time of the method provided by the embodiment of the present application is different from that of the existing scheme, SO that it can be seen that the time for the method SO provided by the embodiment of the present application to complete the task is fastest.
The embodiment of the present application further provides an apparatus for determining attribute data of an unmanned aerial vehicle, fig. 7 is a schematic structural diagram of the apparatus for determining attribute data of an unmanned aerial vehicle provided in the embodiment of the present application, as shown in fig. 7, the apparatus includes:
a first determining module 701, configured to determine a flight speed range, a flight height range, and a beam width of the drone;
a second determining module 702, configured to determine a coverage condition of the drone based on a user corresponding to the drone and a data transmission rate of the sub-period;
a third determining module 703, configured to establish a non-convex problem model based on the flight speed range, the flight altitude range, the beam width of the unmanned aerial vehicle, the coverage condition, and the data transmission rate of the sub-period;
a fourth determination module 704 for converting the non-convex problem model into a plurality of target time-based convex problem models;
a fifth determining module 705 for determining a flight trajectory and a beam width of the drone based on the plurality of convex problem models; the sub-time period is a time period within the preset time, and the target time is less than the preset time.
The device and method embodiments in the embodiments of the present application are based on the same application concept.
An embodiment of the present application further provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method for determining the attribute data of the drone.
Embodiments of the present application further provide a storage medium, where the storage medium may be disposed in a server to store at least one instruction, at least one program, a code set, or a set of instructions related to implementing a method for determining attribute data of a drone in the method embodiments, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for determining attribute data of a drone.
Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The method, the device, the electronic device or the storage medium for determining the attribute data of the unmanned aerial vehicle provided by the application can be seen that the unmanned aerial vehicle is determined based on the coverage condition of the user corresponding to the unmanned aerial vehicle and the data transmission rate of the sub-time period by determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle, and a non-convex problem model is established based on the flight speed range, the flight height range, the beam width of the unmanned aerial vehicle, the coverage condition and the data transmission rate of the sub-time period, and the non-convex problem model is converted into a convex problem model based on the target time, so that the flight track and the beam width of the unmanned aerial vehicle are determined. The method for attribute data of the unmanned aerial vehicle can minimize the time for completing the transmission task of the unmanned aerial vehicle by adjusting the flight track and the antenna beam width of the unmanned aerial vehicle under the condition that the flight speed, the height and the antenna beam width of the unmanned aerial vehicle are limited.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for determining attribute data of an unmanned aerial vehicle is characterized by comprising the following steps:
determining a flight speed range, a flight height range and a beam width of an unmanned aerial vehicle;
determining a coverage condition of a user corresponding to the unmanned aerial vehicle and a data transmission rate of a sub-time period;
establishing a non-convex problem model based on the flight speed range, the flight altitude range, the beam width of the unmanned aerial vehicle, the coverage condition and the data transmission rate of the sub-time period;
converting the non-convex problem model into a plurality of target time-based convex problem models;
determining a flight trajectory and a beam width of the drone based on the plurality of convex problem models;
the sub-time period is a time period within a preset time, and the target time is less than the preset time.
2. The method of claim 1, wherein the determining that the drone is based on coverage conditions of a user to which the drone corresponds comprises:
determining a user position of a user corresponding to the unmanned aerial vehicle;
determining a flight horizontal position of the unmanned aerial vehicle;
determining the coverage condition based on the user position, the flight level position, a flight height range, and the beam width.
3. The method of claim 2, wherein determining the data transmission rate for the sub-period comprises:
determining a channel power gain at a first reference distance;
determining channel power gains for the drone and the user based on the channel power gain, the user position, the flight horizontal position, and the flight altitude range;
and determining the data transmission rate of the sub-time period according to the channel bandwidth, the channel power gain, the unmanned aerial vehicle transmission power and the power of Gaussian white noise.
4. The method of claim 1, wherein converting the non-convex problem model into a plurality of target time-based convex problem models comprises:
determining a plurality of the target times based on the non-convex problem model;
converting the non-convex problem model into a plurality of convex problem models based on the target time.
5. An apparatus for determining attribute data of a drone, comprising:
the device comprises a first determining module, a second determining module and a control module, wherein the first determining module is used for determining the flight speed range, the flight height range and the beam width of the unmanned aerial vehicle;
a second determining module, configured to determine a coverage condition of a user corresponding to the drone and a data transmission rate of a sub-period of time;
a third determining module, configured to establish a non-convex problem model based on the flight speed range, the flight altitude range, the beam width of the drone, the coverage condition, and the data transmission rate of the sub-period;
a fourth determination module to convert the non-convex problem model to a plurality of target time-based convex problem models;
a fifth determining module for determining a flight trajectory and a beam width of the drone based on the plurality of convex problem models;
the sub-time period is a time period within a preset time, and the target time is less than the preset time.
6. The apparatus of claim 5,
the second determining module is further configured to determine a user position of a user corresponding to the unmanned aerial vehicle; determining a flight horizontal position of the unmanned aerial vehicle; determining the coverage condition based on the user position, the flight level position, a flight height range, and the beam width.
7. The apparatus of claim 5,
the second determining module is further configured to determine a channel power gain at a first reference distance; determining channel power gains for the drone and the user based on the channel power gain, the user position, the flight horizontal position, and the flight altitude range; and determining the data transmission rate of the sub-time period according to the channel bandwidth, the channel power gain, the unmanned aerial vehicle transmission power and the power of Gaussian white noise.
8. The apparatus of claim 5,
the fourth determination module is further configured to determine a plurality of the target times based on the non-convex problem model; converting the non-convex problem model into a plurality of convex problem models based on the target time.
9. An electronic device, characterized in that the electronic device comprises a processor and a memory, in which at least one instruction, at least one program, a set of codes or a set of instructions is stored, which is loaded and executed by the processor to implement the method of determining drone attribute data according to any one of claims 1-4.
10. A computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored, which is loaded and executed by a processor to implement the method of determining drone attribute data of any one of claims 1-4.
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