Flight trajectory planning method, system, and storage medium based on random event capture

Abstract

The present invention discloses a flight trajectory planning method based on random event capture, which includes: building a relationship model between flight trajectory and energy consumption based on the historical flight data of the fixed-wing drone; building a random event emulation model in the cruise task based on the camera parameters on the fixed-wing drone and the distribution situation of the event interest points in the cruise area; constraining the three-dimensional trajectory machine of the fixed-wing drone based on the relationship model and the random event model, and performing variable discretization based on the cruise task period, thereby building a target optimization model based on the acceleration in the three-dimensional direction; inputting the cruise task into the target optimization model, and optimizing the flight trajectory of the fixed-wing drone using a reinforcement learning method to obtain a planning result. The present invention further provides a flight trajectory planning system and a storage medium. The method of the present invention can effectively improve the energy efficiency of the event capture of the fixed-wing drone, thereby improving the automation level of the patrol of the fixed-wing drone.

Description

[Technical Field]

[0001] The present invention relates to the technical field of drone flight planning, and in particular to a flight trajectory planning method, system, and storage medium based on random event capture. [Background technology]

[0002] In recent years, the drone (unmanned aerial vehicle, UAV) industry has seen rapid development, expanding its applications from military to civilian use. According to the "Guiding Opinions on Promoting and Standardizing the Development of the Civilian Drone Manufacturing Industry" of the Ministry of Industry and Information Technology, civilian drone production value is expected to reach approximately 180 billion yuan by 2025. In 2018, the China Academy of Communications published the "5G Drone Application White Paper," which detailed the application scenarios and communication demands of connected drones in logistics, agricultural and plant protection, patrol, surveying and mapping, and live streaming, among others, with patrol drones showing the largest future market potential. Currently, patrol drones are emerging in areas that ensure people's personal safety, such as power supply, roads, and urban security. Drones offer low cost, high flexibility, high safety, and resistance to natural environments and terrain, allowing them to replace humans in harsh environments and provide better monitoring perspectives and patrol quality.

[0003] Patent document CN116113025A discloses a method for trajectory design and power allocation in a drone cooperative communication network, which includes constructing a power allocation model with the goal that the final virtual surplus energy of a node is closest to the maximum capacity of the node's battery, and using a reinforcement learning distance reward / punishment algorithm to solve the power allocation model and obtain an optimization plan for node resource allocation and surplus energy. The method allocates drone communication trajectories and power using a reinforcement learning method.

[0004] Patent document CN115877871A discloses a non-zero-sum game drone formation control method based on reinforcement learning, which includes the following steps: S1: construct a drone dynamics model; S2: construct a non-zero-sum game formation model; S3: solve the non-zero-sum game formation model constructed in step S2 using reinforcement learning; S4: design a non-zero-sum game formation controller. This method employs reinforcement learning to organize the drone flight trajectories. Summary of the Invention

[0005] The objective of the present invention is to provide a flight trajectory planning method, system, and storage medium based on random event capture that can effectively improve the energy efficiency of event capture of fixed-wing drones, thereby improving the automation level of fixed-wing drone patrols.

[0006] To achieve the first object of the present invention, a flight trajectory planning method based on random event capture is provided, which includes: Building a relationship model between flight trajectory and energy consumption based on historical flight data of fixed-wing drones; Constructing a random event emulation model for a cruise task based on camera parameters on the fixed-wing drone and the distribution of event interest points within the cruise area; Constraining the three-dimensional trajectory of the fixed-wing drone based on the relational model and the random event model, and performing variable discretization based on the cruise task period, thereby constructing a target optimization model based on the three-dimensional acceleration; The method includes inputting a cruise task into the target optimization model and using a reinforcement learning method to optimize the flight trajectory of the fixed-wing drone to obtain a planning result.

[0007] The present invention obtains optimal flight planning results by constructing a relationship model between trajectory and energy consumption and a random event model, and then employing a reinforcement learning method to solve the target function formed by their combination.

[0008] Specifically, the relationship model specifically calculates the total energy consumption of the fixed-wing drone during the cruise task time as the integral of the electric power over the cruise task time.

[0009] Specifically, the relationship model integrates the total energy consumption after a fixed-wing drone completes a predetermined flight trajectory based on flight time, and the formula is as follows:

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[0010] Specifically, the generation of each event interest point within the cruise area follows a Poisson distribution.

[0011] Specifically, the random event capture emulation model obtains all events captured by the fixed-wing drone within the cruise task period by determining whether the event of the event interest point in the cruise area is within the camera shooting range of the fixed-wing drone when the event occurs, and the formula is as follows:

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[0012] Specifically, the reinforcement learning method selects each flight behavior of the fixed-wing drone and generates a corresponding flight trajectory by using the minimum internal energy consumption within the cruise task period as a reward under predetermined state parameters, where the state parameters include the dynamic characteristics of the event interest point, the current position and current speed of the fixed-wing drone, and the flight behavior includes the three-dimensional acceleration of the fixed-wing drone at each time, and the formula is as follows:

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[0013] Specifically, the flight operations include turning left at zero pitch angle, turning right at zero pitch angle, flying straight at zero pitch angle, turning left at positive pitch angle, turning right at positive pitch angle, flying in a straight line at positive pitch angle, turning left at negative pitch angle, turning right at negative pitch angle, and flying in a straight line with a negative pitch angle.

[0014] Based on the angle between the acceleration direction of the fixed-wing drone and the current flight direction, a flight action is selected and a specific flight trajectory is generated, and the acceleration direction is obtained by fitting with the three-dimensional acceleration obtained by solving using the reinforcement learning method.

[0015] Specifically, the forward direction of the fixed-wing drone is the positive X-axis direction, the right turn direction is the positive Y-axis direction, and the upward direction is the positive Z-axis direction. When the acceleration in the z-axis direction is zero, if the acceleration in the y-axis direction is less than zero, turn left with zero pitch angle.

[0016] When the acceleration in the z-axis direction is zero, if the acceleration in the y-axis direction is greater than zero, turn right with zero pitch angle.

[0017] When the acceleration in the z-axis direction and the acceleration in the y-axis direction are both equal to zero, the robot moves straight with a zero pitch angle.

[0018] If the acceleration in the z-axis direction is greater than zero and the acceleration in the y-axis direction is less than zero, then turn left at a positive pitch angle.

[0019] If the acceleration in the z-axis direction is greater than zero and the acceleration in the y-axis direction is greater than zero, then turn right at a positive pitch angle.

[0020] When the acceleration in the z-axis direction is greater than zero and the acceleration in the y-axis direction is equal to zero, the aircraft will fly in a straight line with a positive pitch angle.

[0021] If the acceleration in the z-axis direction is less than zero and the acceleration in the y-axis direction is less than zero, turn left with a negative pitch angle.

[0022] If the acceleration in the z-axis direction is less than zero and the acceleration in the y-axis direction is greater than zero, turn right with a negative pitch angle.

[0023] Fly a straight line with a negative pitch angle when the acceleration in the z-axis direction is less than zero and the acceleration in the y-axis direction is equal to zero.

[0024] In order to achieve the second object of the present invention, there is provided a flight trajectory planning system implemented based on the above flight trajectory planning method, wherein the system includes a random event Emulation It includes a drone control module, a reinforcement learning module, and a selection and decision module.

[0025] The random event Emulation A module is used to generate the occurrence and disappearance times of random events for each event interest point.

[0026] The reinforcement learning module updates a Q-table of the relationship between each flight operation of the fixed-wing drone and the expected bonus based on the time of occurrence and disappearance of the random event and the state and position of the fixed-wing drone.

[0027] The selection and decision module selects a flight action for the fixed-wing drone based on a maximization bonus and constructs a corresponding flight path.

[0028] The drone control module is used to control a fixed-wing drone based on a selected flight maneuver and flight path.

[0029] To achieve the third object of the present invention, a storage medium is provided containing a program stored thereon, wherein the program, when executed, causes a processor to perform the above-described random event capture based flight trajectory planning method.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: Plan the flight trajectory of a fixed-wing drone based on the actual propulsion energy and motion relationship in three-dimensional free space, as well as the occurrence and disappearance of random events.

[0031] At the same time, reinforcement learning methods are used to optimize and analyze the flight behavior of the fixed-wing drone and obtain the optimal flight trajectory. [Brief explanation of the drawings]

[0032] [Figure 1] FIG. 1 is a schematic diagram of a flight trajectory planning method based on random event capture according to the present embodiment. [Figure 2] FIG. 1 is a schematic diagram of flight behavior rules in reinforcement learning according to the present embodiment. [Figure 3] FIG. 10 is a diagram showing instantaneous power when the fixed-wing drone according to the present embodiment flies in a straight line at a constant speed. [Figure 4] FIG. 10 is a diagram illustrating the relationship between the speed of the fixed-wing drone in a turning state and the angle formed between the acceleration vector according to this embodiment. [Figure 5] FIG. 10 is a diagram showing the relationship between the magnitude of acceleration and power when the fixed-wing drone according to the present embodiment is in a turning state. [Figure 6] 10 shows training curves for nine flight actions in reinforcement learning for a fixed-wing drone according to this embodiment. [Figure 7] FIG. 10 is a linear relationship diagram between event point of interest density and energy efficiency according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0033] The present invention will be further described below with reference to specific examples. It should be understood that these examples are only for the purpose of illustrating the present invention and do not limit the scope of the present invention. It should also be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms are also within the scope defined by the appended claims of this application.

[0034] As shown in FIG. 1, a flight trajectory planning method based on random event capture, comprising: Building a relationship model between trajectory and energy consumption based on historical flight data of fixed-wing drones; Constructing a random event emulation model for a cruise task based on camera parameters on the fixed-wing drone and the distribution of event interest points within the cruise area; Constraining the three-dimensional trajectory of the fixed-wing drone based on the relational model and the random event model, and performing variable discretization based on the cruise task period, thereby constructing a target optimization model based on the three-dimensional acceleration; The method includes inputting a cruise task into the target optimization model and using a reinforcement learning method to optimize the flight trajectory of the fixed-wing drone to obtain a planning result.

[0035] More specifically, a set of event interest points

number

[0036] Fixed-wing drone trajectory and energy consumption relationship model: The total energy consumption of a fixed-wing drone includes its propulsion energy and communication energy. Note that in practice, the propulsion energy of a fixed-wing drone is usually much larger than its signal processing energy. Therefore, in this invention, the communication energy is ignored.

[0037] The 3D trajectory of a fixed-wing drone is q(t)=[x(t)y(t)h(t)] T ∈R 3×1 ,0≦t≦T. The velocity and acceleration vectors of a fixed-wing drone are

number

[0038] The fixed-wing drone instantaneous power model is as follows:

number

[0039] Calculate the total energy consumption of a fixed-wing drone over time T as the integral of power P(v,a) over time T:

number

[0040] Event occurrence and capture: Assuming that each event interest point i∈O is static on the ground, its position is l i =(x i ,y i ,0). Specifically, each event interest point can represent a hotspot for traffic accidents or a point prone to jungle fires. As shown in Figure 1, at any given time t∈(0,T), events at any event interest point i∈O occur independently in space and time. Under this assumption, the event generation process at each event interest point follows a Poisson process. Each event interest point has a random event arrival rate λ and disappearance rate μ, which represent the frequency with which events occur and disappear at that location. Let X and Y describe the event dynamics of the event interest point. X denotes the event residence time, and Y denotes the event arrival time. Note that X and Y follow an exponential distribution with means 1 / λ and 1 / μ.

[0041] During task time T, event interest point i receives a series of n iEvent C i ={C i 1 ,C i 2 ,…,C i ni Consider that event c occurs. i q occurs at event interest point i during time t∈T. i q Let (t)∈0,1, and we obtain the following function:

number

[0042] The fixed-wing drone is equipped with an image sensor, and the stabilized camera can guarantee a circular coverage area. Let ρ denote the field of view (FoV) of the image sensor. At time t, the fixed-wing drone has a camera image footprint on the ground with radius r(t) = h(t) tan(ρ / 2). σ i t Let,be a binary variable, and it is set to 1 if the fixed-wing drone is within the coverage area of ​​the fixed-wing drone at time,t,.,The variable,σ, i t can be calculated with the following constraints:

number

number

number

[0043] ζ(c i q ,T) is the event capture indicator, i.e., ζ(c i q , T)=1 if it is captured during task time T.

[0044] So the constraints for event capture are:

number

[0045] All events captured by the fixed-wing drone during task period T are

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[0046] Based on the above two models, a corresponding target optimization model is constructed.

[0047] The surveillance and monitoring performance of fixed-wing drones is primarily limited by the power budget of their onboard batteries. While fixed-wing drones have the advantage of longer cruise times than rotary-wing drones, inefficient ballistic designs can lead to rapid battery depletion, i.e., instantaneous power P → ∞, defined as the ratio T of the number of captured events to the total energy consumption within the task time, as v → 0.

[0048] The optimization problem is modeled as a linear split-plot program:

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[0049] Constraints (8b) and (8c) provide a bounded surveillance area. Constraint (8d) provides a lower altitude limit for communication restrictions and an upper limit for image quality degradation. Considering the flight altitude h(t) of a fixed-wing drone, this altitude is the minimum height h(t) to achieve high-quality LoS channel conditions. min Higher than 3GPP (registered trademark), which requires a minimum flight altitude of 40 meters for rural macro scenes and 100 meters for urban macro scenes. (8e) ensures that fixed-wing drones fly at a certain minimum speed to stay airborne and do not exceed a maximum speed.

[0050] On the other hand, since the above optimization problem is complicated to solve directly, to ease the problem, the task period T is discretized into equal slots, with n=1,2,...,N as the index. The length of each time step T / N is selected to be long enough, and the fixed-wing drone's position is constant at each time t=nT / N. Therefore, the speed and trajectory are

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[0051] The above optimization problem can be transformed into:

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[0052] That is, for each slot n=1,2,...,N, the three-dimensional acceleration a x [n], a y [n] and a z [n] needs to be determined.

[0053] To better solve the above optimization problem, we use reinforcement learning to reduce its complexity, i.e., by performing an action a∈A and moving to another state, the bonus function calculates the number of such state-action pairs and records it in a Q-table, which is initialized to a specific goal state. The model is denoted as a tuple {S, A, {R}, γ}, as follows:

[0054] State, S: Q-learning is used to derive long-term policies in different environments (e.g., random occurrence and arrival of events). At each slot n, the action (i.e., the vector acceleration of the fixed-wing drone) is calculated based on the current system state s. n The system state vector s n includes: i) the dynamic features δ of the event interest point s; i q (nT / N); ii) the current position q[n] of the fixed-wing drone; iii) the current velocity vector v[n] of the fixed-wing drone.

[0055] Action,A: For each slot n, action a n changes the speed of the fixed-wing drone. The trajectory of a fixed-wing drone changes depending on the action (presenting the fixed-wing drone acceleration). The 3D Dubin-inspired path model describes the optimal path as a series of analogies with the fixed-wing drone: "right turn (R)", "left turn (L)", or "straight ahead (S)" and pitch angle, as shown in Figure 2. A sharp turn angle at high speed for a fixed-wing drone means energy dissipation. Therefore, let Φ and ψ be small, constant steering and pitch angles.

[0056] Each time, the fixed-wing drone can randomly take one of nine actions: i) a left turn with zero pitch angle, ii) a right turn with zero pitch angle, iii) a straight flight with zero pitch angle, iv) a left turn with positive pitch angle, v) a right turn with positive pitch angle, vi) a straight flight with positive pitch angle, vii) a left turn with negative pitch angle, viiii) a right turn with negative pitch angle, and ix) a straight flight with negative pitch angle. When the fixed-wing drone changes flight direction, it also adjusts its speed. Based on the instantaneous power model of the fixed-wing drone, the energy consumption rate is minimized.

[0057] Reward, R: Current status of Reward bonus nWe evaluated the impact of a fixed-wing drone's maneuver plan on the action taken at time step n. The bonus at time n is calculated as the ratio of the number of newly detected events to the propulsion energy at time n:

number

[0058] The proposed Q-learning policy needs to choose an action for a particular state to maximize the average reward within a certain time horizon. The UAV then calculates the Q(s n ,a n The optimal policy is learned by maintaining a Q-table value, denoted as (s, a). The Q-table is updated after completing each transition and observing the current state-action pair (s, a),

number

[0059] where α represents the learning rate that controls the convergence speed, and γ∈(0,1) is the discount factor. In the above model, the agent (UAV) needs to observe the geographical location of event interest points and the occurrence of those events. At each time step n, based on the Q-table, it creates a state s n Select the action that causes the maximum Q value above,

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[0060] Finally, a fixed-wing drone flight trajectory is planned based on the motion corresponding to the output maximum Q value.

[0061] The present invention further provides a flight trajectory planning system based on the flight trajectory planning method provided by the above embodiment, wherein the system includes: Emulation It includes a drone control module, a reinforcement learning module, and a selection and decision module.

[0062] The random event Emulation A module is used to generate the occurrence and disappearance times of random events for each event interest point.

[0063] The reinforcement learning module updates a Q-table of the relationship between each flight operation of the fixed-wing drone and the expected bonus based on the time of occurrence and disappearance of the random event and the state and position of the fixed-wing drone.

[0064] The selection and decision module selects a flight action for the fixed-wing drone based on a maximization bonus and constructs a corresponding flight path.

[0065] The drone control module is used to control a fixed-wing drone based on a selected flight maneuver and flight path.

[0066] At the same time, a storage medium containing a program stored thereon is also provided, wherein the program, when executed, causes a processor to perform the above flight trajectory planning method.

[0067] These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, thereby generating an article of manufacture that includes a command apparatus that implements the functions specified in one or more flows of the flowcharts and / or one or more blocks of the block diagrams. These computer program instructions may be loaded into a computer or other programmable data processing device, thereby causing the computer or other programmable apparatus to perform a series of operational steps to generate a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more flows of the flowcharts and / or one or more blocks of the block diagrams.

[0068] In order to better explain the technical solution of this implementation, Emulation An analysis of the evaluation results is provided.

[0069] In the scenario, a fixed-wing drone is tasked with cruising a 3 × 3 square kilometer area and capturing events of interest that occur at 10 locations within a 10-minute task period. Each event interest point has an event arrival rate of 0.05 and a disappearance rate of 0.05. The maximum and minimum heights of the fixed-wing drone are set to 500 feet and 40 meters, respectively. The energy consumption parameters c1 and c2 are set to 9.26 × 10 -4 and 2250. The mass of the fixed-wing drone including the payload is 10 kg. For the Q-learning method, we use a discount factor of 0.1 and a learning rate of 0.4. The length of each time step is 2 seconds.

[0070] Emulation We compare the results to a circular trajectory, which is the most common patrol trajectory for fixed-wing drones. We consider each point in the data and calculate the average result over 100 runs.

[0071] Figure 3 shows the instantaneous power consumption of a fixed-wing drone flying in a straight line at a constant speed. As the speed changes from 5 m / s to 65 m / s, the fixed-wing drone's energy consumption rate first drops from 450 watts to 100 watts. The power reaches its lowest point at 30 m / s and then slowly increases to 194 watts at 65 m / s. A reasonable guess is that when the fixed-wing drone's speed is slower than 5 m / s, the power output may be much higher than 450 watts. This indicates that fixed-wing drones consume battery power quickly. This proves that fixed-wing drones require different algorithms than rotary-wing drones, which typically sense their environment and hover.

[0072] Figure 4 shows the angular relationship between the velocity and acceleration vector of a fixed-wing drone in a turning state. This figure shows that the choice of acceleration direction is important, especially for high-speed fixed-wing drones. When ||v|| = 45 and ||a|| = 4, the instantaneous power can reach a maximum of 1900 watts and a minimum of 200 watts (10.5% of the maximum power). The fixed-wing drone's power consumption is lowest when the angle between the velocity and the acceleration direction z is 90 degrees. When ||v|| = 15 and ||a|| = 2, the difference between the minimum and maximum power consumption is not significant. More specifically, the minimum power is 8 watts (when $θ = 120$), and the maximum power is 453 watts.

[0073] As shown in Figure 5, the relationship between the magnitude of acceleration and power when a fixed-wing drone is turning. Emulation For this experiment, we set the speed to 30 m / s. When θ = 0 or θ = 180, the fixed-wing drone accelerates or decelerates in the same direction. When θ = 90, the fixed-wing drone turns. The results show that higher accelerations usually require more power. Also, turning requires less energy than accelerating in the same direction.

[0074] Figure 6 shows the training curves for nine flight behaviors of a fixed-wing drone. These are: zero-pitch left turn, zero-pitch right turn, zero-pitch straight, positive pitch left turn, positive pitch right turn, positive pitch straight, negative pitch left turn, and negative pitch right turn. Within the first 100 seconds, the nine behaviors are performed with approximately the same probability of 11%. Subsequently, during training, each behavior gradually increases or decreases until it reaches a stable state. Specifically, at t = 100 seconds, the S-positive, R-positive, and L-positive values ​​increase to approximately 13.5%, more than twice the S-positive values. In other words, fixed-wing drones are encouraged to fly at higher altitudes because fixed-wing drones at higher altitudes cover a larger area. During training, the R-negative and L-negative values ​​show similar trends. That is, the Q-learning method selects the R-negative or S-negative value in 9.1% of the slots. Fixed-wing drones chose S-negative maneuver least frequently, at 6%. This is because adjusting speed while maintaining the same heading consumes a lot of energy. Also, a negative pitch angle reduces the area that a fixed-wing drone can see.

[0075] Figure 7 shows how energy efficiency changes when the event interest point density ranges from 5 to 50. Experiments were conducted using the proposed Q-learning method and compared with a reference circular trajectory with an altitude of 152 meters and a radius of 500 meters. The average power efficiency within 10 minutes of task time was calculated. As shown in the figure, there is a linear relationship between event interest point density and energy efficiency. This is because a fixed-wing drone can encounter more events when the event interest point density is higher. For a circular trajectory with five event interest points, the energy efficiency is 2×10 -4 With 50 event interest points, this results in a 1.6×10 -3 The proposed Q-learning method shows that the energy efficiency is 43% higher than that of the circular trajectory.

[0076] In summary, the present invention aims to deeply study the path optimization problem of fixed-wing drones for patrolling specific events, and to design a 3D trajectory construction algorithm in dynamic environments, thereby improving the energy efficiency of event capture and the automation level of patrol aircraft.

Claims

1. A flight trajectory planning method based on random event capture, comprising: Building a relationship model between flight trajectory and energy consumption based on historical flight data of fixed-wing drones; Constructing a random event emulation model for a cruise task based on camera parameters on the fixed-wing drone and the distribution of event interest points within the cruise area; Constraining the three-dimensional trajectory of the fixed-wing drone based on the relational model and the random event emulation model, and performing variable discretization based on the cruise task period, thereby constructing a target optimization model based on the three-dimensional acceleration; inputting a cruise task into the target optimization model and optimizing the flight trajectory of the fixed-wing drone using a reinforcement learning method to obtain a planning result.

2. 2. The method for flight trajectory planning based on random event capture according to claim 1, wherein the historical flight data includes a 3D trajectory of the fixed-wing drone, a weight and wing area of ​​the fixed-wing drone, a speed and acceleration vector during a cruise task, and environmental factors.

3. The relationship model is constructed by integrating the total energy consumption after a fixed-wing drone completes a predetermined flight trajectory based on flight time, and the formula is as follows: [Equation 1] In the equation, q(t) represents the flight trajectory of the fixed-wing drone, v(t) represents the flight speed of the fixed-wing drone at time t, a(t) represents the acceleration of the fixed-wing drone at time t, P(v, a) represents the instantaneous power of the fixed-wing drone, v represents the instantaneous speed of the fixed-wing drone, a represents the instantaneous acceleration of the fixed-wing drone, g represents the acceleration due to gravity, m represents the mass of the fixed-wing drone, and c １ and C ２ denotes the constant term, 【number】 2. The method for flight trajectory planning based on random event capture according to claim 1, wherein: represents the time integral of total energy consumption at the completion of the flight trajectory of the fixed-wing drone.

4. The flight trajectory planning method based on random event capture according to claim 1 , wherein the generation of each event interest point in the cruise area follows a Poisson distribution.

5. The random event emulation model obtains all events captured by the fixed-wing drone within the cruise task period by determining whether the event of the event interest point in the cruise area is within the camera shooting range of the fixed-wing drone when the event occurs, and the formula is as follows: [Equation 2] In the formula, N denotes all events captured by the fixed-wing drone within the cruise task period, and ζ(c ｉ ｑ , T) denotes the event capture indicator, i.e., ζ(c ｉ ｑ , T) = 1, if the event interest point is captured during task time T, then σ ｉ ｔ indicates the camera's shooting range, and c ｉ ｑ indicates the event occurrence of the event interest point, and (x ｉ ｑ , y ｉ ｑ ) denotes the position coordinate of the event interest point, t denotes time, T denotes the cruise task period, and t∈T.

6. The reinforcement learning method selects each flight behavior of the fixed-wing drone and generates a corresponding flight trajectory with a minimum internal energy consumption within a cruise task period as a reward under predetermined state parameters, where the state parameters include the dynamic characteristics of the event interest point, the current position and current speed of the fixed-wing drone, and the flight behavior includes the three-dimensional acceleration of the fixed-wing drone at each time, and the formula is as follows: [Equation 3] In the formula, α denotes the learning rate that controls the convergence speed, and s ｎ indicates the state parameter at time n, and a ｎ indicates the three-dimensional acceleration at time n, a indicates the acceleration, A indicates the flight motion, [Equation 4] indicates the speed of the fixed-wing drone, and a[n] = {a ｘ [n], a ｙ [n], a ｚ 2. The method for flight trajectory planning based on random event capturing according to claim 1, wherein {{t, n, t}} denotes a three-dimensional acceleration of the fixed-wing drone, n = 1, 2, ... N denotes a time index, T denotes a cruise task period, and T / N denotes a time step.

7. the flight operations include turning left at zero pitch angle, turning right at zero pitch angle, flying straight at zero pitch angle, turning left at positive pitch angle, turning right at positive pitch angle, flying in a straight line at positive pitch angle, turning left at negative pitch angle, turning right at negative pitch angle, and flying in a straight line with a negative pitch angle; The flight trajectory planning method based on random event capture as described in claim 6, characterized in that a flight action is selected and a specific flight trajectory is generated based on the angle between the acceleration direction of the fixed-wing drone and the current flight direction, and the acceleration direction is obtained by fitting with the three-dimensional acceleration obtained by solving with a reinforcement learning method.

8. The target optimization model relaxes the problem by discretizing the task period T into equal slots, and the equation is as follows: [Equation 5] In the formula, a[n] = {a ｘ [n], a ｙ [n], a ｚ [n]} indicates the three-dimensional acceleration of the fixed-wing drone, [Equation 6] 2. The method for flight trajectory planning based on random event capturing according to claim 1, wherein h[n] denotes the speed of the fixed-wing drone, h[n] denotes the flight height of the fixed-wing drone, T denotes the cruise task period, n=1, 2, ... N denotes the index of time, and T / N denotes the time step.

9. A flight trajectory planning system realized by the flight trajectory planning method based on random event capturing according to any one of claims 1 to 8, comprising: a random event emulation module, a drone control module, a reinforcement learning module, and a selection and decision module; The random event emulation module is used to generate the occurrence and disappearance time points of random events for each event point of interest; The reinforcement learning module updates a Q-table of the relationship between each flight operation of the fixed-wing drone and an expected bonus based on the time of occurrence and disappearance of the random event and the state and position of the fixed-wing drone; The selection and determination module selects a flight operation of the fixed-wing drone based on a maximization bonus and constructs a corresponding flight path; A flight trajectory planning system, characterized in that the drone control module is used to control a fixed-wing drone based on a selected flight operation and flight path.

10. 10. A storage medium containing a program stored thereon, wherein the program, when executed, causes a processor to perform a flight trajectory planning method based on random event capturing according to any one of claims 1 to 8.

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