A method and device for optimizing a trajectory of a drone

Abstract

The application discloses a kind of unmanned vehicle trajectory optimization method and device, comprising: according to Bernoulli distribution, communication model is constructed, and according to information age, data freshness model is constructed;Real-time data is obtained, and the optimal long-time average information age is obtained according to the communication model and freshness model;According to the optimal long-time average information age and real-time power, the optimal flight trajectory information is calculated, and the optimal flight trajectory information includes flight parameter, optimal flight direction, optimal flight speed.The application is constructed by communication model according to Bernoulli distribution and data transmission law, the transmission law of real-time data is classified, and the freshness of data is ensured by defining the information age of each data to construct freshness model.Meanwhile, the optimal flight trajectory information is calculated in combination with real-time power, to avoid the calculation delay caused by the low power of unmanned vehicle, improve the real-time performance and security of data transmission.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication, and more particularly to a method and apparatus for optimizing UAV trajectories. Background Technology

[0002] Currently, with the rapid development of drone technology, their applications are becoming increasingly widespread in various fields, including data transmission and communication. However, drone data transmission is highly susceptible to interference and eavesdropping, which limits their performance in data transmission to some extent. The biggest difference between drone-assisted communication systems and traditional wired communication systems is the limited battery capacity of drones, which severely restricts their computing performance. Traditional cryptography cannot function properly in drone-assisted wireless communication, and the encoding and decoding efficiency of drones is lower than that of large servers. This metric is no longer sufficient to meet current demands for data freshness. Due to these issues, we address interference and eavesdropping problems from a physical layer security perspective, using information age as a metric for communication systems. This avoids energy-intensive encryption and decryption processes and allows for the acquisition of fresher data, which has become an urgent need in practical applications.

[0003] Existing UAV trajectory planning methods generally use successive convex approximation to optimize the flight trajectory, or use neural network calculations to obtain the optimal flight trajectory. Although these methods guarantee data to a certain extent, they are computationally intensive, time-consuming, and power-intensive. Real-time calculations cannot be achieved using the UAV's own power, resulting in poor timeliness. Summary of the Invention

[0004] This invention provides a method and apparatus for optimizing the trajectory of unmanned aerial vehicles (UAVs) to solve the technical problems of long optimization time and poor timeliness in existing UAV trajectory planning methods.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for optimizing the trajectory of a UAV, comprising:

[0006] A communication model is constructed based on the Bernoulli distribution, and a data freshness model is constructed based on the information age.

[0007] Acquire real-time data and obtain the optimal long-term average information age based on the communication model and freshness model;

[0008] The optimal flight trajectory information is calculated based on the optimal long-term average information age and real-time battery level. The optimal flight trajectory information includes flight parameters, optimal flight direction, and optimal flight speed.

[0009] This invention constructs a communication model based on Bernoulli distribution and data transmission patterns, classifies the transmission patterns of real-time data, and ensures data freshness by defining the information age of each data point and constructing a freshness model. By determining the information age of data in real time and obtaining the optimal long-term average information age, the invention minimizes the information age throughout the data transmission process, thereby guaranteeing data freshness. Simultaneously, it calculates the optimal flight trajectory information in conjunction with real-time battery power, avoiding computational delays caused by low UAV battery levels and improving the real-time performance and security of data transmission.

[0010] Furthermore, the construction of the communication model based on the Bernoulli distribution includes:

[0011] A communication model is constructed based on flight period, flight altitude, and Bernoulli distribution. The communication model includes:

[0012]

[0013]

[0014] Where (x(n), y(n), H) represents the position coordinates of the UAV in the nth time slot, n represents each moment in the flight time, and d max For the maximum flight distance, β0 represents the reference channel power gain at a distance d0 = 1m. h represents the link distance from the transmitter to the source in the nth time slot. sr (n) represents the channel power gain from S to R in n time slots.

[0015] Furthermore, the step of constructing a data freshness model based on information age includes:

[0016] The set of time slots for generating each data packet is determined based on the data packet's transmission unit time slot and throughput.

[0017] The information age calculation mode is determined based on the data packet arrival information, and the information age structure is constructed based on the information age calculation mode;

[0018] A data freshness model is constructed based on the information age structure and the generation time slot set.

[0019] Furthermore, the step of determining the information age calculation mode based on the data packet arrival information and constructing the information age structure based on the information age calculation mode includes:

[0020] Determine the arrival information of the data packet, and determine the information age calculation mode based on the arrival information. The information age calculation mode includes a first mode and a second mode.

[0021] If the information age calculation mode is the first mode, then the information age will be updated to the age of the final integrated data packet;

[0022] If the information age calculation mode is the second mode, then the information age will be updated according to the data packet transmission time slot.

[0023] Furthermore, determining the arrival information of the data packet and determining the information age calculation mode based on the arrival information includes:

[0024] Determine the arrival information of the data packet, wherein the arrival information includes the data packet's information content;

[0025] The information type is determined based on the amount of information in the data packet. The information type includes a first type and a second type. The first type indicates that a new data packet has arrived, and the second type indicates that no new data packet has arrived.

[0026] If the information type is the first type, then the information age calculation mode is the first mode;

[0027] If the information type is the second type, then the information age calculation mode is the second mode.

[0028] Furthermore, obtaining the optimal long-term average information age based on the communication model and the freshness model includes:

[0029] The receiver and sender of the real-time data are determined according to the communication model, and the transmission mode of each time slot is determined according to the receiver and sender.

[0030] The secure achievable rate of data in each time slot is calculated based on the transmission mode of each time slot and the power consumption model of the UAV.

[0031] Furthermore, the step of obtaining the optimal long-term average information age based on the communication model and the freshness model also includes:

[0032] The long-term average information age of the real-time data is determined based on the aforementioned UAV energy consumption model and freshness model.

[0033] The long-term average security rate of the data packet is determined based on the secure reachability rate of each time slot and the long-term average information age.

[0034] Furthermore, the step of obtaining the optimal long-term average information age based on the communication model and the freshness model also includes:

[0035] The transmission mode for each time slot is determined, and the optimal long-term average information age is determined based on the secure reach rate of the data in each time slot, the long-term average security rate of the data packet, and the transmission mode.

[0036] Furthermore, the step of calculating the optimal flight trajectory information based on the optimal long-term average information age and real-time battery level includes:

[0037] The data transmission queue status and transmission channel status of each time slot are obtained, and the transmission mode of each time slot is determined according to the information age perception algorithm.

[0038] Determine the drone's real-time battery level and determine the calculation path based on the real-time battery level;

[0039] The optimal flight trajectory information is calculated based on the calculation path, the transmission mode of each time slot, the data transmission queue status, and the transmission channel status.

[0040] Secondly, the present invention provides a drone trajectory optimization device, comprising: a model building module, an optimization module, and a trajectory calculation module;

[0041] The model building module is used to build a communication model based on Bernoulli distribution and a data freshness model based on information age.

[0042] The optimization module is used to acquire real-time data and obtain the optimal long-term average information age based on the communication model and the freshness model.

[0043] The trajectory calculation module is used to calculate the optimal flight trajectory information based on the optimal long-term average information age and the information age perception algorithm. The optimal flight trajectory information includes flight parameters, optimal flight direction, and optimal flight speed. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a drone trajectory optimization method provided in an embodiment of the present invention.

[0045] Figure 2 This is another flowchart illustrating the UAV trajectory optimization method provided in an embodiment of the present invention;

[0046] Figure 3 This is an example of the implementation effect of the UAV trajectory optimization method provided in this invention.

[0047] Figure 4 This is a schematic diagram of a UAV trajectory optimization device provided in an embodiment of the present invention; Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1

[0050] Please refer to Figure 1 , Figure 1 A flowchart illustrating the UAV trajectory optimization method provided in this embodiment of the invention includes steps 101 to 103, as detailed below:

[0051] Step 101: Construct a communication model based on Bernoulli distribution and data transmission rules, and construct a data freshness model based on information age;

[0052] In this embodiment, time is divided using time slots, which are the smallest unit for transmitting circuit-switched summary information. A time slot is a very short period of time during which the channel conditions between transmission nodes remain unchanged.

[0053] In this embodiment, the communication model framework includes a transmitter, a receiver, a drone, and an eavesdropper. The arrival patterns of data at the transmitter follow a Bernoulli distribution. By establishing a communication model, the arrival patterns of data are integrated to facilitate subsequent data transmission.

[0054] In this embodiment, constructing a communication model based on Bernoulli distribution and data transmission rules includes:

[0055] A communication model is constructed based on flight period, flight altitude, and Bernoulli distribution. The communication model includes:

[0056]

[0057]

[0058] Where (x(n), y(n), H) represents the position coordinates of the UAV in the nth time slot, n represents each moment in the flight time, and d max For the maximum flight distance, β0 represents the reference channel power gain at a distance d0 = 1m. h represents the link distance from the transmitter to the source in the nth time slot. sr (n) represents the channel power gain from S to R in n time slots, where S represents the transmitter and R represents the UAV.

[0059] In this embodiment, the locations of all grounding nodes, including the transmitting end, the receiving end, and the eavesdropper, are fixed, as an example for illustration.

[0060] In this embodiment, the data transmission links include legitimate links and illegitimate links. The legitimate links are the sender-drone link and the drone-receiver link, and the illegitimate links are the sender-eavesdropper link and the drone-eavesdropper link.

[0061] In this embodiment, the sending end is responsible for collecting data packets in the data buffer. The buffer assistance mechanism enables scheduling. At the same time, the data storage space on the UAV allows the UAV to take operations based on the underlying channel status information and the buffer status information.

[0062] In this embodiment, the positions of the transmitter, receiver, and eavesdropper are represented as (0,0,0), (X), and (X), respectively. D ,Y D ,0) and (X E ,Y E The UAV flies at a fixed altitude H throughout its entire flight cycle T. The UAV relay service cycle T seconds can be subdivided into N equally spaced time slots δ, i.e., T = Nδ. (x(n), y(n), H) and (x0, y0, H) represent the UAV's position coordinates and starting position in the nth time slot, respectively, where 0 ≤ n. th ≤N. Maximum flight speed V of the drone. max The unit is meters per second. The maximum flight distance of the UAV in one time slot is d. max =V max δ is one of the constraints of drones.

[0063] In this embodiment, since the transmission link is a router (LoS), the channel power gain from S to R in n time slots can be calculated according to the free space path loss model, following h. sr (n).

[0064] In this embodiment, the communication model also includes the Loss channel gain of the UAV-receiver and UAV-eavesdropper links in the nth time slot, specifically:

[0065]

[0066]

[0067] In this embodiment, to measure the freshness of data packets generated at the sending end upon arrival at the destination, an information age is defined at the receiving end. The information age is closely related to the time when the data packets are generated at the sending end.

[0068] In this embodiment, constructing a data freshness model based on information age includes:

[0069] The set of time slots for generating each data packet is determined based on the data packet's transmission unit time slot and throughput.

[0070] The information age calculation mode is determined based on the data packet arrival information, and the information age structure is constructed based on the information age calculation mode;

[0071] A data freshness model is constructed based on the information age structure and the generation time slot set.

[0072] In this embodiment, there are N time slots in the total flight time T, and each transmission time is a unit time slot (e.g., δ = 1.5s). The generation of data packets at the sending end follows a Bernoulli process, and therefore can be measured by the arrival rate r∈[0,1]. λ(n) represents whether the sending end generates a data packet in time slot λ(n). If so, λ(n) = 1; otherwise, λ(n) = 0. The timestamp of the generation of the i-th data packet is denoted as t. i Let the arrival rate of the source data be denoted by r. If, under this arrival rate, K data packets will be generated within N time slots, then the transmission unit time slot of the data packet, i.e., the generation time slot, can be expressed as:

[0073]

[0074] In this embodiment, determining the information age calculation pattern based on the data packet arrival information and constructing the information age structure based on the information age calculation pattern includes:

[0075] Determine the arrival information of the data packet, and determine the information age calculation mode based on the arrival information. The information age calculation mode includes a first mode and a second mode.

[0076] If the information age calculation mode is the first mode, then the information age will be updated to the age of the final integrated data packet;

[0077] If the information age calculation mode is the second mode, then the information age will be updated according to the data packet transmission time slot.

[0078] In this embodiment, determining the arrival information of the data packet and determining the information age calculation mode based on the arrival information includes:

[0079] Determine the arrival information of the data packet, wherein the arrival information includes the data packet's information content;

[0080] The information type is determined based on the amount of information in the data packet. The information type includes a first type and a second type. The first type indicates that a new data packet has arrived, and the second type indicates that no new data packet has arrived.

[0081] If the information type is the first type, then the information age calculation mode is the first mode;

[0082] If the information type is the second type, then the information age calculation mode is the second mode.

[0083] In this embodiment, the arrival information of data packets received by the receiving end at any given time is measured according to a first rule and a second rule. The first rule includes accumulating the count of collected data packets when the last bit is received, and the second rule includes performing AoI evolution. The AOI evolution includes a first mode and a second mode. In the first mode, if the data packet is of the first type (i.e., a new data packet), the information age of the receiving end is updated to the age of the final integrated data packet received in the nth time slot. In the second mode, if the received data packet is of the second type (i.e., the data packet collection amount is not accumulated when the data packet is received), the information age of the receiving end will increase with time slot.

[0084] In this embodiment, A(n) represents the age of the last collected data packet received in the nth time slot, and the specific information age at the receiving end is:

[0085]

[0086] Where n∈{1,2,…,N-1}, A1 represents the first pattern, and A2 represents the second pattern.

[0087] In this embodiment, the data packets are all of length L, and L is the bandwidth-normalized data packet length, as an example.

[0088] In this embodiment, the data stream transmitted to the receiving end in the nth time slot can be represented as F(n). Furthermore, the total data arriving at the receiving end from the 1st to the nth time slot is represented as Q. D (n), where and n∈{1,2,..,N}. th The data packet at point D in time slot D is specifically as follows:

[0089]

[0090] Therefore, the information age is specifically:

[0091]

[0092] Step 102: Obtain real-time data and determine the optimal long-term average information age based on the communication model and freshness model;

[0093] In this embodiment, based on the binary variable q k(n), k∈1,2 represents the transmission mode of the nth time slot of the data packet, q1(n)=1 is the mode of the sender transmitting the data stream to the UAV, and q2(n)=1 is the mode of the UAV transmitting the data stream to the receiver.

[0094] In this embodiment, obtaining the optimal long-term average information age based on the communication model and the freshness model includes:

[0095] The receiver and sender of the real-time data are determined according to the communication model, and the transmission mode of each time slot is determined according to the receiver and sender.

[0096] The secure achievable rate of data in each time slot is calculated based on the transmission mode of each time slot and the power consumption model of the UAV.

[0097] In this embodiment, the power consumption model of the UAV is as follows:

[0098]

[0099] in, and These are two constants, representing the blade profile power and inductive power of the drone in hovering state, U tip The tip velocity of the rotor blades is represented by v0, which is called the average rotor induction velocity during hovering. d0 and s represent the drag ratio and rotor robustness of the UAV, respectively. ρ and A represent the air density and rotor disk area, respectively.

[0100] In this embodiment, let P s and P r Let S and R represent the transmit power of the transmitter S and the drone R, respectively. Then, the achievable rates of the transmitter-drone, transmitter-eavesdropper, drone-receiver, and drone-eavesdropper links in the nth time slot are:

[0101]

[0102]

[0103]

[0104]

[0105] In this embodiment, s represents the transmitter, r represents the drone, e represents the eavesdropper, d represents the receiver, and σ 2 It is the AWGN power of the receiver of the communication system based on the drone.

[0106] In this embodiment, the safe rates for the two hops at time slot n are as follows: (The original text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.)

[0107]

[0108]

[0109] Where, [x] + =max(x,0), This represents the safe rate for two hops at time slot n when the sender transmits the data stream to the drone. This represents the safe rate for two hops at time slot n when the drone transmits the data stream to the receiver.

[0110] In this embodiment, obtaining the optimal long-term average information age based on the communication model and the freshness model further includes:

[0111] The long-term average information age of the real-time data is determined based on the aforementioned UAV energy consumption model and freshness model.

[0112] The long-term average security rate of the data packet is determined based on the secure reachability rate of each time slot and the long-term average information age.

[0113] In this embodiment, the data transmission energy consumption at the nth time slot is specifically as follows:

[0114]

[0115] in, V represents the average power consumption. n It is the speed of the drone in the nth time slot.

[0116] In this embodiment, the long-term average information age is specifically:

[0117]

[0118] In this embodiment, the long-term average security rate from the transmitter to the drone and from the drone to the receiver is specifically as follows:

[0119]

[0120] In this embodiment, since the data packets are generated at the sending end and then transmitted to the receiving end with the help of the drone, the minimum average information age will be obtained when all data queues are in a non-absorption state. Therefore, the data arrival rate at the sending end is equal to the safe departure rate at the sending end, and the average safe arrival rate at the receiving end is equal to the average safe departure rate at the drone, that is:

[0121]

[0122] in, The average secure arrival rate at the receiving end. This represents the average safe departure rate at the location of the drone.

[0123] In this embodiment, the key to minimizing the information age problem is to minimize the stable queuing delay of the data queue, which can be achieved by minimizing the data queue.

[0124] In this embodiment, obtaining the optimal long-term average information age based on the communication model and the freshness model further includes:

[0125] The transmission mode for each time slot is determined, and the optimal long-term average information age is determined based on the secure reach rate of the data in each time slot, the long-term average security rate of the data packet, and the transmission mode.

[0126] In this embodiment, the optimal long-term average information age is obtained by minimizing the long-term average information age through the queue state transition problem, based on the Lyaponov optimization framework.

[0127] In this embodiment, Q s (n),Q r (n) represents the backlog in the data buffers at the transmitter and the drone in the nth time slot, respectively. The updates to the transmitter and drone queues at time slot n are as follows:

[0128]

[0129]

[0130] in, This represents the data arrival rate of each data packet in the nth time slot at the sending end, illustrated by an example where this rate is evenly distributed across a time slot.

[0131] In this embodiment, the Lyapunov function is defined as:

[0132]

[0133] Where E(n) represents the virtual energy queue in the nth time slot. Θ(n)=[Q s (n),Q R [(n), E(n)] represents all queue states, μ is a non-negative constant used to ensure the same order of magnitude in the Lyapunov function. a and b are both non-negative constants, usually set to 1.

[0134] In this embodiment, to characterize the expected increment of all queues between two consecutive time slots, the Lyapunov drift function is defined as follows:

[0135]

[0136] In this embodiment, to ensure the stability of the data queue, the transmission decision should minimize the output value of the Lyapunov drift function. Simultaneously, it should minimize the long-term average information age. Therefore, the Lyapunov drift penalty function is used to achieve both the Lyapunov drift function output value and the long-term average information age. Specifically, the Lyapunov drift penalty function is as follows:

[0137]

[0138] V is the non-negative control parameter, which can be manually set to achieve a trade-off between real-time AoI and long-term AoI.

[0139] In this embodiment, the Lyapunov drift penalty function is minimized according to the online trajectory optimization and transmission mode selection algorithm.

[0140] In this embodiment, the optimal long-term average information age is obtained by minimizing the Lyapunov drift penalty function by determining the transmission mode of each time slot.

[0141] In this embodiment, the first mode is (the data stream can be transmitted from S to the drone). In this mode, q1(n) = 1 and q2(n) = 0.

[0142] The optimal long-term average information age is specifically:

[0143]

[0144] stC1:

[0145]

[0146] C2:P s ,P r =p s ,p r

[0147] C3:HUAV=h

[0148] in, This indicates the maneuverability constraints of the drone. The secure reachability rate from the transmitter to the drone, C2 represents the constraints on the transmitter and drone's transmission power, and C3 is the drone's flight altitude.

[0149] The second mode is (the data stream can be transmitted from the drone to D). In this mode, q1(n) = 0 and q2(n) = 1. The optimal long-term average information age is specifically:

[0150]

[0151] stC1:

[0152]

[0153]

[0154] C2:P s ,P r =p s ,p r

[0155] C3:H UAV =h

[0156] in, This indicates the maneuverability constraints of the drone. The safe reach rate from the drone to the receiver, C2 represents the constraint on the drone's transmit power to the receiver, and C3 is the drone's flight altitude.

[0157] Step 103: Calculate the optimal flight trajectory information based on the optimal long-term average information age and real-time battery level. The optimal flight trajectory information includes flight parameters, optimal flight direction, and optimal flight speed.

[0158] In this embodiment, calculating the optimal flight trajectory information based on the optimal long-term average information age and real-time battery level includes:

[0159] The data transmission queue status and transmission channel status of each time slot are obtained, and the transmission mode of each time slot is determined according to the information age perception algorithm.

[0160] Determine the drone's real-time battery level and determine the calculation path based on the real-time battery level;

[0161] The optimal flight trajectory information is calculated based on the calculation path, the transmission mode of each time slot, the data transmission queue status, and the transmission channel status.

[0162] In this embodiment, the flight parameters include optimal transmission mode selection, where the optimal safe rate allocation R is obtained under the corresponding mode. * When (n), according to the first mode and the second mode, the optimal transmission mode selection scheme is as follows:

[0163]

[0164] Among them, Λ i (n) is the transmission mode selection index, given by the following formula.

[0165]

[0166]

[0167] In this embodiment, the optimal transmission mode is determined by confirming the transmission mode of each time slot and obtaining the optimal long-term average information age according to the Lyapunov optimization function, and the optimal flight trajectory information is calculated.

[0168] Please refer to Figure 2 , Figure 2 This is another flowchart illustrating the UAV trajectory optimization method provided in an embodiment of the present invention.

[0169] In this embodiment, during data transmission, the sending end continuously generates data packets and stores them in a buffer. Then, it synchronizes information such as the packet length, arrival rate, and data storage volume of the data packets to the drone. The drone then selects a suitable environment for performing a large number of calculations based on its own power. There are two different environments: one is to perform calculations on the drone itself, which consumes a lot of power, and the other is to perform calculations on an edge server, which saves a lot of energy and reduces latency. The result of the calculation is: the optimal mode selection, the optimal drone flight speed, and the optimal flight direction. Finally, if the system continues to operate, it will continuously cycle through this process; otherwise, the process will end.

[0170] In this embodiment, optimal flight trajectory information is generated based on information age perception transmission, including obtaining UAV parameters, including (x0, y0), (X... E ,Y E ),(X D ,Y D ),V max ,N,P s ,P r ,H,L,r, β0,R in The system calculates the drone's parameters (μ, δ, a, b) and its own battery level. If the drone's battery level is greater than a preset threshold, the calculation path is the computer itself; if the drone's battery level is less than or equal to the preset threshold, the calculation path is the server. Based on the calculation path and drone parameters, the transmission mode for each time slot is determined, the optimal long-term average information age is calculated, and the drone's flight parameters, optimal flight direction, and optimal flight speed for each time slot are updated in real time according to the communication model and freshness model, thereby optimizing the drone's trajectory.

[0171] Please refer to Figure 3 , Figure 3 This is an illustration of an implementation effect of the UAV trajectory optimization method provided in this embodiment of the invention.

[0172] In this embodiment, the optimized drone trajectory avoids eavesdroppers; for example, a diamond shape represents an eavesdropper. Data from the sending end is transmitted to the receiving end with the assistance of the drone. This is a secure drone communication scheme at the physical layer, achieving secure transmission by reducing the capacity of eavesdropping channels.

[0173] Please refer to Figure 4 , Figure 4 A schematic diagram of a UAV trajectory optimization device provided in an embodiment of the present invention includes: a model building module 401, an optimization module 402, and a trajectory calculation module 403;

[0174] The model building module 401 is used to build a communication model based on Bernoulli distribution and a data freshness model based on information age.

[0175] The optimization module 402 is used to acquire real-time data and obtain the optimal long-term average information age based on the communication model and the freshness model.

[0176] The trajectory calculation module 403 is used to calculate the optimal flight trajectory information based on the optimal long-term average information age and real-time battery level. The optimal flight trajectory information includes flight parameters, optimal flight direction, and optimal flight speed.

[0177] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for optimizing the trajectory of a drone, characterized in that, include: A communication model is constructed based on Bernoulli distribution and data transmission patterns, and a data freshness model is constructed based on information age. The construction of the communication model based on Bernoulli distribution and data transmission rules includes: constructing a communication model based on flight period, flight altitude, and Bernoulli distribution, wherein the communication model includes: in, Indicates that the drone is in Position coordinates within each time slot Representing various moments in the flight time, Maximum flight distance Indicates distance Reference channel power gain at that time Indicates the first The link distance from the sender to the source in each time slot. express The channel power gain from S to R in each time slot, where S represents the transmitter and R represents the UAV; Acquire real-time data and obtain the optimal long-term average information age based on the communication model and freshness model; The optimal flight trajectory information is calculated based on the optimal long-term average information age and real-time battery level. The optimal flight trajectory information includes flight parameters, optimal flight direction, and optimal flight speed.

2. The UAV trajectory optimization method as described in claim 1, characterized in that, The process of constructing a data freshness model based on information age includes: The set of time slots for generating each data packet is determined based on the data packet's transmission unit time slot and throughput. The information age calculation mode is determined based on the data packet arrival information, and the information age structure is constructed based on the information age calculation mode; A data freshness model is constructed based on the information age structure and the generation time slot set.

3. The UAV trajectory optimization method as described in claim 2, characterized in that, The step of determining the information age calculation mode based on the data packet arrival information and constructing the information age structure based on the information age calculation mode includes: Determine the arrival information of the data packet, and determine the information age calculation mode based on the arrival information. The information age calculation mode includes a first mode and a second mode. If the information age calculation mode is the first mode, then the information age will be updated to the age of the final integrated data packet; If the information age calculation mode is the second mode, then the information age will be updated according to the data packet transmission time slot.

4. The UAV trajectory optimization method as described in claim 3, characterized in that, The process of determining the arrival information of the data packet and determining the information age calculation mode based on the arrival information includes: Determine the arrival information of the data packet, wherein the arrival information includes the data packet's information content; The information type is determined based on the amount of information in the data packet. The information type includes a first type and a second type. The first type indicates that a new data packet has arrived, and the second type indicates that no new data packet has arrived. If the information type is the first type, then the information age calculation mode is the first mode; If the information type is the second type, then the information age calculation mode is the second mode.

5. The UAV trajectory optimization method as described in claim 1, characterized in that, The step of obtaining the optimal long-term average information age based on the communication model and the freshness model includes: The receiver and sender of the real-time data are determined according to the communication model, and the transmission mode of each time slot is determined according to the receiver and sender. The secure achievable rate of data in each time slot is calculated based on the transmission mode of each time slot and the power consumption model of the UAV.

6. The UAV trajectory optimization method as described in claim 5, characterized in that, The step of obtaining the optimal long-term average information age based on the communication model and the freshness model further includes: The long-term average information age of the real-time data is determined based on the UAV power consumption model and freshness model. The long-term average security rate of the data packet is determined based on the secure reachability rate of each time slot and the long-term average information age.

7. The UAV trajectory optimization method as described in claim 6, characterized in that, The step of obtaining the optimal long-term average information age based on the communication model and the freshness model further includes: The transmission mode for each time slot is determined, and the optimal long-term average information age is determined based on the secure reach rate of the data in each time slot, the long-term average security rate of the data packet, and the transmission mode.

8. The UAV trajectory optimization method as described in claim 7, characterized in that, The calculation of the optimal flight trajectory information based on the optimal long-term average information age and real-time battery level includes: The data transmission queue status and transmission channel status of each time slot are obtained, and the transmission mode of each time slot is determined according to the information age perception algorithm. Determine the drone's real-time battery level and determine the calculation path based on the real-time battery level; The optimal flight trajectory information is calculated based on the calculation path, the transmission mode of each time slot, the data transmission queue status, and the transmission channel status.

9. A drone trajectory optimization device, characterized in that, include: Model building module, optimization module, and trajectory calculation module; The model building module is used to build a communication model based on the Bernoulli distribution and a data freshness model based on information age. The construction of the communication model based on the Bernoulli distribution and data transmission rules includes: building a communication model based on flight cycle, flight altitude, and the Bernoulli distribution. The communication model includes: in, Indicates that the drone is in Position coordinates within each time slot Representing various moments in the flight time, Maximum flight distance Indicates distance Reference channel power gain at that time Indicates the first The link distance from the sender to the source in each time slot. express The channel power gain from S to R in each time slot, where S represents the transmitter and R represents the UAV; The optimization module is used to acquire real-time data and obtain the optimal long-term average information age based on the communication model and the freshness model. The trajectory calculation module is used to calculate the optimal flight trajectory information based on the optimal long-term average information age and real-time battery level. The optimal flight trajectory information includes flight parameters, optimal flight direction, and optimal flight speed.

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