Unmanned aerial vehicle track correction method, device and equipment based on near space environment parameters and storage medium

Abstract

The invention discloses an unmanned aerial vehicle track correction method and device based on near space environment parameters, equipment and a storage medium. The method comprises the steps of generating a plurality of detection tasks based on a natural disaster monitoring task; dividing the near space into a plurality of detection areas according to the detection tasks, and allocating corresponding detection tasks and detection areas to the unmanned aerial vehicle; planning a flight path in the detection area based on the detection task; simulation is carried out according to the environment parameters and the flight trajectory of the detection area, and trajectory correction values under different environment conditions are obtained; and under the condition that the flight path needs to be corrected, performing flight path correction in the flight process of the unmanned aerial vehicle based on the path correction value, and executing a detection task to obtain detection data. According to the method, the flight path is corrected through the path correction value, the correction effect of the flight path is effectively improved, the detection task is executed, the execution efficiency of the detection task and the accuracy of the detection data are remarkably improved, and natural disaster early warning can be accurately carried out.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, apparatus, device, and storage medium for UAV trajectory correction based on near-space environmental parameters. Background Technology

[0002] With the rapid development of drone technology, drones have been widely used in various fields such as natural disaster monitoring, environmental monitoring, and meteorological observation. Especially in natural disaster monitoring, drones have advantages such as real-time data acquisition, wide coverage, and flexible operation.

[0003] In natural disaster monitoring missions, unmanned aerial vehicles (UAVs) often encounter complex and variable environmental parameters when performing detection tasks in near space, such as wind speed, wind direction, temperature, humidity, and air pressure. These environmental parameters significantly affect the flight trajectory of UAVs, potentially causing them to deviate from their intended paths and impacting the accuracy and efficiency of the monitoring mission. Traditional UAV trajectory correction methods mostly rely on ground control stations or pre-set algorithms. However, in the complex environment of near space, these methods often fail to accurately reflect actual environmental changes, resulting in poor correction effects and consequently affecting the effectiveness of natural disaster monitoring. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, and storage medium for UAV trajectory correction based on near-space environmental parameters, aiming to solve the technical problem that traditional UAV trajectory correction methods are difficult to accurately reflect actual environmental changes, resulting in poor correction effects and thus affecting the effectiveness of natural disaster monitoring.

[0005] To achieve the above objectives, this application proposes a UAV trajectory correction method based on near-space environment parameters, the method comprising: Upon receiving a natural disaster monitoring task, multiple detection tasks are generated based on the natural disaster monitoring task; Based on the aforementioned detection mission, the near space is divided into multiple detection zones, and corresponding detection missions and detection zones are assigned to the UAVs. The flight trajectory within the detection area is planned based on the aforementioned detection mission; Simulations were performed based on the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions to obtain the trajectory correction values ​​of the UAV under different environmental conditions. If the flight trajectory needs to be corrected, the UAV's trajectory correction value under different environmental conditions is used to correct the flight trajectory during the flight of the UAV and to perform the detection task, thereby obtaining detection data.

[0006] In one embodiment, the step of simulating the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions to obtain the trajectory correction value of the UAV under different environmental conditions includes: Obtain environmental parameters of the probe area under different environmental conditions; Environmental modeling is performed on the environmental parameters of the detection area under different environmental conditions to obtain an environmental model, which is used to simulate the impact of different environmental conditions on the flight of the UAV. A flight model of the UAV is constructed, and flight simulation is performed based on the flight model and the environment model according to the flight trajectory in the detection area to obtain the original flight trajectory map under different environmental conditions; Based on the original flight trajectory map and the environmental parameters of the detection area under different environmental conditions, the trajectory correction values ​​of the UAV under different environmental conditions are calculated.

[0007] In one embodiment, the calculation based on the original flight trajectory map and environmental parameters of the detection area under different environmental conditions yields trajectory correction values ​​for the UAV under different environmental conditions, including... Based on the environmental parameters of the detection area under the different environmental conditions, determine the air density correction parameters and drag correction parameters; The flight trajectory in the original flight trajectory diagram is corrected according to the air density correction parameter and drag correction parameter to obtain the corrected flight trajectory diagram under different environmental conditions; Based on the corrected flight trajectory map and the original flight trajectory map, the trajectory correction values ​​of the UAV under different environmental conditions are calculated. The formula for calculating the trajectory correction value is as follows: in, The drag coefficient, For air pressure, The gas constant is For temperature, For the speed of the drone, For cross-sectional area, For the quality of drones, This is the time increment.

[0008] In one embodiment, the step of planning the flight trajectory within the detection area based on the detection mission includes: The boundary of the detection area and the flight performance parameters of the UAV are obtained, including at least the flight duration, flight mode, and flight distance threshold. Based on the target of the detection mission, the boundary of the detection area, and the flight performance parameters, a preset trajectory planning algorithm is used to plan the flight trajectory to obtain the initial flight trajectory within the detection area. The initial flight trajectory is optimized using the particle swarm optimization algorithm to obtain the optimal flight path; The optimal flight path is taken as the flight trajectory within the detection area.

[0009] In one embodiment, optimizing the initial flight trajectory using a particle swarm optimization algorithm to obtain the optimal flight path includes: An initial particle swarm is generated based on the initial flight trajectory, and the initial position and initial velocity of each particle in the initial particle swarm are set, wherein the initial position of each particle represents a potential flight trajectory, and the initial velocity of each particle represents its tendency to move towards a potential better flight trajectory. Define a fitness function, wherein the fitness function is a weighted sum of multiple indicators, each indicator is weighted according to the priority of the task, and the indicators include at least flight distance, flight time, flight safety and path smoothness; The fitness value of each particle in the initial particle swarm is calculated using the fitness function. Update the individual optimal position and global optimal position of each particle based on the fitness value; The velocity and position of each particle are updated based on the individual optimal position and the global optimal position to obtain the updated particle swarm. This process continues until a preset iteration termination condition is met, at which point the optimal flight path corresponding to the global optimal position is output. The iteration termination condition is reaching the maximum number of iterations or the improvement in fitness value being less than a preset threshold. The formula for speed update is: in, Let i be the velocity of particle i in the next iteration. Let i be the velocity of particle i in the current iteration. This represents the historical best position of particle i. The global optimal position of the particle swarm. Inertial weights are used to control the search range. , The acceleration constant is used to control the particle's ability to learn towards both the individual optimal solution and the global optimal solution. , It is a random number. Let i be the position of particle i in the current iteration.

[0010] In one embodiment, the step of dividing the area into multiple detection zones according to the detection task and assigning corresponding detection tasks and detection zones to the UAV includes: The near space is divided into different functional areas according to the detection target of the detection mission, wherein each functional area corresponds to a specific detection target and environmental characteristics; Based on the constraints of the shape, size, spatial distribution of the detection area and the flight trajectory of the UAV, a spatial segmentation model is constructed. The spatial segmentation model is used to divide each functional area into multiple detection areas; Based on the size of the detection area, the complexity and priority of the detection task, and the UAV's flight capability and endurance, assign corresponding detection tasks and detection areas to the UAV.

[0011] In one embodiment, after the UAV corrects its flight trajectory and performs the detection task during flight to obtain detection data, the step of using the trajectory correction value further includes: Constructing natural disaster prediction models; The detection data is input into a natural disaster prediction model, and the natural disaster prediction model makes predictions based on the detection data to obtain the type, intensity, and affected area of ​​the natural disaster. The disaster emergency response strategy and the scope of early warning information dissemination shall be determined based on the type of natural disaster, the intensity of the disaster, and the area affected by the disaster. Natural disaster early warnings are issued based on the aforementioned disaster emergency response strategies and the scope of early warning information dissemination.

[0012] Furthermore, to achieve the above objectives, this application also proposes a UAV trajectory correction device based on near-space environmental parameters, the UAV trajectory correction device based on near-space environmental parameters comprising: The generation module is used to generate multiple detection tasks based on the natural disaster monitoring task when a natural disaster monitoring task is received; The partitioning module is used to divide the near space into multiple detection areas according to the detection task, and to assign corresponding detection tasks and detection areas to the UAV. The planning module is used to plan the flight trajectory within the detection area based on the detection mission; The simulation module is used to simulate the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions, and obtain the trajectory correction value of the UAV under different environmental conditions; The correction module is used to correct the flight trajectory of the UAV during flight and perform the detection task when the flight trajectory needs to be corrected, based on the trajectory correction value of the UAV under different environmental conditions, and to obtain detection data.

[0013] Furthermore, to achieve the above objectives, this application also proposes a UAV trajectory correction device based on near-space environmental parameters. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the UAV trajectory correction method based on near-space environmental parameters as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the UAV trajectory correction method based on near-space environmental parameters as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the UAV trajectory correction method based on near-space environmental parameters as described above.

[0016] This application generates multiple detection tasks based on a natural disaster monitoring task upon receiving the task; divides the near-space into multiple detection areas according to the tasks, and assigns corresponding detection tasks and areas to a UAV; plans a flight trajectory within the detection area based on the tasks; simulates the environmental parameters of the detection area and the flight trajectory under different environmental conditions to obtain trajectory correction values ​​for the UAV under different environmental conditions; when the flight trajectory needs correction, the UAV corrects its flight trajectory during flight based on the trajectory correction values ​​under different environmental conditions and executes the detection task to obtain detection data. Through this method, by combining near-space environmental parameters and flight trajectory simulation, the trajectory correction values ​​for the UAV under different environmental conditions are accurately analyzed, and the flight trajectory is corrected during flight, effectively improving the correction effect and thus significantly enhancing the execution efficiency and accuracy of detection data for natural disaster monitoring, facilitating accurate natural disaster early warning. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the UAV trajectory correction method based on near-space environmental parameters provided in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the UAV trajectory correction method based on near-space environmental parameters provided in this application; Figure 3 This is a schematic diagram of the module structure of the UAV trajectory correction device based on near-space environmental parameters according to an embodiment of this application; Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the UAV trajectory correction method based on near-space environmental parameters in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: upon receiving a natural disaster monitoring task, multiple detection tasks are generated based on the natural disaster monitoring task; the adjacent space is divided into multiple detection areas according to the detection tasks, and corresponding detection tasks and detection areas are assigned to the UAV; a flight trajectory within the detection area is planned based on the detection tasks; simulation is performed based on the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions to obtain the trajectory correction value of the UAV under different environmental conditions; if the flight trajectory needs to be corrected, the UAV corrects its flight trajectory and executes the detection task during flight based on the trajectory correction value of the UAV under different environmental conditions to obtain detection data.

[0024] In natural disaster monitoring missions, unmanned aerial vehicles (UAVs) often encounter complex and variable environmental parameters when performing detection tasks in near space, such as wind speed, wind direction, temperature, humidity, and air pressure. These environmental parameters significantly affect the flight trajectory of UAVs, potentially causing them to deviate from their intended paths and impacting the accuracy and efficiency of the monitoring mission. Traditional UAV trajectory correction methods mostly rely on ground control stations or pre-set algorithms. However, in the complex environment of near space, these methods often fail to accurately reflect actual environmental changes, resulting in poor correction effects and consequently affecting the effectiveness of natural disaster monitoring.

[0025] This application provides a solution that, by combining near-space environmental parameters and flight trajectory simulation, accurately analyzes the trajectory correction value of UAV under different environmental conditions, and then corrects the flight trajectory during the flight of the UAV, effectively improving the correction effect of the flight trajectory, thereby performing detection tasks, significantly improving the execution efficiency of natural disaster monitoring detection tasks and the accuracy of detection data, and facilitating accurate natural disaster early warning.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a UAV trajectory correction device based on near-space environmental parameters capable of achieving the above functions. The following description uses a UAV trajectory correction device based on near-space environmental parameters as the executing entity to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, embodiments of this application provide a method for UAV trajectory correction based on near-space environmental parameters, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the UAV trajectory correction method based on near-space environmental parameters of this application.

[0028] In this embodiment, the UAV trajectory correction method based on near-space environment parameters includes steps S10~S50: Step S10: Upon receiving a natural disaster monitoring task, generate multiple detection tasks based on the natural disaster monitoring task.

[0029] It should be noted that the natural disaster monitoring task refers to the task of monitoring natural disasters in a specific area. This task can be initiated by relevant departments or institutions to monitor and warn of possible natural disasters, such as earthquakes, floods, and fires. This embodiment does not impose specific restrictions on this.

[0030] It should be understood that, according to the requirements of natural disaster monitoring tasks, the task is broken down into multiple sub-tasks. Each sub-task corresponds to a detection task, used to monitor a specific area or a specific type of disaster. In this way, natural disaster monitoring tasks can be completed more meticulously and comprehensively, improving monitoring efficiency and accuracy.

[0031] Step S20: Divide the near space into multiple detection areas according to the detection task, and assign corresponding detection tasks and detection areas to the UAV.

[0032] It should be noted that near space refers to the transitional region between Earth's atmosphere and outer space, usually located in the upper layer of the atmosphere, at an altitude of about 20 to 100 kilometers. The environmental parameters in this airspace are complex and variable, which have a significant impact on the flight trajectory of UAVs.

[0033] It should be understood that the detection mission in this embodiment is conducted in near-space. Therefore, in order to achieve comprehensive coverage of the entire natural disaster monitoring area, the near-space needs to be rationally divided into multiple detection zones. Each detection zone has specific geographical features and potential types of natural disasters. The UAV is assigned to a specific detection zone and corresponding detection mission. This ensures that the UAV can collect relevant environmental parameters and disaster information in a targeted manner during flight, improving the targeting and efficiency of data collection.

[0034] Specifically, the near space is divided into multiple detection zones according to the needs of the detection mission, with each zone corresponding to the detection missions of one or more UAVs. This approach allows for more targeted natural disaster monitoring, improving both efficiency and accuracy. Furthermore, assigning each UAV a specific detection mission and zone ensures that it follows a predetermined trajectory during flight, avoiding flight conflicts and redundant detection, thus enhancing detection efficiency.

[0035] It is worth noting that when allocating detection tasks and areas, the flight capabilities and environmental adaptability of the UAVs must also be considered. For example, some areas may be affected by extreme weather conditions, such as strong winds and low temperatures, which places higher demands on the flight performance and stability of the UAVs. Therefore, when allocating tasks, the technical parameters of the UAVs and the environmental conditions of the detection area must be comprehensively considered to ensure that the UAVs can complete the detection tasks safely and reliably.

[0036] In one feasible implementation, step S20 may include steps A11 to A14: Step A11: Divide the near space into different functional areas according to the detection target of the detection mission, wherein each functional area corresponds to a specific detection target and environmental characteristics.

[0037] It should be noted that the division of functional zones is based on the specific needs of the reconnaissance mission and the environmental characteristics of the near space. For example, certain areas may become key monitoring targets due to complex terrain or severe weather conditions. These areas can be divided into specific functional zones so that UAVs can perform reconnaissance missions more accurately.

[0038] Step A12: Construct a spatial segmentation model based on the shape, size, spatial distribution of the detection area and the constraints of the UAV flight trajectory.

[0039] It should be noted that a spatial segmentation model is a mathematical model used to divide a complex space into multiple simple and easily manageable subspaces. In this embodiment, the spatial segmentation model is used to rationally divide the adjacent space into multiple detection areas so that the UAV can conduct detection according to a predetermined trajectory. When constructing the spatial segmentation model, the shape, size, spatial distribution of the detection areas, and the constraints of the UAV's flight trajectory need to be considered to ensure that the UAV can safely and efficiently complete the detection task during flight. Through the spatial segmentation model, a complex adjacent space can be divided into multiple subspaces with specific functions and characteristics, providing more accurate and reliable guidance for the UAV's detection tasks.

[0040] Step A13: Divide each functional area into multiple detection areas using the spatial segmentation model.

[0041] It should be noted that, based on the characteristics of the functional areas and the requirements of the detection mission, each functional area is further subdivided into smaller detection areas. These detection areas may vary in shape, size, and spatial distribution to adapt to different detection targets and environmental conditions. In this way, it can be ensured that the UAV can conduct detection according to a predetermined trajectory in each detection area, while improving the targeting and efficiency of the detection.

[0042] Step A14: Assign corresponding detection tasks and detection areas to the UAV based on the size of the detection area, the complexity and priority of the detection task, and the UAV's flight capability and endurance.

[0043] It should be noted that, considering the size of the detection area, tasks can be equally allocated to different drones based on the size of each area and the complexity of the task. Generally speaking, larger areas or more complex detection tasks will be assigned to drones with stronger performance.

[0044] Considering the flight capabilities and endurance of drones, if each drone has a limited flight time, tasks can be allocated based on the drone's maximum flight time. Drones with longer flight times can be responsible for larger areas, while drones with shorter flight times can be responsible for smaller areas.

[0045] Considering the priority of reconnaissance missions, some areas may require higher priority missions (such as monitoring specific sensitive areas or key targets), and more drones or more powerful drones can be assigned to these areas.

[0046] Specifically, task allocation is dynamically adjusted based on each drone's flight performance, payload, and mission priority to ensure a relatively balanced workload for each drone and avoid overload or resource waste.

[0047] When assigning reconnaissance tasks, it is necessary to ensure the feasibility of the assigned tasks based on the performance parameters of the UAVs (such as flight time, flight speed, maximum flight distance, flight mode, etc.). For example, if the size of a certain reconnaissance area may exceed the flight range or flight time limit of a single UAV, then it is necessary to assign multiple UAVs or adjust the size of the task area.

[0048] When multiple drones are working collaboratively, task allocation also needs to consider the cooperation between drones, such as avoiding repeatedly exploring the same area, or allocating target areas among multiple drones in a specific order. This requires the system to be able to dynamically adjust the drones' flight paths and task execution order.

[0049] In practical implementation, considering the size of the detection area, the complexity and priority of the detection task, and the flight capability and endurance of the UAV, an optimization algorithm can be used to assign the optimal detection task and detection area to each UAV. This optimization algorithm can weigh various factors, such as the UAV's flight performance, payload capacity, task priority, and environmental conditions of the detection area. By comprehensively considering these factors, it can be ensured that the UAV can efficiently collect data during flight, while avoiding resource waste and flight conflicts. For example, heuristic search algorithms, genetic algorithms, and other optimization methods can be used to solve the task allocation problem, achieving more accurate and efficient detection task allocation. This embodiment does not impose specific limitations on this.

[0050] Step S30: Plan the flight trajectory within the detection area based on the detection mission.

[0051] It is important to note that when planning the flight path, the UAV's flight performance, environmental parameters, and the specific requirements of the detection mission must be fully considered. The planned flight path should ensure that the UAV can safely and efficiently reach the target detection area and conduct detection within that area according to the predetermined trajectory. To achieve this goal, a pre-defined trajectory planning algorithm can be used. This algorithm comprehensively considers the UAV's flight capabilities, environmental adaptability, and the requirements of the detection mission to generate the optimal flight path. Simultaneously, the flight path planning also needs to consider the UAV's endurance to ensure that the UAV can safely return to base or proceed to the next mission after completing its initial task.

[0052] In practice, the flight trajectory can be dynamically adjusted based on the environmental characteristics of the detection area and the flight performance of the UAV to adapt to complex and ever-changing environmental conditions.

[0053] In one feasible implementation, step S30 may include steps B11 to B14: Step B11: Obtain the boundary of the detection area and the flight performance parameters of the UAV, the flight performance parameters including at least flight duration, flight mode and flight distance threshold.

[0054] It should be noted that obtaining the boundary information of the detection area is for the purpose of determining the drone's flight range and the specific location of the detection target. Boundary information can include the shape, size, and geographical location of the detection area, which is crucial for planning the drone's flight trajectory.

[0055] Meanwhile, the flight performance parameters of the UAV are also an important basis for planning its flight trajectory. The flight duration determines the length of time the UAV can perform its mission, the flight mode (such as straight flight, curved flight, hovering, etc.) affects the UAV's detection efficiency and flexibility, and the flight distance threshold limits the UAV's flight range. By comprehensively considering these factors, the UAV's flight trajectory and detection strategy can be preliminarily determined.

[0056] Step B12: Based on the target of the detection mission, the boundary of the detection area, and the flight performance parameters, a preset trajectory planning algorithm is used to plan the flight trajectory to obtain the initial flight trajectory within the detection area.

[0057] It's important to note that the objective of the reconnaissance mission determines the types of data and accuracy requirements that the drone needs to collect, which are key factors to consider when planning its flight path. For example, if the objective of the reconnaissance mission is to monitor weather conditions in a specific area, then the drone needs to fly along a specific trajectory and at a specific altitude to ensure that it can accurately collect the required meteorological data.

[0058] The boundary information of the detection area defines the drone's flight range and the specific location of the target. When planning the flight path, it is necessary to ensure that the drone can fly within the boundary area and detect the target according to the predetermined trajectory. At the same time, the impact of terrain, obstacles, and other factors within the detection area on the flight path must be considered to ensure the drone's flight safety.

[0059] Flight performance parameters form the basis for flight trajectory planning. Parameters such as the UAV's flight duration, flight mode, and flight distance threshold directly affect flight trajectory planning and the execution of the detection mission. For example, if the UAV's flight duration is short, a more efficient flight trajectory needs to be planned to ensure that the UAV can complete the detection mission within a limited time.

[0060] In its implementation, the pre-defined trajectory planning algorithm can automatically generate the optimal flight trajectory based on information such as the target of the detection mission, the boundary of the detection area, and flight performance parameters. This algorithm comprehensively considers multiple factors, such as the UAV's flight speed, altitude, and heading, to ensure that the UAV flies along the predetermined trajectory and performs efficient detection within the detection area. Furthermore, the algorithm can dynamically adjust the flight trajectory based on the UAV's real-time position and status information to adapt to complex and changing environmental conditions.

[0061] Step B13: Optimize the initial flight trajectory based on the particle swarm optimization algorithm to obtain the optimal flight path.

[0062] It should be noted that Particle Swarm Optimization (PSO) is a swarm intelligence-based optimization method that simulates the foraging behavior of bird flocks. Through information sharing and cooperation among individuals within the group, it seeks the globally optimal solution. In this embodiment, PSO is used to optimize the initial flight trajectory to improve the detection efficiency and flight safety of the UAV.

[0063] Specifically, the initial flight trajectory is treated as an individual particle in a swarm, with each individual representing a possible flight path. Then, based on the target of the detection mission, the boundary of the detection area, and flight performance parameters, a fitness function is defined to evaluate the merits of each individual. The fitness function comprehensively considers factors such as the UAV's flight distance, flight time, and detection efficiency to ensure that the obtained flight path meets the requirements of the detection mission. Next, the position and velocity of the particles are iteratively updated to continuously approach the global optimum. In each iteration, the fitness value of each particle is calculated according to the fitness function, and the global optimum and the individual optimum are updated. Then, based on the information from the individual and global optimum solutions, the velocity and position of the particles are adjusted to gradually approach the global optimum. Through multiple iterations, the optimal flight path is finally obtained, which ensures that the UAV flies efficiently and safely within the detection area according to the predetermined trajectory.

[0064] In one feasible implementation, step B13 specifically includes: generating an initial particle swarm based on the initial flight trajectory, and setting the initial position and initial velocity of each particle in the initial particle swarm, wherein the initial position of each particle represents a potential flight trajectory, and the initial velocity of each particle represents its tendency to move towards a potentially better flight trajectory; setting a fitness function, wherein the fitness function is a weighted sum of multiple indicators, each indicator being weighted according to the priority of the task, and the indicators include at least flight distance, flight time, flight safety, and path smoothness; calculating the fitness value of each particle in the initial particle swarm using the fitness function; updating the individual optimal position and global optimal position of each particle based on the fitness value; updating the velocity and position of each particle based on the individual optimal position and global optimal position to obtain an updated particle swarm, until a preset iteration termination condition is met, and outputting the optimal flight path corresponding to the global optimal position, wherein the iteration termination condition is reaching the maximum number of iterations or the improvement in fitness value is less than a preset threshold.

[0065] It should be noted that when generating the initial particle swarm based on the initial flight trajectories, the position and velocity of each particle are randomly generated to ensure the breadth and diversity of the search space. The choice of initial position represents different potential flight trajectories, while the initial velocity reflects the tendency and motivation of the particle to move towards a potentially better solution. By setting reasonable initial positions and initial velocities, the particle swarm can converge to the global optimum more efficiently in subsequent iterations. In this embodiment, the initial position of each particle represents a potential flight trajectory, and the initial velocity of each particle represents its tendency to move towards a potentially better flight trajectory.

[0066] It should be understood that when setting the fitness function, multiple indicators need to be comprehensively considered based on the specific requirements of the detection mission and the flight performance parameters of the UAV, such as flight distance, flight time, flight safety, and path smoothness. These indicators reflect the key factors that the UAV needs to consider when performing detection missions. By assigning reasonable weights to each indicator, it can be ensured that the fitness function can comprehensively and accurately evaluate the merits of each particle, thereby guiding the particle swarm towards the global optimum.

[0067] In practical implementation, the fitness function can be calculated based on a preset mathematical model and algorithm. By inputting information such as the UAV's flight performance parameters, the boundary information of the detection area, and the target of the detection mission, the fitness function can output the fitness value of each particle. This value reflects the quality of the flight trajectory represented by the particle. In this embodiment, the fitness function is a weighted sum of multiple indicators, each of which is weighted according to the priority of the task. The indicators include at least flight distance, flight time, flight safety, and path smoothness.

[0068] Updating the individual optimal position and global optimal position of each particle based on its fitness value is one of the key steps in the particle swarm optimization algorithm. The individual optimal position records the best solution found by each particle during the iteration process, while the global optimal position records the best solution found by the entire particle swarm. By continuously updating these two positions, the particle swarm can gradually converge to the global optimal solution.

[0069] When updating the velocity and position of particles, information about the individual optimal position and the global optimal position is required, along with preset parameters such as the learning factor and inertia weight. The learning factor determines the degree to which the particle learns towards its individual and global optimal positions, while the inertia weight controls the tendency of the particle to maintain its original state of motion. By setting these parameters appropriately, the particle swarm can maintain adequate exploration and development capabilities during the iteration process, thereby finding the global optimal solution more efficiently.

[0070] The iteration termination condition is set to ensure that the particle swarm optimization algorithm converges to a stable optimal solution within a finite time. Common iteration termination conditions include reaching the maximum number of iterations or the improvement in fitness value being less than a preset threshold. When the iteration termination condition is met, the algorithm will stop iterating and output the optimal flight path corresponding to the globally optimal position. This path is obtained by comprehensively considering factors such as the requirements of the exploration mission, the flight performance parameters of the UAV, and the environmental characteristics of the exploration area, thus ensuring the efficiency and safety of the UAV when performing exploration missions.

[0071] The formula for speed update is: in, Let i be the velocity of particle i in the next iteration. Let i be the velocity of particle i in the current iteration. This represents the historical best position of particle i. The global optimal position of the particle swarm. Inertial weights are used to control the search range. , The acceleration constant is used to control the particle's ability to learn towards both the individual optimal solution and the global optimal solution. , It is a random number. Let i be the position of particle i in the current iteration.

[0072] The formula for position update is: in, Let i be the position of particle i in the next iteration (i.e., the new flight path). Let i be the velocity of particle i in the current iteration. Let i be the position of particle i in the current iteration.

[0073] Step B14: Use the optimal flight path as the flight trajectory within the detection area.

[0074] Step S40: Simulate the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions to obtain the trajectory correction value of the UAV under different environmental conditions.

[0075] It is important to note that when conducting simulations, the impact of various environmental parameters on the drone's flight trajectory must be fully considered. These environmental parameters may include meteorological conditions such as wind speed, wind direction, temperature, and air pressure, as well as geographical features such as terrain and obstacles. By simulating the effects of these environmental factors on the drone's flight trajectory, more accurate trajectory correction values ​​can be obtained, thereby improving the stability and safety of the drone in actual flight.

[0076] In practical implementation, advanced simulation software and algorithms can be used to simulate the flight process of unmanned aerial vehicles (UAVs). These software programs and algorithms, based on physical laws and mathematical models, can accurately calculate the flight trajectory and state parameters of UAVs under different environmental conditions. By inputting the UAV's flight performance parameters, the boundary information of the detection area, and the target information of the detection mission, the simulation software can generate realistic flight scenarios and simulate the UAV's flight process within those scenarios. During the simulation, it is crucial to focus on the changes in key parameters such as the UAV's flight trajectory, speed, altitude, and attitude. Simultaneously, the interaction between the UAV and its environment, such as the impact of air resistance and terrain obstacles on the flight trajectory, must be considered. By comprehensively analyzing these parameters and factors, trajectory correction values ​​for the UAV under different environmental conditions can be obtained.

[0077] Step S50: If the flight trajectory needs to be corrected, the UAV's flight trajectory is corrected based on the trajectory correction value under different environmental conditions during the flight of the UAV, and the detection task is performed to obtain detection data.

[0078] It should be noted that during actual flight, drones may encounter various unforeseen circumstances and environmental changes, causing the planned flight path to fail to meet the requirements of the detection mission. In such cases, it is necessary to correct the flight path in real time based on pre-obtained trajectory correction values. This trajectory correction process can be achieved using advanced control algorithms and sensor technology. By monitoring the drone's flight status and environmental parameters in real time, the control algorithm can calculate the necessary trajectory correction amount and guide the drone to fly along the corrected trajectory. In this way, even in complex and changing environmental conditions, the drone can maintain a stable flight state and efficiently and accurately detect targets according to the predetermined detection strategy. Finally, the drone transmits the collected detection data back to the base or a designated data center for natural disaster analysis.

[0079] Specifically, when the flight trajectory needs to be corrected, a target trajectory correction value is selected from the trajectory correction values ​​of the UAV under different environmental conditions according to the current environmental conditions, and the flight trajectory of the UAV is adjusted in real time based on the value to ensure that the UAV can safely and accurately complete the detection mission according to the corrected trajectory.

[0080] In one feasible implementation, after step S50, steps C11-C14 may be included: Step C11: Construct a natural disaster prediction model.

[0081] It should be noted that the natural disaster prediction model is built upon historical disaster data and real-time detection data collected by drones. This model can comprehensively consider multiple factors, such as meteorological conditions, geological structure, and topography, to assess the likelihood and potential impact of disasters.

[0082] Understandably, by inputting detection data from drones and other relevant information, the prediction model can output key information such as the probability of disaster occurrence, disaster type, and scope of impact, providing a basis for disaster early warning and emergency response.

[0083] Step C12: Input the detection data into the natural disaster prediction model, and make a prediction based on the detection data to obtain the natural disaster type, disaster intensity and disaster-affected area.

[0084] It should be noted that after the detection data is input into the natural disaster prediction model, the model will analyze and calculate based on this data. This data may include various environmental parameters collected by the drone within the detection area, such as meteorological conditions and topographic features, as well as the drone's flight trajectory and status parameters. By comprehensively utilizing this data, the natural disaster prediction model can identify potential disaster characteristics and, based on this, assess the probability, type, intensity, and potential impact area of ​​the disaster.

[0085] It should be understood that during the prediction process, models may employ various algorithms and techniques, such as machine learning, statistical analysis, and physical simulation. These algorithms and techniques can process and analyze large amounts of data, extracting useful information and features to predict and assess disasters. By continuously optimizing and improving the prediction model, its accuracy and reliability can be enhanced.

[0086] Step C13: Determine the disaster emergency response strategy and the scope of early warning information dissemination based on the type of natural disaster, the intensity of the disaster, and the area affected by the disaster.

[0087] It should be noted that disaster emergency response strategies refer to the development of corresponding emergency measures and resource allocation plans for different types of natural disasters, varying disaster intensities, and affected areas. These strategies may include multiple aspects such as personnel evacuation, material allocation, and rescue operations, aiming to minimize the losses and impacts caused by disasters. Simultaneously, based on disaster forecasting results, the scope of early warning information dissemination is determined to ensure that relevant information is promptly and accurately communicated to potentially affected populations and organizations so that they can take appropriate preventative measures.

[0088] Step C14: Conduct natural disaster early warning based on the disaster emergency response strategy and the scope of early warning information dissemination.

[0089] It should be noted that natural disaster early warnings are disseminated to the public and relevant organizations through various channels and methods to enhance people's awareness and capabilities in disaster prevention and mitigation. Early warning information may include key information such as the type, intensity, affected area, and expected time of occurrence of the disaster, as well as corresponding prevention suggestions and emergency measures. Utilizing various media and communication methods, including radio, television, the internet, and mobile phone text messages, ensures that early warning information can be rapidly and widely disseminated to the audience. Upon receiving early warning information, people can take corresponding preventative measures based on the suggestions, such as finding safe shelters, preparing necessary emergency supplies, and paying attention to the latest information released by official sources, to mitigate the potential losses and impacts of the disaster. Simultaneously, relevant organizations can also activate emergency response mechanisms based on the early warning information, allocating resources and manpower for rescue and relief work.

[0090] This embodiment generates multiple detection tasks based on a natural disaster monitoring task upon receiving the task. The adjacent space is divided into multiple detection areas according to these tasks, and corresponding detection tasks and areas are assigned to the UAV. A flight trajectory is planned within each detection area based on the detection tasks. Simulations are performed based on environmental parameters of the detection areas and the flight trajectory under different environmental conditions to obtain trajectory correction values ​​for the UAV under different conditions. If trajectory correction is needed, the UAV corrects its flight trajectory during flight based on these correction values ​​and executes the detection task to obtain detection data. By combining adjacent space environmental parameters and flight trajectory simulations, the trajectory correction values ​​for the UAV under different environmental conditions are accurately analyzed. This allows for trajectory correction during flight, effectively improving the accuracy of flight trajectory correction and thus significantly enhancing the execution efficiency and accuracy of detection data in natural disaster monitoring, facilitating accurate natural disaster early warning.

[0091] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The UAV trajectory correction method based on near-space environment parameters further includes steps S401 to S404 in step S40: Step S401: Obtain environmental parameters of the detection area under different environmental conditions.

[0092] It should be noted that environmental parameters may include, but are not limited to, meteorological conditions such as wind speed and direction (e.g., crosswind, headwind, tailwind), temperature, humidity, and air pressure. Wind direction affects the horizontal and vertical movement of the drone. Temperature, humidity, and air pressure affect air density, which in turn affects the lift and drag of the aircraft. These parameters can be obtained through various sensors carried by the drone, such as meteorological sensors.

[0093] Step S402: Based on the environmental parameters of the detection area under different environmental conditions, perform environmental modeling to obtain an environmental model, which is used to simulate the impact of different environmental conditions on the flight of the UAV.

[0094] It should be noted that environmental modeling refers to simulating the impact of factors such as airflow, wind speed, and terrain on the flight trajectory under different environmental conditions. For example, wind speed and direction can be simulated using simple linear wind speed models or meteorological models. Terrain is simulated using DEM data to depict topographic relief, ensuring the flight path does not intersect with the ground. Airflow: If there are complex airflow disturbances, physical models or CFD simulations can be used.

[0095] Step S403: Construct a flight model of the UAV, and perform flight simulation based on the flight model and the environment model according to the flight trajectory in the detection area to obtain the original flight trajectory map under different environmental conditions.

[0096] It should be noted that the flight model is described using the UAV's dynamic equations. For example, in a simplified 2D model, the flight model can be represented as: UAV position (x, y): changing with time; velocity (v): its relationship with time; control inputs: such as rudder, elevator, thrust, etc. In three-dimensional space, the flight model also needs to consider altitude (z), yaw, pitch, roll, etc.

[0097] Understandably, simulations are then performed by combining flight and environmental models. The flight trajectory can be solved using numerical integration methods (such as the Euler method, Runge-Kutta method, etc.), thus obtaining the original flight trajectory diagrams of the UAV under different environmental conditions. These original flight trajectory diagrams show the expected flight path of the UAV without the influence of trajectory corrections.

[0098] Step S404: Based on the original flight trajectory map and the environmental parameters of the detection area under different environmental conditions, calculate the trajectory correction value of the UAV under different environmental conditions.

[0099] It should be noted that the calculation of trajectory correction values ​​typically relies on advanced algorithms. These algorithms analyze the relationship between the original flight trajectory map and environmental parameters, identify potential flight deviations, and calculate the necessary trajectory corrections accordingly. These algorithms may include, but are not limited to, machine learning algorithms, optimization algorithms, or prediction algorithms based on physical models. By comprehensively considering the impact of various environmental factors such as wind speed, wind direction, temperature, humidity, and air pressure on UAV flight, the algorithm can accurately calculate the trajectory correction values ​​required for the UAV under different environmental conditions. This ensures that the UAV can fly accurately according to the predetermined detection mission, thereby improving the efficiency and accuracy of natural disaster monitoring.

[0100] In one feasible implementation, step S404 may include steps D11 to D13: Step D11: Determine the air density correction parameter and drag correction parameter based on the environmental parameters of the detection area under the different environmental conditions.

[0101] It's important to note that air density and drag are crucial factors affecting drone flight performance. Air density varies with temperature, humidity, and air pressure, directly impacting the drone's lift and thrust efficiency. Drag, on the other hand, is related to the drone's shape, speed, and environmental weather conditions, influencing its flight distance and speed. By accurately measuring and analyzing environmental parameters, appropriate air density and drag correction parameters can be determined, allowing for more accurate predictions of drone flight performance.

[0102] Step D12: Correct the flight trajectory in the original flight trajectory diagram according to the air density correction parameter and drag correction parameter to obtain the corrected flight trajectory diagram under different environmental conditions.

[0103] It should be noted that the revised flight trajectory diagram shows the expected flight path of the UAV after taking into account air density and drag corrections. These paths are closer to actual flight conditions and reduce deviations caused by environmental factors.

[0104] Step D13: Calculate the trajectory correction values ​​of the UAV under different environmental conditions based on the corrected flight trajectory map and the original flight trajectory map.

[0105] It should be noted that by comparing the corrected flight trajectory with the original flight trajectory, the effect of the trajectory correction can be clearly seen. The trajectory correction value reflects the amount of adjustment required by the UAV to maintain the predetermined flight path under different environmental conditions. These correction values ​​are crucial for guiding the UAV's trajectory adjustments during actual flight, ensuring that the UAV can accurately perform detection tasks under complex and changing environmental conditions, and improving the reliability and accuracy of natural disaster monitoring.

[0106] The formula for calculating the trajectory correction value is: in, The drag coefficient, For air pressure, The gas constant is For temperature, For the speed of the drone, For cross-sectional area, For the quality of drones, This is the time increment.

[0107] This embodiment acquires environmental parameters of the detection area under different environmental conditions; performs environmental modeling based on these parameters to obtain an environmental model, which is used to simulate the impact of different environmental conditions on the drone's flight; constructs a flight model for the drone, and performs flight simulation based on the flight model and environmental model according to the flight trajectory within the detection area to obtain original flight trajectory maps under different environmental conditions; and calculates the trajectory correction values ​​for the drone under different environmental conditions based on the original flight trajectory maps and the environmental parameters of the detection area under different environmental conditions. Through this method, the trajectory correction values ​​for the drone under different environmental conditions are accurately analyzed, effectively improving the trajectory correction effect and ensuring that the drone can accurately perform detection tasks under complex and changing environmental conditions, thereby improving the reliability and accuracy of natural disaster monitoring.

[0108] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the UAV trajectory correction method based on near-space environmental parameters of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0109] This application also provides a UAV trajectory correction device based on near-space environmental parameters, please refer to... Figure 3 The UAV trajectory correction device based on near-space environment parameters includes: The generation module 10 is used to generate multiple detection tasks based on the natural disaster monitoring task when a natural disaster monitoring task is received; The partitioning module 20 is used to divide the near space into multiple detection areas according to the detection task, and to assign corresponding detection tasks and detection areas to the UAV. Planning module 30 is used to plan the flight trajectory within the detection area based on the detection mission; The simulation module 40 is used to simulate the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions, and to obtain the trajectory correction value of the UAV under different environmental conditions. The correction module 50 is used to correct the flight trajectory of the UAV based on the trajectory correction value of the UAV under different environmental conditions when the flight trajectory needs to be corrected, and to perform the detection task to obtain detection data.

[0110] The UAV trajectory correction device based on near-space environmental parameters provided in this application employs the UAV trajectory correction method based on near-space environmental parameters in the above embodiments. This solves the technical problem that traditional UAV trajectory correction methods often fail to accurately reflect actual environmental changes, resulting in poor correction effects and consequently impacting natural disaster monitoring. Compared to the prior art, the beneficial effects of the UAV trajectory correction device based on near-space environmental parameters provided in this application are the same as those of the UAV trajectory correction method based on near-space environmental parameters provided in the above embodiments. Furthermore, other technical features of the UAV trajectory correction device based on near-space environmental parameters are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0111] This application provides a drone trajectory correction device based on near-space environmental parameters. The drone trajectory correction device based on near-space environmental parameters includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the drone trajectory correction method based on near-space environmental parameters in the above embodiment 1.

[0112] The following is for reference. Figure 4 This document illustrates a structural schematic diagram of a drone trajectory correction device based on near-space environmental parameters suitable for implementing embodiments of this application. The drone trajectory correction device based on near-space environmental parameters in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The UAV trajectory correction device based on near-space environment parameters shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0113] like Figure 4As shown, the UAV trajectory correction device based on near-space environment parameters may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the UAV trajectory correction device based on near-space environment parameters. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the UAV trajectory correction device based on near-space environment parameters to exchange data with other devices wirelessly or via wired communication. Although the figure shows a UAV trajectory correction device based on near-space environment parameters with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0114] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0115] The UAV trajectory correction device based on near-space environmental parameters provided in this application employs the UAV trajectory correction method based on near-space environmental parameters in the above embodiments. This solves the technical problem that traditional UAV trajectory correction methods often fail to accurately reflect actual environmental changes, resulting in poor correction effects and consequently impacting natural disaster monitoring. Compared to the prior art, the beneficial effects of the UAV trajectory correction device based on near-space environmental parameters provided in this application are the same as those of the UAV trajectory correction method based on near-space environmental parameters provided in the above embodiments. Furthermore, other technical features of this UAV trajectory correction device based on near-space environmental parameters are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0116] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0118] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the UAV trajectory correction method based on near-space environment parameters in the above embodiments.

[0119] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0120] The aforementioned computer-readable storage medium may be included in a UAV trajectory correction device based on near-space environmental parameters; or it may exist independently and not be assembled into a UAV trajectory correction device based on near-space environmental parameters.

[0121] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a UAV trajectory correction device based on near-space environmental parameters, the UAV trajectory correction device based on near-space environmental parameters: upon receiving a natural disaster monitoring task, generates multiple detection tasks based on the natural disaster monitoring task; divides the near-space into multiple detection areas according to the detection tasks, and assigns corresponding detection tasks and detection areas to the UAV; plans a flight trajectory within the detection area based on the detection tasks; simulates the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions to obtain trajectory correction values ​​for the UAV under different environmental conditions; and when the flight trajectory needs correction, corrects the flight trajectory of the UAV during flight based on the trajectory correction values ​​for the UAV under different environmental conditions and executes the detection tasks to obtain detection data.

[0122] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0124] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0125] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described UAV trajectory correction method based on near-space environmental parameters. This solves the technical problem that traditional UAV trajectory correction methods often fail to accurately reflect actual environmental changes, resulting in poor correction effects and consequently impacting natural disaster monitoring. Compared to existing technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the UAV trajectory correction method based on near-space environmental parameters provided in the above embodiments, and will not be elaborated upon here.

[0126] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for correcting the trajectory of a UAV based on near-space environmental parameters.

[0127] The computer program product provided in this application can solve the technical problem that traditional UAV trajectory correction methods are unable to accurately reflect actual environmental changes, resulting in poor correction effects and thus affecting the effectiveness of natural disaster monitoring. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the UAV trajectory correction method based on near-space environmental parameters provided in the above embodiments, and will not be repeated here.

[0128] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for correcting the trajectory of a UAV based on near-space environmental parameters, characterized in that, The method includes: Upon receiving a natural disaster monitoring task, multiple detection tasks are generated based on the natural disaster monitoring task; Based on the aforementioned detection mission, the near space is divided into multiple detection zones, and corresponding detection missions and detection zones are assigned to the UAVs. The flight trajectory within the detection area is planned based on the aforementioned detection mission; Simulations were performed based on the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions to obtain the trajectory correction values ​​of the UAV under different environmental conditions. If the flight trajectory needs to be corrected, the UAV's trajectory correction value under different environmental conditions is used to correct the flight trajectory during the flight of the UAV and to perform the detection task, thereby obtaining detection data.

2. The method as described in claim 1, characterized in that, The simulation, based on environmental parameters of the detection area and flight trajectory within the detection area under different environmental conditions, yields trajectory correction values ​​for the UAV under different environmental conditions, including: Obtain environmental parameters of the probe area under different environmental conditions; Environmental modeling is performed on the environmental parameters of the detection area under different environmental conditions to obtain an environmental model, which is used to simulate the impact of different environmental conditions on the flight of the UAV. A flight model of the UAV is constructed, and flight simulation is performed based on the flight model and the environment model according to the flight trajectory in the detection area to obtain the original flight trajectory map under different environmental conditions; Based on the original flight trajectory map and the environmental parameters of the detection area under different environmental conditions, the trajectory correction values ​​of the UAV under different environmental conditions are calculated.

3. The method as described in claim 2, characterized in that, The calculation is performed based on the original flight trajectory map and the environmental parameters of the detection area under different environmental conditions to obtain the trajectory correction values ​​of the UAV under different environmental conditions, including... Based on the environmental parameters of the detection area under the different environmental conditions, determine the air density correction parameters and drag correction parameters; The flight trajectory in the original flight trajectory diagram is corrected according to the air density correction parameter and drag correction parameter to obtain the corrected flight trajectory diagram under different environmental conditions; Based on the corrected flight trajectory map and the original flight trajectory map, the trajectory correction values ​​of the UAV under different environmental conditions are calculated. The formula for calculating the trajectory correction value is as follows: in, The drag coefficient, For air pressure, The gas constant is... For temperature, For the speed of the drone, For cross-sectional area, For the quality of drones, For time increments.

4. The method as described in claim 1, characterized in that, The flight trajectory planned within the detection area based on the detection mission includes: The boundary of the detection area and the flight performance parameters of the UAV are obtained, including at least the flight duration, flight mode, and flight distance threshold. Based on the target of the detection mission, the boundary of the detection area, and the flight performance parameters, a preset trajectory planning algorithm is used to plan the flight trajectory to obtain the initial flight trajectory within the detection area. The initial flight trajectory is optimized using the particle swarm optimization algorithm to obtain the optimal flight path; The optimal flight path is taken as the flight trajectory within the detection area.

5. The method as described in claim 4, characterized in that, The optimization of the initial flight trajectory based on the particle swarm optimization algorithm to obtain the optimal flight path includes: An initial particle swarm is generated based on the initial flight trajectory, and the initial position and initial velocity of each particle in the initial particle swarm are set, wherein the initial position of each particle represents a potential flight trajectory, and the initial velocity of each particle represents its tendency to move towards a potential better flight trajectory. Define a fitness function, wherein the fitness function is a weighted sum of multiple indicators, each indicator is weighted according to the priority of the task, and the indicators include at least flight distance, flight time, flight safety and path smoothness; The fitness value of each particle in the initial particle swarm is calculated using the fitness function. Update the individual optimal position and global optimal position of each particle based on the fitness value; The velocity and position of each particle are updated based on the individual optimal position and the global optimal position to obtain the updated particle swarm. This process continues until a preset iteration termination condition is met, at which point the optimal flight path corresponding to the global optimal position is output. The iteration termination condition is reaching the maximum number of iterations or the improvement in fitness value being less than a preset threshold. The formula for speed update is: in, Let i be the velocity of particle i in the next iteration. Let i be the velocity of particle i in the current iteration. This represents the historical best position of particle i. The global optimal position of the particle swarm. Inertial weights are used to control the search range. , The acceleration constant is used to control the particle's ability to learn towards both the individual optimal solution and the global optimal solution. , It is a random number. Let i be the position of particle i in the current iteration.

6. The method as described in claim 1, characterized in that, The process of dividing the area into multiple detection zones based on the detection task and assigning corresponding detection tasks and detection zones to the UAV includes: The near space is divided into different functional areas according to the detection target of the detection mission, wherein each functional area corresponds to a specific detection target and environmental characteristics; Based on the constraints of the shape, size, spatial distribution of the detection area and the flight trajectory of the UAV, a spatial segmentation model is constructed. The spatial segmentation model is used to divide each functional area into multiple detection areas; Based on the size of the detection area, the complexity and priority of the detection task, and the UAV's flight capability and endurance, assign corresponding detection tasks and detection areas to the UAV.

7. The method as described in claim 1, characterized in that, The step of correcting the flight trajectory of the UAV and performing the detection task during flight, based on the trajectory correction value, after obtaining the detection data, further includes: Constructing natural disaster prediction models; The detection data is input into a natural disaster prediction model, and the natural disaster prediction model makes predictions based on the detection data to obtain the type, intensity, and affected area of ​​the natural disaster. The disaster emergency response strategy and the scope of early warning information dissemination shall be determined based on the type, intensity, and affected area of ​​the natural disaster. Natural disaster early warnings are issued based on the aforementioned disaster emergency response strategies and the scope of early warning information dissemination.

8. A UAV trajectory correction device based on near-space environmental parameters, characterized in that, The UAV trajectory correction device based on near-space environment parameters includes: The generation module is used to generate multiple detection tasks based on the natural disaster monitoring task when a natural disaster monitoring task is received; The partitioning module is used to divide the near space into multiple detection areas according to the detection task, and to assign corresponding detection tasks and detection areas to the UAV. The planning module is used to plan the flight trajectory within the detection area based on the detection mission; The simulation module is used to simulate the environmental parameters of the detection area and the flight trajectory within the detection area under different environmental conditions, and obtain the trajectory correction value of the UAV under different environmental conditions; The correction module is used to correct the flight trajectory of the UAV during flight and perform the detection task when the flight trajectory needs to be corrected, based on the trajectory correction value of the UAV under different environmental conditions, and to obtain detection data.

9. A UAV trajectory correction device based on near-space environmental parameters, characterized in that, The UAV trajectory correction device based on near-space environment parameters includes: a memory, a processor, and a UAV trajectory correction program based on near-space environment parameters stored in the memory and executable on the processor. The UAV trajectory correction program based on near-space environment parameters is configured to implement the UAV trajectory correction method based on near-space environment parameters as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a UAV trajectory correction program based on near-space environment parameters. When the UAV trajectory correction program based on near-space environment parameters is executed by the processor, it implements the UAV trajectory correction method based on near-space environment parameters as described in any one of claims 1 to 7.

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