Unmanned aerial vehicle track and multi-domain resource joint planning method based on situation awareness

Abstract

The invention discloses an unmanned aerial vehicle track and multi-domain resource joint planning method based on situation awareness, and belongs to the field of satellite communication. Aiming at unmanned aerial vehicle assisted satellite communication in an unreliable complex channel environment, a framework based on perception, transmission, calculation and navigation integration is constructed, and available resources are jointly optimized. And comprehensively designing the movement track of the unmanned aerial vehicle and the power and strategy of the general inductance calculation, and completing the data transmission task within the shortest time when the safety requirement is met. In order to reduce complexity, an original optimization problem is divided into two sub-processes of situation awareness and anti-interception transmission. Based on unmanned aerial vehicle trajectory and multi-domain resource joint planning, unmanned aerial vehicle adaptive trajectory planning is realized, an unknown eavesdropping environment is perceived, and an eavesdropping environment situation map is constructed step by step while data calculation and uploading are considered. Based on a trajectory and resource joint optimization algorithm of A * and a greedy algorithm, according to planned unmanned aerial vehicle trajectory calculation frequency, uploading power and a calculation strategy, a shortest path of a transmission task is obtained, and meanwhile, corresponding calculation and transmission power is matched.

Description

Technical Field

[0001] The present invention relates to a method for jointly planning unmanned aerial vehicle (UAV) trajectories and multi-domain resources based on situational awareness, and belongs to the field of satellite communications. Background Art

[0002] High-orbit satellite communications have become a common method for emergency communications due to their wide coverage, high reliability, and large communication capacity. However, obstacles can block communications, creating blind spots. Drones, with their high maneuverability and flexible communication networking, can serve as aerial relays, facilitating communications between satellites and users on the ground. At the same time, the high openness of satellite channels makes them vulnerable to potential eavesdropping, making secure transmission a key issue for high-orbit satellite communications.

[0003] Beamforming techniques, such as precoding, interference alignment, or artificial noise, have been developed to improve the physical layer security of high-orbit satellite communications. However, most studies assume that potential eavesdroppers have been identified and that the serving base station can somehow obtain some of their channel state information (CSI). In reality, since potential eavesdroppers typically do not interact with the base station, their presence is difficult to detect, and obtaining relevant CSI becomes extremely challenging. This issue has become a key constraint on secure wireless network transmission, making how to ensure communication security in the presence of unknown eavesdroppers a difficult problem that needs to be solved urgently.

[0004] When communications are disrupted by earthquakes or base station damage, terrestrial and satellite networks often struggle to cover complex terrain such as mountainous and forested areas. However, drones, with their maneuverability, flexibility, and rapid deployment, can integrate with terrestrial and satellite networks to penetrate communication blind spots, becoming a critical communication hub between the outside world and emergency response areas. First, during the data collection phase, drones equipped with cameras, infrared sensors, and other equipment quickly reach the target area, gathering real-time information on disaster conditions, personnel needs, and communications. Subsequently, the drones autonomously fly to a communication coverage area, utilizing onboard computing power for data processing, such as compression optimization, to improve transmission efficiency. Furthermore, leveraging integrated communication and sensing technology, drones can proactively sense their environment and other drones, assisting with security monitoring and mitigating the risk of eavesdropping. Finally, during the data transmission phase, drones transmit information via airlink to the receiving end, which then forwards it to the emergency communication system, rapidly establishing a dedicated emergency network and providing reliable communication support and rescue services to the affected areas. Summary of the Invention

[0005] Based on the scenario of drone-assisted satellite communication in an unreliable and complex channel environment, the purpose of the present invention is to provide a method for jointly planning drone trajectories and multi-domain resources based on situational awareness. The method utilizes the sensing equipment carried by drones and the integrated perception capabilities in existing wireless networks to obtain the environmental information required for the target area. The environmental information helps to solve the problem of anti-interception and safe transmission in unknown environments. Based on the joint planning of drone trajectories and multi-domain resources, the adaptive trajectory planning of drones is realized. The frequency, upload power and calculation strategy are calculated according to the planned drone trajectory, and the shortest path of the transmission task is obtained. At the same time, the corresponding calculation and transmission power are matched to minimize the task completion time.

[0006] The object of the present invention is achieved through the following technical solutions:

[0007] The present invention discloses a method for jointly planning UAV trajectories and multi-domain resources based on situational awareness, which constructs a comprehensive framework based on the integration of perception, transmission, computing and navigation for UAV-assisted satellite communications in unreliable and complex channel environments. The framework deploys a multifunctional UAV with integrated sensing and computing, which enhances physical layer security through target perception and improves transmission efficiency by using computing power. At the same time, the UAV's mobile trajectory, sensing and computing power and strategy are jointly designed to maximize data transmission efficiency while meeting security requirements. Based on the joint planning of UAV trajectories and multi-domain resources, UAV adaptive trajectory planning is realized, which perceives unknown eavesdropping environments and gradually builds an eavesdropping environment situation map while taking into account data calculation and upload. The trajectory and resource joint optimization algorithm based on A* and greedy algorithms calculates the frequency, upload power and computing strategy according to the planned UAV trajectory, and then obtains the shortest path of the transmission task, while matching the corresponding computing and transmission power to minimize the task completion time.

[0008] The present invention discloses a method for jointly planning UAV trajectories and multi-domain resources based on situational awareness, comprising the following steps:

[0009] Step 1: Establish a multi-domain integrated aerospace emergency communication system model, divide the satellite's communication area into grids, and obtain the distance the UAV moves between time slots;

[0010] A multi-domain integrated aerospace emergency communication system model is established. The model includes a high-orbit satellite and an auxiliary communication UAV. The ground area served by the high-orbit satellite has a communication blind spot due to the influence of terrain. There is an aerial eavesdropper Eve with an unknown position in the high-orbit satellite communication area, but the position information of the aerial eavesdropper can be obtained through radar perception. When the channel quality in the communication blind spot is severely damaged and direct satellite-to-ground communication is impossible, the UAV acts as a relay, collecting disaster information in the communication blind spot and then flying to the satellite communication coverage area to upload data. During the UAV uploading process, Eve eavesdrops on the information. During the entire flight upload process, the UAV sends a perception signal to determine Eve's position information, and at the same time performs on-board computing to compress the uploaded data. The UAV is equipped with a uniform linear array composed of NT antennas for communication and perception, and Eve uses a single antenna for eavesdropping. The positions of the satellite and Eve are fixed. Eve's position is unknown to the UAV. The UAV moves on a two-dimensional plane.

[0011] The high-orbit satellite communication area is divided into I×I grids, each of which contains two attributes: satellite channel status and eavesdropping channel status. The satellite channel status is determined by the geographical location and environment and has a certain degree of randomness. The eavesdropping channel status is determined by Eve's position, and the grid area close to Eve is more susceptible to eavesdropping. UAVs start from a fixed starting point and move in grid units. They advance one grid per time slot or maintain the same position. After flying one circle, they return to the starting point to form a closed-loop path. The total flight time slot of the UAV is expressed as Where T end is the time to return to the starting point; UAV performs perception calculation and transmission on each grid at a time interval of ΔT; in time slot t, the UAV is located at the lth t =[x t ,y t ] grid;Eve position l e =[x e ,y e ]; High-orbit satellite position l s =[x s ,y s ], the height difference from the UAV flight plane is H; Indicates the first t and l t+1 The distance between grids, and satisfy

[0012]

[0013] Among them x∈[1,I], y∈[1,I].

[0014] Step 2: Establish a transmission model based on statistical CSI to model the channel; obtain the safety interruption probability based on the transmission model;

[0015] The satellite service area will be affected by the environment, so the satellite communication channel is modeled as an uncertainty model. t The channel state of the grid is

[0016]

[0017] in is the path loss from the UAV to the satellite, f is the operating frequency, For drones in the first t The distance between the grid and the satellite; |g s | 2 is the channel fading factor, which characterizes the impact of complex environment blocking on the communication between the UAV and the satellite; the impact includes scattering effect, masking effect, and scattering and masking effect; |g s | 2 The probability density function PDF satisfies

[0018]

[0019] Among them, 1F1(m; 1; δx) is the confluent hypergeometric function, and the Nakagami-m fading parameter is Real number m∈(0,∞), μ is the average power of the line-of-sight component, 2κ is the average value of the multipath power; superimposing equation (3) yields the cumulative distribution function CDF, which is expressed as follows

[0020]

[0021] in is a raised power function, is the gamma function; represents the incomplete gamma function; integrating (2) yields |g i | 2 Cumulative distribution function of

[0022] The satellite channel status in the target area is G t ={g 1,1 ,…,g i,j ,…,g I,I}, g i,j Indicates l t = Satellite channel status at position [i, j]; l t The location eavesdropping channel state is expressed as

[0023]

[0024] in is the path loss of the eavesdropper, is the distance between the drone and Eve, θ t∈[-π / 2,π / 2] is the azimuth angle from the UAV to Eve, a(θ t ) is the drone-eavesdropper link steering vector;

[0025] At time slot t, the UAV transmits a signal:

[0026] x(t)=w(t)s com (t)+w e (t)s rad (t) (6)

[0027] where s com (t) and s rad (t) are the communication part and the perception part of the synaesthesia integrated signal, and is the precoding matrix;

[0028] The signal received by the satellite is expressed as

[0029]

[0030] in l t Position communication channel status, is Gaussian white noise; Eve receives the signal

[0031]

[0032] in l t Location eavesdropping channel status, is Gaussian white noise;

[0033] Define an auxiliary variable r(t) as the lower bound of the safe rate that the satellite can reach for the UAV in the tth time slot, and the achieved safe throughput C(t) is expressed as

[0034] C(t)=I C ([C s (t)-C e (t)] + ≥r(t))·r(t) (9)

[0035] Among them I C (·) is the indicator function, when [C s (t)-C e (t)] + ≥r(t)I C (·)=1, r(t) is the minimum expected communication rate of the link per time slot, C s (t) and C e (t) are the transmission rate from the UAV to the satellite and the eavesdropping rate, respectively, expressed as

[0036]

[0037] as well as

[0038]

[0039] Where B is the bandwidth;

[0040] The probability of safe interruption from drone to satellite meets

[0041] Pr{[C s (t)-C e (t)] + ≤r(t)}≤ε (12)

[0042] Where ε≤0.1 is the maximum allowable interruption probability.

[0043] Step 3: Use radar signals to perceive Eve’s location and estimate the state of the eavesdropping channel.

[0044] The position parameters of Eve are estimated based on the radar echo signal; the uncertainty model of the potential eavesdropper's position is:

[0045]

[0046] In the formula is the location estimate obtained when sensing a potential eavesdropper, Δl e (t) is the position measurement error; In real location e (t) satisfies the Gaussian distribution, that is,

[0047]

[0048] Where δ is the measurement standard deviation; the measurement error of the tth time slot satisfies

[0049]

[0050] Among them G MF is the MF gain;

[0051] The distribution measured over multiple time slots is accumulated as

[0052]

[0053] in satisfy is the measurement standard deviation of the cumulative distribution function; the threshold of measurement accuracy is set to Ω, when When , Eve’s position perception is sufficient;

[0054] The estimated eavesdropping channel state is in Indicates that Eve's estimated position is In this case, t = the eavesdropping channel state at position [i, j].

[0055] Step 4: Compress the data file and transmit the compressed file to the satellite.

[0056] In each mission, the UAV collects N data files to be transmitted in the communication blind area. Each data file is not related to each other. The UAV needs to upload all the data it carries to the satellite in this mission. Each data file is associated with an onboard computing task. The nth onboard computing task is used (d an ,d bn ,c n ) description, where c n To calculate the number of CPU revolutions required for this task, d an and d bn are the data sizes before and after calculation respectively; define indicator variable x n (t)∈{0,1} represents the calculation strategy of the nth task in the tth time slot, x n (t) = 1 means that the data of the nth task has been calculated and processed, otherwise x n (t) = 0; for simplicity, define d a =[d a1 ,…,d aN ] T ,d b =[d b1 ,…,d bN ] T and c=[c1,…,c N ] T ;

[0057] Using the computing function of the drone, the data files are compressed during the flight upload process to reduce the amount of data transmission; t time slot, drone position l t , carrying compressed data Uncompressed data volume Its satisfaction

[0058]

[0059] as well as

[0060]

[0061] in is the amount of data uploaded from the compressed queue, is the amount of data uploaded from the uncompressed queue, ΔT is the time slot length; To calculate the amount of data reduction in the uncompressed queue, The amount of data increase in the compressed queue after calculation is x(t)=[x1(t),…,x N (t)] T is the calculation strategy for the tth time slot.

[0062] Step 5: Establish a multi-domain resource joint optimization problem and decompose it. The original problem is divided into situational awareness problem and anti-interception transmission problem according to the perception end time.

[0063] Step 5.1: Optimizing the safe transmission time under limited power constraints. Analyze the main constraints in the proposed scenario and then establish a joint optimization problem for the combined power, perception strategy, computation strategy, and trajectory planning.

[0064] Analyze the power consumption constraints on the drone; the power consumption includes communication power consumption, computing power consumption and perception power consumption; the total power of the drone in each time slot is constrained, the total power P t satisfy

[0065]

[0066] Among them, P w (t)=|w(t)| 2 is the transmission power, P e (t)=|w e (t)| 2 is the perceived power, To calculate power, To calculate the power consumption factor, f c (t) is the calculation frequency, P max The upper limit of power allowed for each time slot;

[0067] In order to jointly optimize limited onboard resources to solve the problem of efficient and secure data transmission in satellite coverage blind spots under eavesdropping environments, the initial problem P1 is established, which enables data to be uploaded to the satellite in the shortest time under secure transmission conditions, and at the same time the drone returns to the starting point to perform the next mission; therefore, the objective function of the initial total problem and the corresponding constraints are expressed as follows

[0068]

[0069] st(9),(18),(20a)

[0070]

[0071] 1≤T s ≤T end (20d)

[0072]

[0073] in is the closed-loop flight trajectory, T end To satisfy and The moment; T s is the perception strategy, that is, the drone perception end time, satisfying To calculate the strategy, To calculate the frequency, is the transmission power variable, is the perception power variable; (20b) indicates that all onboard data should be transmitted when the UAV returns to the starting point, and (20c) indicates that the computing tasks in each time slot should be completed within that time slot; (20d), (20e) and (20f) impose basic constraints on the perception time, computing frequency and computing strategy range; due to the non-convexity of the optimization objective (20), x n (t) The coupling between the continuous variables and the randomness of the optimization target introduced by statistical CSI, the initial problem It is described as a mixed integer nonlinear programming problem MINLP with probabilistic optimization of random objectives;

[0074] Step 5.2: Based on the problem decomposition of the situational awareness and anti-interception transmission task phase, the original optimization problem is split into two sub-processes: situational awareness and anti-interception transmission, to reduce the complexity of the multi-domain resource optimization problem;

[0075] T s To partition, the initial problem The process is divided into two stages. The first stage is the situational awareness stage. In this stage, the eavesdropping environment is unknown. The drone senses Eve's position and gradually determines the state of the eavesdropping channel. It also performs data transmission and onboard calculations until the perception accuracy requirement is met. This stage ends. The second stage is the anti-interception transmission stage. In this stage, the eavesdropping environment state is known. Based on the environmental situation sensed by the drone in the first stage, the drone trajectory is planned, power is compressed, and power is uploaded to minimize the task completion time.

[0076] The initial problem P1 is transformed into a two-layer problem. The main problem is to design the first-stage movement trajectory, upload power, perception power, calculation frequency, perception strategy, and calculation strategy of the UAV to gradually determine Eve's location and minimize the task completion time under secure transmission conditions. The secondary problem is to design the second-stage movement trajectory, upload power, calculation frequency, and calculation strategy of the UAV under a certain eavesdropping environment to minimize the second-stage task completion time.

[0077] definition is the first stage path, Calculate frequency, transmission power and calculation strategy for the first stage; is the second stage path, Calculate frequency, transmission power and calculation strategy for the second stage;

[0078] Original optimization problem Equivalent to

[0079]

[0080] st(20a),(20d),(21a)

[0081] c T x(t)≤ΔTf c (t),t∈[1,T s ](21b)

[0082] 0≤f c (t)≤f max ,t∈[1,T s ] (21c)

[0083] x n (t)∈{0,1},t∈[1,T s ] (21d)

[0084]

[0085] in, is the shortest mission completion time of the second stage that can be achieved under the environmental situation perceived by the UAV in the first stage; U is determined by Existence of L I , P s ,T s and X I Composition, expressed as There is a group Satisfies (22a), (22b), (22c), (22d), Through the anti-interception transmission problem P3, we can get:

[0086]

[0087] st(20a),(20b), (22a)

[0088] c T x(t)≤ΔTf c (t),t∈[T s +1,T end ] (22b)

[0089] 0≤f c (t)≤f max ,t∈[T s +1,T end ] (22c)

[0090] x n (t)∈{0,1},t∈[T s +1,T end ] (22d)

[0091] Step 6: Solve the situation awareness problem P2 to obtain the eavesdropping environment situation;

[0092] Due to situational awareness issues The objective function in the problem has the characteristics of long-term accumulation, which makes the situation awareness problem Convert it into a Markov decision problem and use the DRL method to solve the specific problem;

[0093] Situational Awareness Issues Transformed into a Markov decision process, i.e., the tuple is the state space, is the action space, For reward space, is the transfer probability, γ is the reward discount factor; at time t, the drone first observes the current environment information to obtain the state Then take action Act on the environment, and then use the transition probability Transition to the next state s t+1 , and generates the reward r after the action is executed t ;

[0094]

[0095] G t are the eavesdropping channel state and satellite channel state of the target area sensed at time t respectively;

[0096] a t ={l t+1 ,P w (t+1),P s (t+1),f c (t+1),x(t+1),b t} (twenty four)

[0097] where b t ∈{0,1} indicates whether the perception phase is over, b t =1 means that the perception phase ends at the current moment; from (24), we know that l t+1,x(t+1),b t is a discrete variable, P w (t+1),P s (t+1),f c (t+1) is a continuous variable; therefore, the agent's output action is a mixed high-dimensional vector, which increases the complexity of the search space for finding the optimal action;

[0098] r t =R(t)+X1(t)+X2(t) (25)

[0099] The constant penalty term X1(t), R(t) is expressed as:

[0100] R(t)=ξ(P s (t),t)-ξ(P s (t-1),t-1) (26)

[0101] The penalty term X2(t) with hysteresis is expressed as

[0102]

[0103] Secondly, according to and r t , trained through DDPG, deployed the trained parameters to the drone to obtain the perceived environmental situation, and and other parameters;

[0104] Step 7: Perform equivalent iterations on the anti-interception transmission problem to obtain the optimal trajectory problem and the optimal calculation frequency, upload power, and calculation strategy problem;

[0105] The problem of anti-interception transmission Projection to variable L II , the optimization trajectory problem P4 is expressed as

[0106]

[0107] L II ∈V (28b) in is the amount of data completed in the first phase, For a given L II The maximum amount of data completed, Given by the subproblem

[0108]

[0109] st (19),(22b),(22c),(22d) (29a)

[0110] Pr{(Cs (t)-C e (t))≤r(t)}≤ε (29b)

[0111] Since the eavesdropping channel state in the anti-interception transmission phase is known, the position where the main channel transmission rate is less than the eavesdropping rate is set to be unreachable by the drone; therefore, the problem of optimizing the calculation frequency, upload power and calculation strategy is Satisfy C s (t)≥C e (t), the safety interruption probability is written in the form of (29b), and V is given by Existence of L II Composition, expressed as

[0112] Anti-interception transmission problem Transformed into an iteration of two problems; Optimizing trajectory problem It is a shortest path planning problem based on the A* algorithm, which solves the MINLP subproblem Then we introduce the shortest path planning algorithm based on the A* algorithm to solve the trajectory optimization problem

[0113] Step 8: Use the equivalent rate constraint to transform the problem into an optimization problem with a deterministic objective function. This problem is then transformed into solving sequential subproblems, which are solved efficiently using an iterative method to obtain the optimal computing frequency, upload power, and computing strategy.

[0114] Step 8.1: Use the equivalent rate constraint to solve the problem It is transformed into an optimization problem with a deterministic objective function.

[0115] By introducing the equivalent rate constraint lemma, we can obtain a closed expression for the minimum expected communication rate of the t-th time slot link.

[0116]

[0117] Step 8.2: Question Transformed into a series of sequential subproblems to solve P6;

[0118] Based on the equivalent rate constraint, the problem The optimal result of is achieved by maximizing the amount of completed data in each time slot. In the tth time slot, The corresponding sub-problem is expressed as

[0119]

[0120] st(29a),(29b)(31a) The closed-form expression of C(t) is

[0121]

[0122] Step 8.3: Perform communication and computation decoupling iterations to obtain the continuous knapsack problem P7 and subproblem P8.

[0123] P5 subproblem Rewritten as:

[0124]

[0125] stc T x(t)≤c max ,(33a)

[0126]

[0127] in It is further simplified to a continuous knapsack problem with additional constraints, the constraints are Therefore, by solving for a given c sum The optimal strategy and the optimal c sum Iterate between updates to solve and X * (c sum ) means that under the constraint c T x(t)=c sum The optimal c under sum and the corresponding optimal calculation strategy; X * (csum) is obtained by greedy algorithm; further, c sum Bring in Afterwards

[0128]

[0129] stc sum <c max (34a)

[0130] in

[0131] By optimizing P8, P7 and P6 iteratively, the optimal result of MINLP communication subproblem P5 is obtained.

[0132] Step 9: Use the hierarchical A* algorithm to perform K-shortest path planning to obtain the shortest path for the UAV, while matching the corresponding computing and transmission power to minimize the task completion time;

[0133] The drone has now reached tThe hovering point, taking into account both the path length and the channel state, uses the secondary cost function shown in Equations (35) and (36) to determine the next point to be expanded:

[0134]

[0135] f1(l t+1 ) is the first-level cost function considering the path length, f2(l t+1 ) is a secondary cost function that considers the channel state; in the planning process, first, according to f1(l t+1 ) to make a first-level judgment and select the point with the smallest path length cost to expand. If the path costs of multiple points are the same, a second-level judgment is made and the channel state cost f2(l t+1 ) higher point expansion; by continuously selecting f(l t+1 ) value is expanded until the target point appears in the set of adjacent points, that is, the shortest path planning is achieved; COST and FRONTIER are used to track f(l t+1 ) and expansion process; each point reached retains a pointer to its parent point in CAMEFROM to facilitate tracing back the path; using CAMEFROM, construct a shortest path from the start point to the end point;

[0136] The deviation path algorithm is used to solve the K shortest path problem. Define node i = 1, 2, ..., N, where (1) is the starting point and (N) is the end point; d ij ,i≠j, represents the direct distance from (i) to (j). If a path exists between points i and j, then d ij is a finite number, otherwise it is considered infinite; A k =(1)-(2 k )-(3 k )-…-(Q k k )-(N), k=1,2,…,K is the kth shortest path from (1) to (N), where (2 k ),(3 k ),…,(Q k k ) are the 2nd, 3rd, ..., Qth of the kth shortest path k nodes; It is from A k-1 The set of “deviations” at (i); Yes and A k-1 Overlapping subpaths; Yes and A k-1 The latter part of the path has only one node overlap;

[0137] After the first iteration, the shortest path A is determined1 ; After the kth iteration (k=2,3,…,K), determine the k shortest paths A k Finally, the shortest path is obtained, achieving the minimum completion time under the constraint of the lower limit of data completion amount;

[0138] The trajectory optimization problem P4 and the calculation frequency, upload power and calculation strategy optimization problem P5 are iteratively optimized to obtain the optimal solution to the anti-interception transmission problem P3, thereby minimizing the completion time of the second phase mission.

[0139] The frequency, upload power, and calculation strategy are calculated based on the planned drone trajectory to obtain the shortest path for the transmission task, while matching the corresponding calculation and transmission power to minimize the task completion time.

[0140] Beneficial effects:

[0141] Compared to traditional multi-domain resource allocation technologies for airborne emergency communication systems, the situational awareness-based UAV trajectory and multi-domain resource joint planning method disclosed in this paper fully leverages the role of UAVs in emergency communications, achieving rational allocation of system perception, computing, and communication resources under limited resource conditions, and providing agile, anti-interception communication support in emergency situations. Furthermore, the UAV's trajectory, inter-sensing computing power, and strategy are comprehensively designed to maximize data transmission rates while meeting safety requirements.

[0142] 2. Compared to the multi-domain resource allocation technology of traditional air-based emergency communication systems, the situational awareness-based joint planning method for drone trajectories and multi-domain resources disclosed in this invention utilizes deep reinforcement learning to construct a situational awareness-based joint planning method for drone trajectories and multi-domain resources. This method utilizes deep reinforcement learning to perform adaptive drone trajectory planning, balancing data calculation and upload in unknown eavesdropping environments while gradually constructing a situational map of the eavesdropping environment. This map improves communication security and stability.

[0143] 3. Compared with the multi-domain resource allocation technology of traditional air-based emergency communication systems, the situational awareness-based UAV trajectory and multi-domain resource joint planning method disclosed in the present invention implements a specialized reward and punishment mechanism for different dimensions in multi-dimensional actions in the UAV multi-domain resource planning based on reward decomposition DDPG. On the basis of overall performance improvement, it better reflects the independent contribution of each action dimension to system performance.

[0144] 4. Compared to traditional path planning and resource optimization techniques for airborne emergency communication systems, the situational awareness-based joint planning method for drone trajectories and multi-domain resources disclosed in this paper uses the A* and greedy algorithms to optimize the shortest path to complete the transmission task based on environmental awareness, while also matching the corresponding computational and transmission power. The A* algorithm, through its heuristic strategy, prunes ineffective paths during the optimization process, maintaining high efficiency even in high-density network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] Figure 1 This is a flow chart of the method for joint planning of UAV trajectories and multi-domain resources based on situational awareness of the present invention;

[0146] Figure 2 This is a model diagram of a UAV-assisted satellite communication system that integrates perception, transmission, calculation, and navigation in the UAV trajectory and multi-domain resource joint planning method based on situational awareness of the present invention;

[0147] Figure 3 This is a flow chart of the K shortest path algorithm in the joint planning method of UAV trajectory and multi-domain resources based on situational awareness of the present invention. DETAILED DESCRIPTION

[0148] The present invention will be described in detail below with reference to the accompanying drawings and embodiments, and the technical problems solved by the technical solution of the present invention and the beneficial effects thereof will be discussed. It should be noted that the described embodiments are intended to facilitate understanding of the present invention and do not have any limiting effect on the present invention.

[0149] Example 1

[0150] This example discusses the application of a situational awareness-based drone trajectory and multi-domain resource joint planning method for drone-assisted satellite communications in earthquake-stricken areas. When an earthquake strikes a region, roads are damaged and some base stations are damaged and out of service, disrupting communication channels within and outside the affected area. Vehicles cannot reach the epicenter in a timely manner, making it difficult for both ground and satellite networks to provide coverage to users at the epicenter. The Ministry of Emergency Management dispatches a drone to the affected epicenter. First, the drone approaches the disaster area to collect data. Based on emergency communication needs, the drone carries various sensor devices, such as cameras and infrared sensors, to collect real-time data and information, including images of the disaster, personnel requirements, and communication information. After data collection is complete, the drone autonomously flies with the data to a communication coverage area outside the disaster area. Once in the communication coverage area, the drone performs the data transmission phase, forwarding the collected data to the emergency communication processing system. The command center quickly establishes an emergency dedicated network, promptly obtains detailed information about the damage in the epicenter, and dispatches appropriate personnel and supplies for rescue operations, providing timely and reliable communication support and rescue services to the affected areas.

[0151] like Figure 1 As shown, the method for jointly planning UAV trajectories and multi-domain resources based on situational awareness disclosed in this embodiment is specifically implemented as follows:

[0152] Step 1: Establish a multi-domain integrated aerospace emergency communication system model, divide the satellite's communication area into grids, and obtain the distance the UAV moves between time slots;

[0153] Establish a multi-domain integrated aerospace emergency communication system model; the system model is shown in the figure below. Figure 2 As shown in the figure, the model includes a high-orbit satellite and an auxiliary communication UAV. The ground area served by the high-orbit satellite has a communication blind spot due to the influence of terrain. There is an aerial eavesdropper Eve with an unknown position in the high-orbit satellite communication area, but the position information of the aerial eavesdropper can be obtained through radar perception. When the channel quality in the communication blind spot is severely damaged and direct satellite-to-ground communication is impossible, the UAV acts as a relay, collects disaster information in the communication blind spot, and then flies to the satellite communication coverage area to upload data. During the UAV uploading process, Eve eavesdrops on the information. During the entire flight upload process, the UAV sends a perception signal to determine Eve's position information, and at the same time performs on-board calculations to compress the uploaded data. The UAV is equipped with a uniform linear array composed of NT antennas for communication and perception, and Eve uses a single antenna for eavesdropping. The positions of the satellite and Eve are fixed. Eve's position is unknown to the UAV. The UAV moves on a two-dimensional plane.

[0154] The high-orbit satellite communication area is divided into 10×10 grids, each containing two attributes: satellite channel status and eavesdropping channel status. The UAV starts at (0m, 0m, H m) and flies at a constant speed of 20m / s, hovering for 5 seconds at each point to perform data transmission and computation tasks. The satellite channel status is determined by the geographic location and environment and has a certain degree of randomness. The eavesdropping channel status is determined by Eve's position, and grid areas close to Eve are more susceptible to eavesdropping. UAVs start from a fixed starting point and move in grid units. They advance one grid per time slot or maintain the same position. After flying one circle, they return to the starting point to form a closed-loop path. The total flight time slot of the UAV is expressed as Where T end is the time to return to the starting point; UAV performs perception calculation and transmission on each grid at a time interval of ΔT; in time slot t, the UAV is located at the lth t =[x t ,y t ] grid;Eve position l e =[x e ,y e ]; High-orbit satellite position l s =[x s ,y s], the height difference from the UAV flight plane is H; Indicates the first t and l t+1 The distance between grids, and satisfy

[0155]

[0156] Where x∈[1,I], y∈[1,I];

[0157] Step 2: Establish a transmission model based on statistical CSI, model the channel, and obtain the safety interruption probability;

[0158] The satellite service area will be affected by the environment, so the satellite communication channel is modeled as an uncertainty model. t The channel state of the grid is

[0159]

[0160] in is the path loss from the UAV to the satellite, f is the operating frequency, For drones in the first t The distance between the grid and the satellite; |g s | 2 is the channel fading factor, which characterizes the impact of complex environment blocking on the communication between the UAV and the satellite; the impact includes scattering effect, masking effect, and scattering and masking effect; |g s | 2 The probability density function PDF satisfies

[0161]

[0162] Among them, 1F1(m; 1; δx) is the confluent hypergeometric function, and the Nakagami-m fading parameter is Real number m∈(0,∞), μ is the average power of the line-of-sight component, 2κ is the average value of the multipath power; superimposing equation (3) yields the cumulative distribution function CDF, which is expressed as follows

[0163]

[0164] in is a raised power function, is the gamma function; represents the incomplete gamma function; integrating (2) yields |g i | 2 Cumulative distribution function of

[0165] The satellite channel status in the target area is G t ={g1,1 ,…,g i,j ,…,g I,I}, g i,j Indicates l t = Satellite channel status at position [i, j];

[0166] l t The location eavesdropping channel state is expressed as

[0167]

[0168] in is the path loss of the eavesdropper, is the distance between the drone and Eve, θ t ∈[-π / 2,π / 2] is the azimuth angle from the UAV to Eve, a(θ t ) is the drone-eavesdropper link steering vector;

[0169] At time slot t, the UAV transmits a signal:

[0170] x(t)=w(t)s com (t)+w e (t)s rad (t) (6)

[0171] where s com (t) and s rad (t) are the communication part and the perception part of the synaesthesia integrated signal, and is the precoding matrix;

[0172] The signal received by the satellite is expressed as

[0173]

[0174] in l t Position communication channel status, is Gaussian white noise;

[0175] Eve receives the signal

[0176]

[0177] in l t Location eavesdropping channel status, is Gaussian white noise;

[0178] Define an auxiliary variable r(t) as the lower bound of the safe rate that the satellite can reach for the UAV in the tth time slot, and the achievable safe throughput C(t) is expressed as

[0179] C(t)=I C ([C s (t)-C e (t)] + ≥r(t))·r(t) (9)

[0180] Among them I C (·) is the indicator function, when [C s (t)-C e (t)] + ≥r(t)I C (·)=1, r(t) is the minimum expected communication rate of the link per time slot, C s (t) and C e (t) are the transmission rate from the UAV to the satellite and the eavesdropping rate, respectively, expressed as

[0181]

[0182] as well as

[0183]

[0184] Where B is the bandwidth;

[0185] The probability of safe interruption from drone to satellite meets

[0186] Pr{[C s (t)-C e (t)] + ≤r(t)}≤ε (12)

[0187] Where ε≤0.1 is the maximum permissible interruption probability;

[0188] Step 3: Use radar signals to perceive Eve’s location and estimate the state of the eavesdropping channel;

[0189] The radar signal is used to perceive Eve’s position. The radar echo signal received by the drone is expressed as

[0190]

[0191] in For the round-trip channel state, satisfy

[0192]

[0193] For Eve's radar cross section, For noise.

[0194] The position parameters of Eve are estimated based on the radar echo signal; the uncertainty model of the potential eavesdropper's position is:

[0195]

[0196] In the formula is the location estimate obtained when sensing a potential eavesdropper, Δl e (t) is the position measurement error; the error in the estimated position is mainly caused by noise, so Δl e (t) satisfies Gaussian distribution. Then Δl e (t) The calculation results of Eve position affected In real location e (t) satisfies the Gaussian distribution, that is,

[0197]

[0198] Where δ is the measurement standard deviation; the measurement error of the tth time slot satisfies

[0199]

[0200] Among them G MF is the MF gain;

[0201] The distribution measured over multiple time slots is accumulated as

[0202]

[0203] in satisfy is the measurement standard deviation of the cumulative distribution function; because the position measured each time is distributed near the true position, the cumulative probability distribution of the cumulative distribution of multiple measurements should have the highest value at the true position of the radiation source. Set the threshold of measurement accuracy to Ω, when When , Eve’s position perception is sufficient;

[0204] The estimated eavesdropping channel state is in Indicates that Eve's estimated position is In this case, t = the eavesdropping channel state at position [i, j];

[0205] Step 4: compress the data file and transmit the compressed file to the satellite;

[0206] In each mission, the UAV collects N data files to be transmitted in the communication blind area. Each data file is not related to each other. The UAV needs to upload all the data it carries to the satellite in this mission. Each data file is associated with an onboard computing task. The nth onboard computing task is used (d an ,dbn ,c n ) description, where c n To calculate the number of CPU revolutions required for this task, d an and d bn are the data sizes before and after calculation respectively; define indicator variable x n (t)∈{0,1} represents the calculation strategy of the nth task in the tth time slot, x n (t) = 1 means that the data of the nth task has been calculated and processed, otherwise x n (t) = 0; for simplicity, define d a =[d a1 ,…,d aN ] T ,d b =[d b1 ,…,d bN ] T and c=[c1,…,c N ] T ;

[0207] Using the computing function of the drone, the data files are compressed during the flight upload process to reduce the amount of data transmission; it is assumed that the drone will give priority to transmitting the compressed data; at time slot t, the drone position l t , carrying compressed data Uncompressed data volume Its satisfaction

[0208]

[0209] as well as

[0210]

[0211] in is the amount of data uploaded from the compressed queue, is the amount of data uploaded from the uncompressed queue, ΔT is the time slot length; To calculate the amount of data reduction in the uncompressed queue, The amount of data increase in the compressed queue after calculation is x(t)=[x1(t),…,x N (t)] T is the calculation strategy for the tth time slot;

[0212] Step 5: Establish a multi-domain resource joint optimization problem and decompose it. The original problem is divided into situational awareness problem and anti-interception transmission problem according to the perception end time.

[0213] In order to achieve the rational allocation and efficient utilization of the limited onboard resources of the UAV, a multi-domain resource joint optimization problem is established in this step. Specifically, the main constraints in the proposed scenario are first analyzed, and then a joint optimization problem of synaesthesia computing power, perception strategy, calculation strategy, and trajectory planning is established. Under the premise of ensuring safe transmission, the execution time of the UAV emergency mission is minimized and the time-sensitive anti-interception data transmission function is realized under unknown security threats. Furthermore, the original optimization problem is split into two sub-processes: situational awareness and anti-interception transmission to reduce the complexity of the multi-domain resource optimization problem.

[0214] 1) The optimization problem of safe transmission time under limited power constraints describes and analyzes the main constraints in the proposed scenario, and then establishes a joint optimization problem of synaesthesia power, perception strategy, calculation strategy and trajectory planning.

[0215] The only energy source for most drones is the initial energy stored in the battery. The drone needs to complete the entire mission within the limited battery capacity. First, analyze the power consumption constraints on the drone; power consumption includes communication power consumption, computing power consumption, and perception power consumption. The total power of the drone in each time slot is constrained, and the total power P t satisfy

[0216]

[0217] Among them, P w (t)=|w(t)| 2 is the transmission power, P e (t)=|w e (t)| 2 is the perceived power, To calculate power, To calculate the power consumption factor, f c (t) is the calculation frequency, P max The upper limit of power allowed for each time slot;

[0218] In order to jointly optimize limited onboard resources to solve the problem of efficient and secure data transmission in satellite coverage blind spots under eavesdropping environments, an optimization problem is established to upload data to the satellite in the shortest time under secure transmission conditions, while the drone returns to the starting point to perform the next mission; therefore, the objective function of the overall problem and the corresponding constraints are expressed as follows

[0219]

[0220] st(9),(20),(22a)

[0221]

[0222] 1≤Ts ≤T end (22d)

[0223]

[0224] in is the closed-loop flight trajectory, T end To satisfy and The moment; T s is the perception strategy, that is, the drone perception end time, satisfying To calculate the strategy, To calculate the frequency, is the transmission power variable, is the perception power variable; (22b) indicates that all onboard data should be transmitted when the UAV returns to the starting point, and (22c) indicates that the computing tasks in each time slot should be completed within the time slot; (22d), (22e) and (22f) impose basic constraints on the perception time, computing frequency and computing strategy range; due to the non-convexity of the optimization objective (22), x n (t) The coupling between the continuous variables and the randomness of the optimization objectives introduced by statistical CSI, the problem It is described as a mixed integer nonlinear programming problem MINLP with random target probability optimization; in addition, the unknown nature of the eavesdropping environment leads to the time-varying nature of the decision-making problem of the perception task strategy, which needs to be adjusted in real time according to the dynamic changes of the environment state. Therefore, The solution is extremely complex.

[0225] 2) Based on the problem decomposition of situational awareness and anti-interception transmission task stages, the original optimization problem is split into two sub-processes: situational awareness and anti-interception transmission, to reduce the complexity of the multi-domain resource optimization problem;

[0226] According to the UAV mission execution process, it can be observed that The environmental state of the problem presents a gradual evolution feature, that is, it gradually transitions from the initial unknown state of the eavesdropping channel to the known state. Therefore, in order to reduce the complexity of the problem, T s To divide, the problem The process is divided into two stages. The first stage is the situational awareness stage. In this stage, the eavesdropping environment is unknown. The drone senses Eve's position and gradually determines the state of the eavesdropping channel. It also performs data transmission and onboard calculations until the perception accuracy requirement is met. This stage ends. The second stage is the anti-interception transmission stage. In this stage, the eavesdropping environment state is known. Based on the environmental situation sensed by the drone in the first stage, the drone trajectory is planned, power is compressed, and power is uploaded to minimize the task completion time.

[0227] P1 is transformed into a two-layer problem. The main problem is to design the first-stage movement trajectory, upload power, perception power, calculation frequency, perception strategy, and calculation strategy of the UAV to gradually determine Eve's position and minimize the task completion time under secure transmission conditions. The secondary problem is to design the second-stage movement trajectory, upload power, calculation frequency, and calculation strategy of the UAV under a certain eavesdropping environment to minimize the second-stage task completion time.

[0228] definition is the first stage path, Calculate frequency, transmission power and calculation strategy for the first stage; is the second stage path, Calculate frequency, transmission power and calculation strategy for the second stage;

[0229] Original optimization problem Equivalent to

[0230]

[0231] st(22a),(22d),(23a)

[0232] c T x(t)≤ΔTf c (t),t∈[1,T s ] (23b)

[0233] 0≤f c (t)≤f max ,t∈[1,T s ] (23c)

[0234] x n (t)∈{0,1},t∈[1,T s ] (23d)

[0235]

[0236] in, is the shortest mission completion time of the second stage that can be achieved under the environmental situation perceived by the UAV in the first stage; U is determined by Existence of L I , P s ,T s and X I Composition, expressed as There is a group Satisfies (24a), (24b), (24c), (24d), Through P3 we get:

[0237]

[0238] st(22a),(22b), (24a)

[0239] c T x(t)≤ΔTf c (t),t∈[T s +1,T end ] (24b)

[0240] 0≤f c (t)≤f max ,t∈[T s +1,T end ] (24c)

[0241] x n (t)∈{0,1},t∈[T s +1,T end ] (24d)

[0242] Step 6: Solve the situational awareness problem to obtain the eavesdropping environment situation;

[0243] Transform the problem P1 into a double-layer problem with a main problem containing a sub-problem; The objective function in has the characteristics of long-term accumulation. The present invention transforms the optimization problem into a Markov decision problem and adopts the DRL method to solve the specific problem.

[0244] First, Transformed into a Markov decision process, i.e., the tuple is the state space, is the action space, For reward space, is the transfer probability, γ is the reward discount factor; at time t, the drone first observes the current environment information to obtain the state Then take action After acting on the environment, the transition probability Transfer to the following state s t+1 , and generates the reward r after the action is executed t ; Specifically, the state, action and reward functions of the Markov process can be expressed as follows;

[0245] 1. State Space At the tth time slot, the state of the system includes the distance between the UAV and the origin, the eavesdropping channel estimation state, the satellite channel state, the airborne data, and the perception standard deviation, which can be expressed as

[0246]

[0247] G t are the eavesdropping channel state and satellite channel state of the target area sensed at time t respectively;

[0248] 2. Action Space In the situation awareness phase, the UAV needs to adaptively plan the flight path, perceive the eavesdropper's position, and complete the data transmission and airborne compression tasks at the same time; therefore, in the tth time slot, the intelligent agent needs to t The decision is made on the drone's position in the next time slot, the power used for perception, computing, and transmission, the computing strategy, and the perception strategy, and then executed in the next time slot. Therefore, the action space needs to be mapped into high-dimensional parameters. The action of the agent in the tth time slot can be expressed as

[0249] a t ={l t+1 ,P w (t+1),P s (t+1),f c (t+1),x(t+1),b t} (26)

[0250] where b t ∈{0,1} indicates whether the perception phase is over, b t =1 means that the perception phase ends at the current moment; From (26), we can see that l t+1 ,x(t+1),b t is a discrete variable, P w (t+1),P s (t+1),f c (t+1) is a continuous variable; therefore, the agent's output action is a mixed high-dimensional vector, which increases the complexity of the search space for finding the optimal action;

[0251] 3. Reward Space Due to the complexity of mixed high-dimensional action spaces, rewards from actions in different dimensions can easily lead to compensation. This makes it difficult for agents to analyze the pros and cons of actions in different dimensions based on system rewards, making training agents more difficult. Therefore, this invention sets up specialized reward and punishment mechanisms for actions in different dimensions based on system rewards, namely the "basic reward + individual reward" model, so that agents can more effectively learn and understand the impact of actions in each dimension.

[0252] Specifically, we first set up a basic reward function to evaluate the overall system performance. The basic reward reflects the contribution of the agent to the overall goal of the system. In order to perceive the eavesdropper's position and complete the data transmission task, the drone needs to choose a location that is more conducive to shortening the overall task completion time. Therefore, the basic reward is defined as the estimated task completion time gain based on the current channel state. That is, assuming that the drone perception phase ends at the current moment, then according to the current channel state, the optimal time result of the anti-interception transmission phase is estimated. The difference between the estimated result at this moment and the estimated result at the previous moment is used as the reward at this moment, which is expressed as

[0253] R(t)=ξ(P s (t),t)-ξ(P s (t-1),t-1) (27)

[0254] where ξ(P s ,t) is the shortest task completion time of the anti-interception transmission phase that can be achieved under the environmental situation perceived by the UAV in time slot t;

[0255] For the UAV trajectory planning dimension l t+1 and perceived strategy dimension b t , the present invention sets "individual reward", in which when the trajectory planning action of the intelligent agent exceeds the boundary of the target area, a constant penalty term X1(t) is set to regulate its trajectory planning range; for the perception strategy dimension b t , set the penalty term X2 with hysteresis, expressed as

[0256]

[0257] Where C2>0 is a constant, that is, if the basic reward of time slot t+1 is positive, then the perception strategy b of time slot t t It plays a role in shortening the task completion time and is rewarded; if the basic reward of the t+1 time slot is negative, the perception strategy b of the t time slot t Errors prolong the task completion time and are punished; therefore, the final reward function can be expressed as

[0258] r t =R(t)+X1(t)+X2(t) (29)

[0259] Based on this, the present invention implements a specialized reward and punishment mechanism for different dimensions in multi-dimensional actions, which better reflects the independent contribution of each action dimension to system performance on the basis of overall performance improvement;

[0260] Train with DDPG and deploy the trained parameters to the drone to obtain the perceived environmental situation and the amount of data completed in the first phase. and other parameters;

[0261] Step 7: Perform equivalent iterations on the anti-interception transmission problem to obtain the optimal trajectory problem and the optimal calculation frequency, upload power, and calculation strategy problem;

[0262] Will Projection to variable L II , the main problem is expressed as

[0263]

[0264] L II ∈V (30b)

[0265] in For a given L II The maximum amount of data that can be completed under It can be given by the sub-problem

[0266]

[0267] st (19),(22b),(22c),(22d) (31a)

[0268] Pr{(C s (t)-C e (t))≤r(t)}≤ε (31b)

[0269] Since the eavesdropping channel status in the anti-interception transmission phase is known, the location where the main channel transmission rate is less than the eavesdropping rate is set to be unreachable by the drone; therefore Satisfy C s (t)≥C e (t), the safety interruption probability can be written in the form of (31b), V is given by Existence of L II Composition, expressed as Translated into an iteration of two questions; This is a shortest path planning problem that can be implemented based on the A* algorithm. The following solves the MINLP subproblem Then we introduce the shortest path planning algorithm based on A* algorithm to solve the problem

[0270] Step 8: Use the equivalent rate constraint to transform the problem into an optimization problem with a deterministic objective function. This problem is then transformed into solving sequential subproblems, which are solved efficiently using an iterative method to obtain the optimal computing frequency, upload power, and computing strategy.

[0271] 1) Using the equivalent rate constraint to solve the problem Transformed into an optimization problem with a deterministic objective function;

[0272] Since the random variable |g i | 2 existence, This is a probabilistic optimization problem with a random objective. To solve this problem, we first use the maximum allowable outage probability constraint in the random objective function to derive a closed-form expression for the function. By introducing the equivalent rate constraint lemma, we obtain a closed-form expression for the safe rate of the t-th time slot.

[0273]

[0274] 2) The problem Transformed into a series of sequential subproblems to solve P6;

[0275] It can be transformed into a series of sequential decision sub-problems on a greedy basis, that is, the problem The optimal result can be achieved by maximizing the amount of completed data in each time slot. In the tth time slot, the corresponding sub-problem is expressed as

[0276]

[0277] st(31a),(31b) (33a)

[0278] in The closed-form expression of C(t) is

[0279]

[0280] Based on this, the greedy algorithm can be used to solve the problem Get the local optimal strategy that meets the current optimization goal of each time slot, and then get the overall optimal strategy to solve the problem Optimize the goal;

[0281] 3) Perform communication and computation decoupling iterations to obtain P7 and P8;

[0282] Decouple the calculation from the communication, derive the relationship between the calculation frequency and transmission power and the calculation strategy, and obtain the optimal transmission power and calculation frequency expression under a given calculation strategy; Optimization problem can be rewritten as

[0283]

[0284] st c T x(t)≤c max , (35a)

[0285]

[0286] in It can be further simplified into a continuous knapsack problem with additional constraints, the constraints are Therefore, by solving for a given c sum The optimal strategy and the optimal c sum Iterate between updates to solve use and Indicates that under constraint c T x(t)=c sum The optimal c under sum and the corresponding optimal calculation strategy; X*(csum) can be obtained by the greedy algorithm; further, c sum Bring in After that you can get

[0287]

[0288] st c sum <c max (36a)

[0289] in By optimizing P8, P7 and P6 iteratively, the optimal result of MINLP communication subproblem P5 is finally obtained;

[0290] Step 9: Use the hierarchical A* algorithm to perform K-shortest path planning to obtain the shortest path for the UAV, while matching the corresponding computing and transmission power to minimize the task completion time;

[0291] Using the K shortest path optimization algorithm based on hierarchical A*, each time the K shortest path is selected, and whether the transmission task is completed under the path is determined, and the two are iterated to achieve the minimum completion time optimization under the lower limit constraint of the data completion amount; the K shortest path algorithm flow chart is as follows Figure 3 Assume that the drone has reached l t Point, taking into account both the path length and the channel state, the second-level cost function shown in the following formula is used to determine the next point to be expanded, which are expressed as

[0292]

[0293] f1(l t+1 ) is the first-level cost function considering the path length, f2(l t+1 ) is a secondary cost function that considers the channel state; in the planning process, first, according to f1(l t+1 ) to make a first-level judgment and select the point with the smallest path length cost to expand. If the path costs of multiple points are the same, a second-level judgment is made and the channel state cost f2(l t+1) higher point expansion; by continuously selecting f(l t+1 ) value is expanded until the target point appears in the set of adjacent points, that is, the shortest path planning is achieved; COST and FRONTIER are used to track f(l t+1 ) and expansion process; each point reached retains a pointer to its parent point in CAMEFROM to facilitate tracing the path; using CAMEFROM, a shortest path from the starting point to the end point can be constructed;

[0294] The algorithm idea of deviation path is used to solve the K shortest path problem. In an N-node network, define node i = 1, 2, ..., N, where (1) is the starting point and (N) is the end point. ij ,i≠j, represents the direct distance from (i) to (j). If a path exists between two points i and j, then dij is a finite number, otherwise it is considered infinite; A k =(1)-(2 k )-(3 k )-…-(Q k k )-(N), k=1,2,…,K is the kth shortest path from (1) to (N), where (2 k ),(3 k ),…,(Q k k ) are the 2nd, 3rd, ..., Qth of the kth shortest path k nodes; It is from A k-1 The set of “deviations” at (i); Yes and A k-1 Overlapping subpaths; Yes and A k-1 The latter part of the path has only one node overlap;

[0295] The implementation steps of the K shortest path algorithm based on the deviation path idea are as follows:

[0296] 1. First iteration: determine the shortest path A 1

[0297] First, use the A* shortest path algorithm to find the shortest path A1. Note that the K shortest path algorithm is not applicable when negative cycles exist in the network. If there are no negative cycles, at least one path with the shortest length should be found. If more than one path exists, assign A1 to any of them and store it in List A, the K shortest path list. The remaining paths are stored in List B, the candidate list.

[0298] 2. kth iteration (k = 2, 3, ..., K): determine the k shortest paths A k

[0299] To find the k-th shortest path A k , the shortest path A 1 ,A 2 ,…,A k-1 must be determined in advance. Then A k It can be obtained as follows:

[0300] I. For A k-1 Node i on the path = 1, 2, ..., Q k-1 , (a) Check the sequence A k-1 Is the subpath composed of the first i nodes consistent with A in the sequence j ,j=1,2,…,k-1, the subpaths composed of the first i nodes are exactly the same. If so, set diq to infinity, where (q) is A j (b) Apply the shortest path algorithm to find the shortest path from (i) to (N), allowing for nodes that are not already included in the path. If there are multiple subpaths from (i) to (N) of the same length, choose one of them and record it as (c) By connecting and turn up Then Add to List B.

[0301] II. Find the shortest path from List B. If the path found plus the paths already in List A exceeds K, then the task is complete. Otherwise, this path is recorded as A. k , and move it from list B to list A, keeping the other paths in list B. Then go to iteration k+1. In iteration k, set diq to infinity to force A k-1 Each node on the path deviates. Then steps I(b) and I(c) are followed, which find the node that is related to A. j ,j=1,2,…,k-1 different A k-1 Finally, in step II, A k Select from all possible candidates in list B. Therefore, the A obtained by the iterative process j ,j=1,2,…,K, is the K shortest and cycle-free paths from the starting point to the end point.

[0302] P4 and P5 were iteratively optimized to obtain the optimal solution of P3, thereby minimizing the completion time of the second-stage task.

[0303] The present invention is compared with three solutions:

[0304] Random Scheme (RS): In this scheme, the position of the drone in each time slot during the navigation process is randomly selected within the neighborhood, and the perception and calculation strategies are random.

[0305] Greedy Scheme (GS): In this scheme, the position of the drone in each time slot always chooses the one with the best channel state within the neighborhood. Perception stops only when the system perception accuracy requirement is met, and the calculation strategy is random.

[0306] DDPG-NDR: Based on the proposed algorithm, the agent learns only with basic rewards.

[0307] The performance of the proposed algorithm was evaluated under varying workloads, power budgets, and satellite channel fading levels. Table 1 shows the relationship between task completion time and workload for different schemes. As shown in the table, when there are fewer onboard tasks, the task completion times for each scheme are similar. This is because the transmission rate requirement is low when the data volume is small, and all schemes are able to complete the task in a relatively short time. As the amount of onboard task data increases, the proposed algorithm is able to achieve a trade-off between eavesdropper perception, data transmission, and computation based on the current environment. By flexibly allocating limited onboard resources to different tasks, it achieves coordination between these multiple functions, outperforming the baseline scheme. Furthermore, compared to the reward-free decomposition DDPG scheme, the proposed algorithm performs better under a certain task load. Because the agent performs targeted learning on trajectory planning, power allocation, perception strategy, and computation strategy during training, it has strong adaptability to complex environments. Compared with the RS and GS schemes, this scheme can improve task execution efficiency by up to 38%, with individual rewards contributing approximately 15.5% to the algorithm.

[0308] Table 1 Task completion time under different workloads

[0309]

[0310] Table 2 Task completion time under different power constraints

[0311]

[0312]

[0313] Table 2 evaluates the relationship between mission completion time and power constraints under different schemes. It can be seen that mission completion time increases under each scheme as the power budget decreases. However, compared with other schemes, the proposed algorithm is relatively less affected by a decrease in available power. Its core advantage lies in its design, which incorporates a dynamic resource allocation mechanism. Through intelligent trajectory planning, perception strategies, and power regulation, it can effectively adapt to reduced power budgets. This means that even when energy supply is limited, the algorithm can still flexibly adjust drone behavior, thereby minimizing the impact of power reduction on system performance. Compared with the RS and GS schemes, this scheme can improve mission execution efficiency by up to 35%.

[0314] Finally, the shortest path is obtained, achieving the minimum completion time under the constraint of the lower limit of data completion amount.

Claims

1. A joint planning method for UAV trajectories and multi-domain resources based on situational awareness, characterized by: The following steps are included: Step 1: Establish a multi-domain integrated aerospace emergency communication system model, divide the satellite's communication area into grids, and obtain the distance the UAV moves between time slots; Step 2: Establish a transmission model based on statistical CSI to model the channel; Obtain the security interruption probability based on the transmission model; Step 3: Use radar signals to perceive Eve’s location and estimate the state of the eavesdropping channel; Step 4: compress the data file and transmit the compressed file to the satellite; Step 5: Establish a multi-domain resource joint optimization problem and decompose it. The original problem P1 is divided into the situational awareness problem P2 and the anti-interception transmission problem P3 according to the perception end time. Step 6: Solve the situation awareness problem P2 to obtain the eavesdropping environment situation; Step 7: Perform equivalent iterations on the anti-interception transmission problem P3 to obtain the trajectory optimization problem P4 and the optimization calculation frequency, upload power and calculation strategy problem P5; Step 8: Use the equivalent rate constraint to transform problem P5 into an optimization problem with a deterministic objective function. P5 is then transformed into a sequential subproblem to be solved efficiently using an iterative method to obtain the optimal computing frequency, upload power, and computing strategy. Step 9: Use the hierarchical A* algorithm to perform K-shortest path planning to obtain the shortest path for the drone, while matching the corresponding computing and transmission power to minimize the task completion time.

2. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 1, characterized in that: In step one, A multi-domain integrated aerospace emergency communication system model was established. The model includes a high-orbit satellite and an auxiliary communication UAV. The ground area served by the high-orbit satellite has a communication blind spot due to the influence of terrain. There is an aerial eavesdropper Eve with an unknown position in the high-orbit satellite communication area, but the aerial eavesdropper's position information can be obtained through radar perception. When the channel quality in the communication blind spot is severely impaired and direct satellite-to-ground communication is impossible, the UAV acts as a relay, collecting disaster information in the communication blind spot and then flying to the satellite communication coverage area to upload the data. During the UAV uploading process, Eve eavesdrops on the information. During the entire flight upload process, the UAV sends perception signals to determine Eve's position information, and at the same time performs onboard calculations to compress the uploaded data. The UAV is equipped with a uniform linear array of NT antennas for communication and sensing, while Eve uses a single antenna for eavesdropping. The satellite and Eve are fixed in position; Eve's position is unknown to the UAV; and the UAV moves in a two-dimensional plane. The high-orbit satellite communication area is divided into I×I grids, each of which contains two attributes: satellite channel status and eavesdropping channel status. The satellite channel status is determined by the geographical location and environment and is random. The eavesdropping channel status is determined by Eve's position, and the grid area close to Eve is more susceptible to eavesdropping. UAVs start from a fixed starting point and move in grid units. They advance one grid per time slot or maintain the same position. After flying one circle, they return to the starting point to form a closed-loop path. The total flight time slot of the UAV is expressed as T = {1,…,t,…,T end }, where T end is the time to return to the starting point; UAV performs perception calculation and transmission on each grid at a time interval of ΔT; in time slot t, the UAV is located at the lth t =[x t ,y t ] grid;Eve position l e =[x e ,y e ]; High-orbit satellite position l s =[x s ,y s ], the height difference with the UAV flight plane is H; Indicates the first t and l t+1 The distance between grids, and satisfy Among them x∈[1,I], y∈[1,I].

3. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 2, characterized in that: In step 2, The satellite service area will be affected by the environment, so the satellite communication channel is modeled as an uncertainty model. t The channel state of the grid is in is the path loss from the UAV to the satellite, f is the operating frequency, For drones in the first t The distance between the grid and the satellite; |g s | 2 is the channel fading factor, which characterizes the impact of complex environment blocking on the communication between the UAV and the satellite; the impact includes scattering effect, masking effect, and scattering and masking effect; |g s | 2 The probability density function PDF satisfies Among them, 1F1(m; 1; δx) is the confluent hypergeometric function, and the Nakagami-m fading parameter is Real number m∈(0,∞), μ is the average power of the line-of-sight component, 2κ is the average value of the multipath power; superimposing equation (3) yields the cumulative distribution function CDF, which is expressed as follows in is a raised power function, is the gamma function; represents the incomplete gamma function; integrating (2) yields |g i | 2 Cumulative distribution function of The satellite channel status in the target area is G t ={g 1,1 ,…,g i,j ,…,g I,I }, g i,j Indicates l t = Satellite channel status at position [i, j]; l t The location eavesdropping channel state is expressed as in is the path loss of the eavesdropper, is the distance between the drone and Eve, θ t ∈[-π / 2,π / 2] is the azimuth angle from the UAV to Eve, a(θ t ) is the drone-eavesdropper link steering vector; At time slot t, the UAV transmits a signal: x(t)=w(t)s com (t)+w e (t)s rad (t) (6) where s com (t) and s rad (t) are the communication part and the perception part of the synaesthesia integrated signal, and is the precoding matrix; The signal received by the satellite is expressed as y(t)=g t w(t)s com (t)+g lt w e (t)s rad (t)+n (7) in for l t Position communication channel state, n~CN(0,σ 2 ) is Gaussian white noise; Eve receives the signal in l t Location eavesdropping channel status, is Gaussian white noise; Define an auxiliary variable r(t) as the lower bound of the safe rate that the satellite can reach for the UAV in the tth time slot, and the achieved safe throughput C(t) is expressed as C(t)=I C ([C s (t)-C e (t)] + ≥r(t))·r(t) (9) Among them I C (·) is the indicator function, when [C s (t)-C e (t)] + ≥r(t)I C (·)=1, r(t) is the minimum expected communication rate of the link per time slot, C s (t) and C e (t) are the transmission rate from the UAV to the satellite and the eavesdropping rate, respectively, expressed as as well as Where B is the bandwidth; The probability of safe interruption from drone to satellite meets Pr{[C s (t) -C e (t)] + ≤r(t)}≤ε (12) Where ε≤0.1 is the maximum allowable interruption probability.

4. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 3, characterized in that: The implementation method of step three is: The position parameters of Eve are estimated based on the radar echo signal; the uncertainty model of the potential eavesdropper's position is: In the formula is the location estimate obtained when sensing a potential eavesdropper, Δl e (t) is the position measurement error; In real location e (t) satisfies the Gaussian distribution, that is, Where δ is the measurement standard deviation; the measurement error of the tth time slot satisfies Among them G MF is the MF gain; The distribution measured over multiple time slots is accumulated as in satisfy is the measurement standard deviation of the cumulative distribution function; the threshold of measurement accuracy is set to Ω, when When , Eve’s position perception is sufficient; The estimated eavesdropping channel state is in Indicates that Eve's estimated position is In this case, t = the eavesdropping channel state at position [i, j].

5. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 4, characterized in that: In step four, In each mission, the UAV collects N data files to be transmitted in the communication blind area. Each data file is not related to each other. The UAV needs to upload all the data it carries to the satellite in this mission. Each data file is associated with an onboard computing task. The nth onboard computing task is used (d an ,d bn ,c n ) description, where c n To calculate the number of CPU revolutions required for this task, d an and d bn are the data sizes before and after calculation respectively; define indicator variable x n (t)∈{0,1} represents the calculation strategy of the nth task in the tth time slot, x n (t) = 1 means that the data of the nth task has been calculated and processed, otherwise x n (t) = 0; for simplicity, define d a =[d a1 ,…,d aN ] T ,d b =[d b1 ,…,d bN ] T and c=[c1,…,c N ] T ; Using the computing function of the drone, the data files are compressed during the flight upload process to reduce the amount of data transmission; t time slot, drone position l t , carrying compressed data Uncompressed data volume Its satisfaction as well as in is the amount of data uploaded from the compressed queue, is the amount of data uploaded from the uncompressed queue, ΔT is the time slot length; To calculate the amount of data reduction in the uncompressed queue, The amount of data increase in the compressed queue after calculation is x(t)=[x1(t),…,x N (t)] T is the calculation strategy for the tth time slot.

6. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 5, characterized in that: The implementation method of step five is: Step 5.1: Optimizing the safe transmission time under limited power constraints. Analyze the main constraints in the proposed scenario and then establish a joint optimization problem for the combined power, perception strategy, computation strategy, and trajectory planning. Analyze the power consumption constraints on the drone; the power consumption includes communication power consumption, computing power consumption and perception power consumption; the total power of the drone in each time slot is constrained, the total power P t satisfy P t =P w (t)+P e (t)+P c (t)≤P max ,t∈T (19) Among them, P w (t)=|w(t)| 2 is the transmission power, P e (t)=|w e (t)| 2 is the perceived power, To calculate power, To calculate the power consumption factor, f c (t) is the calculation frequency, P max The upper limit of power allowed for each time slot; In order to jointly optimize limited onboard resources to solve the problem of efficient and secure data transmission in satellite coverage blind spots under eavesdropping environments, the initial problem P1 is established, which enables data to be uploaded to the satellite in the shortest time under secure transmission conditions, while the drone returns to the starting point to perform the next mission; Therefore, the objective function of the initial total problem and the corresponding constraints are expressed as follows st(9),(18), (20a) c T x(t)≤ΔTf c (t),t∈T (20c) 1≤T s ≤T end (20d) 0≤f c (t)≤f max ,t∈T, (20e) x n (t)∈{0,1},t∈T (20f) in is the closed-loop flight trajectory, T end To satisfy and The moment; T s is the perception strategy, that is, the drone perception end time, satisfying To calculate the strategy, To calculate the frequency, is the transmission power variable, is the perception power variable; (20b) indicates that all onboard data should be transmitted when the UAV returns to the starting point, and (20c) indicates that the computing tasks in each time slot should be completed within the time slot; (20d), (20e) and (20f) impose basic constraints on the perception time, computation frequency and computation strategy range; due to the non-convexity of the optimization objective (20), x n (t) The coupling between continuous variables and the influence of the randomness of the optimization objective introduced by statistical CSI, the initial problem P1 is described as a mixed integer nonlinear programming problem MINLP with random objective probabilistic optimization; Step 5.2: Based on the problem decomposition of the situational awareness and anti-interception transmission task phase, the original optimization problem is split into two sub-processes: situational awareness and anti-interception transmission, to reduce the complexity of the multi-domain resource optimization problem; T s To divide the problem, the initial problem P1 is decomposed into two stages. The first stage is the situational awareness stage. In this stage, the eavesdropping environment is unknown. The UAV gradually determines the state of the eavesdropping channel by sensing Eve's position, and simultaneously performs data transmission and onboard calculations until the perception accuracy requirement is met. This stage ends. The second stage is the anti-interception transmission stage. In this stage, the eavesdropping environment state is known. Based on the environmental situation perceived by the UAV in the first stage, the UAV trajectory is planned, the power is compressed, and the power is uploaded to minimize the task completion time. The initial problem P1 is transformed into a two-layer problem. The main problem is to design the first-stage movement trajectory, upload power, perception power, calculation frequency, perception strategy, and calculation strategy of the UAV to gradually determine Eve's location and minimize the task completion time under secure transmission conditions. The secondary problem is to design the second-stage movement trajectory, upload power, calculation frequency, and calculation strategy of the UAV under a certain eavesdropping environment to minimize the second-stage task completion time. definition is the first stage path, Calculate frequency, transmission power and calculation strategy for the first stage; is the second stage path, Calculate frequency, transmission power and calculation strategy for the second stage; The initial problem P1 is equivalent to st(20a),(20d), (21a) c T x(t)≤ΔTf c (t),t∈[1,T s ] (21b) 0≤f c (t)≤f max ,t∈[1,T s ] (21c) x n (t)∈{0,1},t∈[1,T s ] (21d) in, is the shortest mission completion time of the second stage that can be achieved under the environmental situation perceived by the UAV in the first stage; U is determined by existential and X I Composition, expressed as There is a group Satisfies (22a), (22b), (22c), (22d), Through the anti-interception transmission problem P3, we can get: st(20a),(20b), (22a) c T x(t)≤ΔTf c (t),t∈[T s +1,T end ] (22b) 0≤f c (t)≤f max ,t∈[T s +1,T end ] (22c) x n (t)∈{0,1},t∈[T s +1,T end ] (22d)。 7. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 6, characterized in that: The implementation method of step six is: Since the objective function in the situation awareness problem P2 has the characteristics of long-term accumulation, the situation awareness problem P2 is transformed into a Markov decision problem, and the DRL method is used to solve the specific problem; The situational awareness problem P2 is transformed into a Markov decision process, i.e., a tuple (S, A, R, P, γ), where S is the state space, A is the action space, R is the reward space, P is the transition probability, and γ is the reward discount factor. At time t, the drone first observes the current environment information to obtain the state s t ∈S, take action a t ∈A acts on the environment, and then uses the transition probability P(s t+1 ∣s t ,a t )Transfer to the next state s t+1 , and generates the reward r after the action is executed t ; G t are the eavesdropping channel state and satellite channel state of the target area sensed at time t respectively; a t ={l t+1 ,P w (t+1),P s (t+1),f c (t+1),x(t+1),b t } (24) where b t ∈{0,1} indicates whether the perception phase is over, b t =1 means that the perception phase ends at the current moment; from (24), we know that l t+1 ,x(t+1),b t is a discrete variable, P w (t+1),P s (t+1),f c (t+1) is a continuous variable; therefore, the agent's output action is a mixed high-dimensional vector, which increases the complexity of the search space for finding the optimal action; r t =R(t)+X1(t)+X2(t) (25) The constant penalty term X1(t), R(t) is expressed as: R(t)=ξ(P s (t),t)-ξ(P s (t-1),t-1) (26) The penalty term X2(t) with hysteresis is expressed as Secondly, according to s t ∈S,a t ∈A and r t , trained through DDPG, deployed the trained parameters on the UAV, and obtained the perceived environmental situation, as well as D(L I )parameter.

8. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 7, characterized in that: The implementation method of step seven is: Project P3 to variable L II , the optimization trajectory problem P4 is expressed as L II ∈V (28b) Where D(L I ) is the amount of data completed in the first stage, D(L II ) is given by L II The maximum amount of data completed, D(L II ) is given by the subproblem st(19),(22b),(22c),(22d)(29a) Pr{(C s (t)-C e (t))≤r(t)}≤ε(29b) Since the eavesdropping channel state in the anti-interception transmission phase is known, the position where the main channel transmission rate is less than the eavesdropping rate is set to be unreachable by the drone; therefore, P5 satisfies C s (t)≥C e (t), the safety interruption probability is written in the form of (29b), and V is determined by making D(L II ) exists II Composition, expressed as The anti-interception transmission problem P3 is transformed into an iteration of two problems; the optimization trajectory problem P4 is a shortest path planning problem based on the A* algorithm. Next, we solve the MINLP sub-problem P5, and then introduce the shortest path planning algorithm based on the A* algorithm to solve the optimization trajectory problem P4.

9. The method for jointly planning UAV trajectories and multi-domain resources based on situational awareness according to claim 8, characterized in that: The implementation method of step eight is: Step 8.1: Use the equivalent rate constraint to transform the optimization problem P5 of computing frequency, upload power, and computing strategy into an optimization problem with a deterministic objective function. By introducing the equivalent rate constraint lemma, we can obtain a closed expression for the minimum expected communication rate of the t-th time slot link. Step 8.2: Convert the problem P5 of optimizing the computation frequency, upload power, and computation strategy into a series of sequential subproblems to obtain subproblem P6. Based on the equivalent rate constraint, the optimal result of the problem P5 for optimizing the calculation frequency, upload power, and calculation strategy is achieved by maximizing the amount of completed data in each time slot. In the tth time slot, the subproblem corresponding to the problem P5 for optimizing the calculation frequency, upload power, and calculation strategy is expressed as st(29a),(29b)(31a) The closed-form expression is Step 8.3: Perform communication and computation decoupling iterations to obtain the continuous knapsack problem P7 and subproblem P8. P5 subproblem P6 can be rewritten as: s.t.c T x(t)≤c max ,(33a) in P7 is further simplified to a continuous knapsack problem with additional constraints: Therefore, by solving for a given c sum The optimal strategy and the optimal c sum Iterate between updates to solve the continuous knapsack problem P7; and X * (c sum ) means that under the constraint c T x(t)=c sum The optimal c under sum and the corresponding optimal calculation strategy; X * (c sum ) is obtained by greedy algorithm; further, c sum After bringing in P7, we get s.t.c sum <c max (34a) in By optimizing P8, P7 and P6 iteratively, the optimal result of MINLP communication subproblem P5 is obtained.

10. The method for joint planning of UAV trajectory and multi-domain resources based on situational awareness according to claim 9, characterized in that: The implementation method of step nine is: The drone has now reached t The hovering point, taking into account both the path length and the channel state, uses the secondary cost function shown in Equations (35) and (36) to determine the next point to be expanded: f1(l t+1 ) is the first-level cost function considering the path length, f2(l t+1 ) is a secondary cost function that considers the channel state; in the planning process, first, according to f1(l t+1 ) to make a first-level judgment and select the point with the smallest path length cost to expand. If the path costs of multiple points are the same, a second-level judgment is made and the channel state cost f2(l t+1 ) higher point expansion; by continuously selecting f(l t+1 ) value is expanded until the target point appears in the set of adjacent points, that is, the shortest path planning is achieved; COST and FRONTIER are used to track f(l t+1 ) and expansion process; each point reached retains a pointer to its parent point in CAMEFROM to facilitate tracing back the path; Use CAMEFROM to construct a shortest path from the starting point to the end point; The deviation path algorithm is used to solve the K shortest path problem. Define node i = 1, 2, ..., N, where (1) is the starting point and (N) is the end point. d ij ,i≠j, represents the direct distance from (i) to (j). If a path exists between points i and j, then d ij is a finite number, otherwise it is considered infinite; A k =(1)-(2 k )-(3 k )-…-(Q k k )-(N), k=1,2,…,K is the kth shortest path from (1) to (N), where (2 k ),(3 k ),…,(Q k k ) are the 2nd, 3rd, ..., Qth of the kth shortest path k nodes; i=1,2,…,Q k , is from A k-1 The set of "deviations" at (i); Yes and A k-1 Overlapping subpaths; Yes and A k-1 The latter part of the path has only one node overlap; After the first iteration, the shortest path A is determined 1 ; After the kth iteration (k=2,3,…,K), determine the k shortest paths A k Finally, the shortest path is obtained, achieving the minimum completion time under the constraint of the lower limit of data completion amount; Iteratively optimize the trajectory optimization problem P4 and the calculation frequency, upload power and calculation strategy optimization problem P5 to obtain the optimal solution to the anti-interception transmission problem P3, thereby minimizing the completion time of the second phase mission; The frequency, upload power, and calculation strategy are calculated based on the planned drone trajectory to obtain the shortest path for the transmission task, while matching the corresponding calculation and transmission power to minimize the task completion time.

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