Surveying and mapping route optimization method based on unmanned aerial vehicle remote sensing

Abstract

The invention discloses a surveying and mapping route optimization method based on unmanned aerial vehicle remote sensing. The method comprises the steps of collecting historical data, disassembling the historical data into components, calculating a component difference, generating a dynamic offset vector, calculating a stability index based on the dynamic offset vector, a wind speed time sequence and a wind direction change rate, and generating a segmented path; a real shot image of the unmanned aerial vehicle is obtained, grids are generated according to the real shot image and the critical points of the segmented paths, and the paths are adjusted; acquiring real-time water area data to generate a water area grid, calculating a feasibility index of the grid, and planning an optimal water entry track based on the feasibility index and the model; obtaining an optimal water outlet track based on the feasibility index, extracting water outlet data according to the water outlet track, and converting the water outlet data and the air data; the optimal water entry trajectory, the underwater surveying and mapping path and the optimal water exit trajectory are integrated into an optimized path, the influence of wind on the flight path of the unmanned aerial vehicle is reduced, and the surveying and mapping precision and the task success rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing mapping optimization, and particularly to a mapping route optimization method based on unmanned aerial vehicle (UAV) remote sensing. Background Art

[0002] With the rapid development of UAV technology, UAVs are increasingly widely used in the field of mapping. UAV remote sensing mapping can quickly obtain large-area surface information with advantages such as high efficiency, flexibility, and low cost, providing important data support for many fields such as urban planning, resource exploration, and environmental monitoring. However, in actual UAV mapping tasks, there are many complex environmental factors and challenges, which have a significant impact on the accuracy and efficiency of mapping.

[0003] Traditional mapping route planning methods often ignore the dynamic changes of the wind field and adopt fixed flight paths. This makes the UAV vulnerable to the influence of the wind during flight and unable to fly along the expected path, thus reducing the mapping accuracy. Therefore, how to optimize the mapping route based on the changes in the wind field and reduce the influence of the wind on the UAV flight path has become a key issue for improving mapping accuracy. Terrain factors also have an important impact on UAV mapping. Complex terrain can lead to unstable UAV flight and increase flight risks. For example, in areas with large terrain undulations such as mountains and hills, the UAV may encounter problems such as airflow disorder and blocked vision during flight, affecting the quality of image acquisition. Existing mapping methods lack a real-time monitoring mechanism for terrain parameters and a dynamic path adjustment mechanism, and are unable to adjust the flight path in a timely manner according to the actual situation of the terrain, thus increasing flight risks and reducing mapping efficiency. Summary of the Invention

[0004] This application provides a mapping route optimization method based on UAV remote sensing. By disassembling historical data and optimizing segmented paths, it reduces the influence of the wind on the UAV flight path, improves mapping accuracy and mission success rate. By adding wind shelter points and adjusting flight speed, it significantly reduces flight deviation during high-risk periods and improves the accuracy of mapping data.

[0005] This application provides a mapping route optimization method based on UAV remote sensing, including: S101, collecting historical data, disassembling the historical data into components, calculating the component differences, generating a dynamic offset vector, calculating a stability index based on the dynamic offset vector, wind speed time series, and wind direction change rate, and generating segmented paths based on the stability index; S102, obtaining the actual captured images of the UAV, generating a grid based on the actual captured images and the critical points of the segmented paths, and adjusting the path based on the grid; S103. Obtain real-time water area data, generate a water area grid based on the water area data, calculate the feasibility index of the grid, and plan the optimal entry trajectory based on the feasibility index and the model. S104. Obtain the optimal exit trajectory based on the feasibility index, extract the exit data according to the exit trajectory, and convert the exit data with the aerial data. S105. Integrate the optimal entry trajectory, the underwater survey path, and the optimal exit trajectory into an optimized path.

[0006] Preferably, the formula for calculating the stability index is: , where represents the stability index, and its value range is between 0 and 1. , represents the standard deviation of the wind speed; represents the wind direction change rate; represents the covariance.

[0007] Preferably, the segmented path includes a high-risk period path, a medium-risk period path, and a low-risk period path.

[0008] Preferably, adjust the path based on the grid, detect the grid, and when a high-risk grid is detected, add an avoidance point at the high-risk grid, and update the flight path based on the avoidance point.

[0009] Preferably, the formula for calculating the feasibility index is: , where , and are weight coefficients, corresponding to the influence degrees of wave height, flow velocity, and energy loss respectively, and their value ranges are between 0 and 1, and satisfy α + β + γ = 1. is the feasibility index. is the wave height data, representing the vertical height of the wave. is the flow velocity data, representing the horizontal velocity of the water flow. is the energy loss data, representing the energy consumed by the UAV during the entry process.

[0010] Preferably, S201. Construct a four-dimensional grid according to the historical data and the elevation data. S202. Obtain the positions of the UAV group, associate the UAV positions with the four-dimensional grid, and the UAV group includes a leader UAV and follower UAVs. S203. The leader UAV generates a global path and a bubble mark, and guides the follower UAVs according to the bubble mark to realize the collaborative survey path of the UAV group.

[0011] Preferably, the elevation data refers to the height information data of the mission area in the vertical direction. The medium types are distinguished according to the obtained elevation data, and the medium types include land, water area and coastal zone.

[0012] Preferably, the pilot aircraft determines the activation duration of the bubble marker. When entering the water, T_in = 1.2 × T_w, and when exiting the water, T_out = 0.8 × T_w, where T_w is the wave period. The pilot aircraft obtains the value of T_w through the built-in wave period measuring device and then calculates the activation duration.

[0013] Preferably, after the bubble marker is deployed, it is affected by the wind speed and water flow speed and generates a drift displacement. The calculation formula for the drift distance is: )×t, where, represents the wind speed, represents the water flow speed, is the included angle between the wind speed and the water flow speed, and t is the time interval from the deployment of the bubble marker to the arrival of the slave aircraft.

[0014] Preferably, the calculation formula for the deployment position of the bubble marker is: , where, is the deployment position, is the initial deployment position, is the drift distance, is the unit vector of the drift direction, which is determined by the vector sum of the wind speed and the water flow speed.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: By disassembling historical data and optimizing the segmented path, the influence of wind on the flight path of the UAV is reduced, the mapping accuracy and mission success rate are improved. By adding shelter points and adjusting the flight speed, the flight deviation during high-risk periods is significantly reduced, and the accuracy of mapping data is improved. Based on real-time wind field and terrain data, risk grids are divided and the path is optimized to reduce the flight risk of the UAV, improve flight stability and image acquisition quality. By dividing risk grids, more refined risk assessment can be carried out for different regions, so as to more accurately identify high-risk regions. According to the real-time risk assessment results, the flight path can be adjusted to avoid high-risk regions and reduce flight risk; Based on real-time water area data, the cross-medium land-sea integrated mapping path is optimized to reduce the risk during medium switching, improve mapping accuracy and efficiency, and achieve seamless integration of water-air data. Through the feasibility index, more refined control of the medium switching process is realized, the risk is reduced, and the mapping accuracy and efficiency are improved through optimizing the mapping path. The four-dimensional grid comprehensively considers space, medium type, and risk indicators, providing more comprehensive and detailed information for the path planning of the unmanned aerial vehicle (UAV) group. It can formulate more accurate scanning strategies according to the characteristics of different grids, avoiding the blindness of traditional fixed path planning. The bubble marks provide precise guidance for the synchronous actions of the UAV group, greatly improving the collaborative efficiency of medium switching, reducing the synchronous error, thereby improving the accuracy and safety of mapping. This is particularly significant in complex water environments. The dynamic grid can be dynamically adjusted according to the real-time position of the UAV and environmental changes, enabling the UAV group to adapt to complex and changing environments in a timely manner, improving the flexibility and adaptability of mapping, achieving path optimization for the group collaborative land and sea mapping of the UAV group in complex coastal zones and waters, improving mapping efficiency, accuracy, and safety, and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of a mapping route optimization method based on UAV remote sensing according to the present invention; Figure 2 It is a schematic flow chart of realizing collaborative mapping of the UAV group according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To facilitate the understanding of the present invention, the present application will be described more comprehensively with reference to the relevant drawings; the preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0018] It should be noted that the terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0020] Example 1: Figure 1 It is a schematic flow chart of a mapping route optimization method based on UAV remote sensing according to an embodiment of the present invention, including: S101, collecting historical data, disassembling the historical data into components, calculating the component differences, generating a dynamic offset vector, calculating a stability index based on the dynamic offset vector, the wind speed time series, and the wind direction change rate, and generating a segmented path based on the stability index; Specifically, the collected historical data includes historical wind speed and historical wind direction data. Historical wind speed and wind direction records are obtained from the meteorological department, and the expected flight direction vector is obtained from the mapping task planning stage. The collected data is cleaned and transformed, and the historical wind speed and historical wind direction data are split into horizontal wind components and vertical wind components. The formulas for splitting the historical wind speed and historical wind direction data are: =U , where represents the horizontal wind component, U represents the historical wind speed data, represents the historical wind direction data; =U , where represents the vertical wind component, U represents the historical wind speed data, represents the historical wind direction data. The horizontal deviation and vertical deviation are calculated based on the horizontal wind component and vertical wind component. The horizontal deviation and vertical deviation respectively reflect the offset of the wind on the UAV flight in the horizontal and vertical directions. The formulas for calculating the horizontal deviation and vertical deviation are: , where represents the horizontal deviation, represents the horizontal wind component, represents the horizontal component of the expected flight direction vector; , where represents the vertical deviation, represents the vertical wind component, represents the vertical component of the expected flight direction vector. A dynamic offset vector is constructed based on the horizontal deviation and vertical deviation. The dynamic offset vector is used to quantify the total offset of the wind on the UAV flight path in the horizontal and vertical directions. The formula for the dynamic offset vector is: , where represents the dynamic offset vector, represents the horizontal deviation, represents the vertical deviation. According to the magnitude and direction of the dynamic offset vector, the flight path of the UAV can be adjusted to reduce the impact of the wind on the flight. During the flight, according to the real-time monitored wind parameters and the dynamic offset vector, the flight attitude and speed of the UAV are dynamically adjusted to ensure stable flight.

[0021] Wind field stability is an important process for evaluating the degree of change of the wind field at different time periods. The stability of the wind field is quantified by calculating the wind speed standard deviation, wind direction change rate, covariance, and stability index. The formula for the wind speed standard deviation is: , where represents the wind speed standard deviation, which measures the fluctuation degree of the wind speed, represents the average wind speed, Denote the wind speed at the $i$-th time point, $N$ represents the number of wind speed data, and the calculation formula for the wind direction change rate is: , where denotes the wind direction change rate, which measures the speed of wind direction change, denotes the change in wind direction, denotes the time interval, and the calculation formula for covariance is: , where denotes the covariance, which measures the correlation between the wind speed standard deviation and the wind direction change rate, denotes the wind speed standard deviation, denotes the average value of the wind speed standard deviation, denotes the wind direction change rate, denotes the average value of the wind direction change rate. Calculate the stability index based on the covariance, and the formula is: , where denotes the stability index, and its value range is between 0 and 1. The closer the value is to 1, the more stable the wind field is, denotes the covariance, denotes the wind speed standard deviation; denotes the wind direction change rate; Divide the time period according to the calculated stability index. When < 0.3, determine that this location or time period is a high-risk period. When 0.3 ≤ ≤ 0.7, determine it as a medium-risk period. When When it is > 0.7, it is determined as a low-risk period. During the process of dividing the periods, record the start and end positions or time points of each period type. For adjacent different risk periods, select the actual coordinates corresponding to the boundary position between them as the switching point. For example, when transitioning from a high-risk period to a medium-risk period, find the coordinates of the end position of the high-risk period and the start position of the medium-risk period, and take the boundary position as the switching point. The segmented path includes the high-risk period path, the medium-risk period path, and the low-risk period path. For the generation of the high-risk period path, according to the path segment or time period corresponding to the divided high-risk period, determine the start and end coordinates for generating the high-risk path to determine the path range, that is, the boundary coordinates of the high-risk period. Based on the original surveying and mapping path, add shelter points through the path planning algorithm A* algorithm. According to meteorological data and geographical information, select a shelter area as the shelter point. By calculating the path lengths before and after the detour, adjust the position and quantity of the shelter points to meet the detour distance requirement. Set the speed adjustment coefficient, and record the generated high-risk period path, including information such as the sequence of coordinate points on the path and the speed adjustment coefficient, as the segmented path P1; for the generation of the medium-risk period path, according to the boundary coordinates of the medium-risk period, determine the start and end positions of the medium-risk path. Based on the original surveying and mapping path, without changing the main path direction, add some small detours or bypasses to increase the redundant distance, which is achieved by adding some tiny path segments on the path. Set the speed adjustment coefficient, and record the medium-risk period path as the segmented path P2, including information such as path coordinates and the speed adjustment coefficient; for the generation of the low-risk period path, according to the boundary coordinates of the low-risk period, determine the start and end positions of the low-risk path, use the shortest path planning algorithm to directly generate the shortest path from the start coordinate to the end coordinate of the low-risk period, and maintain normal speed. Record the low-risk period path as the segmented path P3, including information such as path coordinates and the speed adjustment coefficient. Integrate the high-risk period path, the medium-risk period path, and the low-risk period path, and connect the generated segmented paths P1, P2, and P3 in sequence according to the switching point coordinates to form a complete path set , ensuring the continuity and smoothness of the path at the switching point, and avoiding path mutations or unreasonable turns.

[0022] S102, Obtain the actual captured images of the drone, generate a grid based on the actual captured images and the critical points of the segmented path, and adjust the path based on the grid; Furthermore, the real-shot images taken by the drone are obtained. The real-shot images contain RGB color information and depth information. The RGB information is used to identify ground features, and the depth information is used to obtain the elevation data of the terrain. The grid is divided with the critical point of the segmented path as the center. For example, if there is a critical point, the critical point is used as the geometric center and expanded 100m around to form a square grid with a side length of 200m. The terrain parameters in the grid are extracted, all elevation data points in the grid are collected, and the standard deviation of the elevation data is calculated. The standard deviation reflects the degree of discreteness of the elevation data. The larger the standard deviation, the more uneven the terrain undulation and the higher the complexity of the terrain. Multiple points are selected in the grid to calculate the slope. The formula is: ,in, is the slope, is the elevation change between adjacent points, is the change in the horizontal distance between adjacent points. The average of multiple slope values is taken to represent the slope of the grid. The larger the slope, the greater the inclination of the terrain, and the higher the challenges and risks faced by the drone when flying.

[0023] The wind speed data is monitored in real time in each grid, and the average wind speed of the current grid is calculated. The average wind speed reflects the overall strength of the wind in the grid. The force acting on the UAV flight is closely related to the average wind speed. The wind speed standard deviation is calculated to measure the degree of fluctuation of the wind speed in the grid. The larger the wind speed standard deviation, the more drastic the wind speed change, and the greater the impact on the stability of the UAV flight. The risk coefficient is calculated based on the average wind speed, wind speed standard deviation and elevation standard deviation. The formula is: ,in, represents the risk factor, Indicates the average wind speed of the current grid. Indicates the standard deviation of wind speed in the current grid, It represents the elevation standard deviation of the grid. The risk level of the grid is determined according to the calculated risk coefficient. If R>8, the grid is a high-risk grid (red grid). If 3≤R When R<8, the grid is a medium-risk grid (yellow grid); if R<3, the grid is a low-risk grid (green grid).

[0024] During the flight of the drone, the risk value of the current grid and other related terrain and wind parameters are obtained in real time, the flight path is dynamically adjusted, and the grid is detected. When a high-risk grid is detected, an avoidance point is added near the high-risk grid, and the flight path is updated based on the avoidance point. The original path A→B→C is adjusted to A→B1 (avoidance point)→C. At the same time, the adjusted path length is calculated. For example: when flying to grid B, its risk value RB = 9.2 is detected, which belongs to the high-risk area, and the terrain parameters of the grid are obtained at the same time. = 0.8, slope α = 15°. A new avoidance point B1 is added near grid B, and its coordinate offsets Δx = +150 m and Δy = -80 m are determined according to the actual situation. After updating the flight path, the path length increases from the original 1.0 km to 1.3 km. Adjust the flight altitude by increasing it by 20 m to avoid possible turbulence, improve flight safety, and reduce the flight speed from the original 8 m / s to 5 m / s to improve the flight stability of the drone in complex environments.

[0025] S103. Obtain real-time water area data, generate a water area grid based on the water area data, calculate the feasibility index of the grid, and plan the optimal water entry trajectory based on the feasibility index and the model. Specifically, the drone uses the satellite remote sensing receiving device it carries to obtain the water area map of the target sea area in real time, and extracts key data such as wave height and flow velocity from the water area map. At the same time, the drone queries the historical database, which records the energy consumption data of the drone when entering the water under different water areas. By comparing the current water area data with the historical data, the energy loss during the water entry process is evaluated. The water area data obtained by satellite remote sensing is fused with the historical energy loss data. The drone determines the sea surface range through the GPS positioning system. The drone divides the sea surface range and the grid size into dynamic grids using the grid generation algorithm Delaunay triangulation method to generate a grid map covering the entire area. Each grid has a unique identifier.

[0026] Calculate the feasibility index based on the collected wave height data, flow velocity data, and energy consumption data. The formula is: , where , and are weight coefficients, corresponding to the influence degrees of wave height, flow velocity, and energy loss respectively. The value range is between 0 and 1, and satisfies α + β + γ = 1. is the feasibility index. is the wave height data, representing the vertical height of the wave. is the flow velocity data, representing the horizontal velocity of the water flow. is the energy loss data, representing the energy consumed by the drone during the water entry process. Associate the calculated feasibility index with the water area grid to form a water area grid with a value attribute. According to Based on the value of FI, the risk levels of water area grids are divided. If FI≥0.7, it is a safe grid (green area), indicating that the water conditions of this water area grid are suitable for the drone to directly enter the water; if 0.4≤FI<0.7, it is an auxiliary required grid (yellow area), indicating that the water conditions of this water area grid require the drone to take auxiliary measures before it can enter the water; if FI<0.4, it is a prohibited grid (red area), indicating that the water conditions of this water area grid are too harsh and the drone should avoid entering the water in this area. The entry and exit water range is the range of safe grids and auxiliary grids.

[0027] Obtain data such as the mass, external dimensions, and surface roughness of the drone, as well as fluid environment data such as water density and gravitational acceleration. According to the classical rigid body dynamics model, input the mass, external dimensions, surface roughness of the drone, as well as water density and gravitational acceleration into the classical rigid body dynamics model. Construct a drone hydrodynamic model through the principles of the classical rigid body dynamics model. Set the entry angle and speed control curve according to the hydrodynamic model. For the entry angle, when the drone plans the entry trajectory, based on the principles of hydrodynamics, through a large number of theoretical calculations and simulation experiments, it is determined that the entry angle is constrained within the angle range of 34°≤θ≤36°. When the entry angle is greater than 36°, the contact area between the drone and the water surface will increase sharply instantaneously, and the impact force generated by the water surface on the drone will increase exponentially. Such an excessive impact force will not only cause serious damage to the drone's shell. On the contrary, if the entry angle is less than 34°, the drone will slide on the water surface for a certain distance like "water skiing" and it is difficult to smoothly enter the water. For the speed control curve, during the dive of the drone, the speed control follows the law of decreasing with the square of time, that is , where is the initial speed of the drone at the start moment of the dive ( ), k is a constant. When the drone enters the instant of entering the water, the speed remains at a relatively stable value, that is .

[0028] After the drone enters the water through the optimal path, it conducts mapping according to the set mapping path.

[0029] S104, obtain the optimal exit water trajectory based on the feasibility index, extract the exit water data according to the exit water trajectory, and convert the exit water data and the aerial data; Furthermore, the drone calculates the exit water time based on the obtained spectral characteristics and the performance of the drone. The spectral characteristics are obtained by the millimeter-wave radar carried by the drone. According to the calculated exit water time, the entry and exit water range, and the surface tension of the wing, the optimal exit water trajectory is formed. The surface tension of the wing is regulated by electro-wetting materials to increase the surface tension instantaneously at the moment of exiting the water to help the drone quickly break away from the water surface.

[0030] Extract the water outlet data according to the obtained optimal water outlet trajectory, and convert the coordinates of the water outlet data into the air coordinate system through the joint matrix. The joint matrix is as follows: , where and respectively represent the medium switching position compensation amounts. During the medium switching process when the UAV enters the air from water, due to the characteristics of water such as refraction and reflection, as well as the change in the UAV's own motion state, the data obtained by the sensor will have deviations. These two compensation amounts are determined through experiments and are used to correct the position errors during the coordinate conversion process. is the wave height data, representing the vertical height of the wave. is the flow velocity data, representing the horizontal velocity of the water flow. is used to scale the x-axis coordinate in the underwater coordinate system, adjust the underwater x-axis coordinate to match the air coordinate system, 1.18 is used to scale the y-axis coordinate in the underwater coordinate system, and the underwater y-axis coordinate is adjusted by this factor to adapt to the air coordinate system. 1 indicates that during the coordinate conversion process, the z-axis coordinate does not undergo scaling and translation operations. Through the joint matrix, the points in the underwater coordinate system are converted to the points in the air coordinate system to achieve the coordinate unification of water-air data.

[0031] S105. Integrate the optimal water inlet trajectory, the underwater mapping path, and the optimal water outlet trajectory into an optimized path.

[0032] Specifically, integrate the optimized paths of the three stages to form a continuous and complete cross-medium flight path. During the integration process, fully consider the connection and transition between each stage. For example, at the end of underwater mapping, ensure that the UAV can smoothly adjust its attitude and position to prepare for the water outlet stage. A transition area can be set, and within this area, adjust the UAV's speed, attitude, and data to make the conversion between different stages smoother and more efficient. Optimize the integrated path according to the global environmental information after water-air data fusion. Through the environmental information obtained by devices such as underwater sonar and aerial optical sensors, a more comprehensive and accurate environmental map can be obtained after data fusion processing. If a potential danger is found in a certain area, adjust the path to avoid this area.

[0033] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: By disassembling historical data and optimizing segmented paths, the influence of wind on the flight path of the drone is reduced, the mapping accuracy and mission success rate are improved. By adding wind shelter points and adjusting the flight speed, the flight deviation during high-risk periods is significantly reduced, and the accuracy of mapping data is improved. Based on real-time wind field and terrain data, risk grids are divided and paths are optimized to reduce the flight risk of the drone, improve flight stability and image acquisition quality. By dividing risk grids, more refined risk assessment can be carried out for different regions, so as to more accurately identify high-risk regions. According to the real-time risk assessment results, the flight path can be adjusted to avoid high-risk regions and reduce flight risk; Based on real-time water area data, the cross-media land-sea integrated mapping path is optimized, the risk during medium switching is reduced, the mapping accuracy and efficiency are improved, and seamless integration of water-air data is achieved. Through the feasibility index, more refined control of the medium switching process is realized, the risk is reduced, and the mapping accuracy and efficiency are improved through optimizing the mapping path.

[0034] Embodiment 2: In the above Embodiment 1, the path of a single drone is optimized. In this embodiment, a four-dimensional grid is constructed, the four-dimensional grid is associated with the position of the drone group, and dynamic adjustment is performed according to the position of the drone and the actual situation of the grid to divide dynamic grids. According to the grid division and bubble mark guidance, synchronous actions (such as entering the water or emerging from the water simultaneously) are performed at the critical points, and collaborative scanning is achieved through the grid-associated data, as Figure 2 shown.

[0035] S201, construct a four-dimensional grid according to historical data and elevation data; According to the requirements of the surveying and mapping task, clarify the geographical boundary of the task area, use Geographic Information System (GIS) software to divide the task area into grids according to the size of 200m×200m, assign a unique ID to each grid. The elevation data refers to the height information data of the task area in the vertical direction. Distinguish the medium types according to the obtained elevation data. According to geographical common sense and the requirements of the surveying and mapping task, set the elevation threshold range, which includes two boundary values, the lowest threshold of the range and the highest threshold of the range. Determine the area where the elevation data is greater than or equal to the highest threshold of the range as land, determine the area where the elevation data is between the lowest threshold and the highest threshold of the range as water area, determine the area where the elevation data is less than the lowest threshold and close to land as the coastal zone. Import the elevation data and grid data into the Geographic Information System software, use the spatial analysis function of the GIS software to overlay the elevation data with the grid, and determine the medium type of the grid according to the elevation distribution in each grid according to the set elevation threshold. For a grid, count the values of all elevation points inside it. If most of the elevation points are greater than or equal to the highest threshold of the range, then preliminarily determine this grid as land; if most of the elevation points are between the lowest threshold and the highest threshold of the range, then determine it as a water area; if the elevation points mainly meet the area where they are less than the lowest threshold and close to land, then determine it as the coastal zone. According to the calculated risk coefficient, use the database management system to create a four-dimensional grid database to store the ID, medium type, risk level, geographical location information, and relevant environmental data of each grid, forming a four-dimensional grid. The four dimensions include longitude, latitude, elevation, and time dimension, and the time dimension is reflected by the dynamic change of the risk level with the environment.

[0036] S202, obtain the position of the unmanned aerial vehicle group, associate the unmanned aerial vehicle position with the four-dimensional grid. The unmanned aerial vehicle group includes a pilot aircraft and slave aircrafts; Further, the drones in the drone group are divided into a leading drone and slave drones. Each drone is equipped with a high-precision positioning system, multiple sensors, and communication equipment. A bubble generation device is set in the leading drone. The bubble generation device includes an air supply pipeline, a porous pipe fitting, and an air pump system. The four-dimensional grid database is imported into the control system of each drone so that it can obtain grid information in real time. The leading drone is responsible for global path planning, bubble label generation and control, data fusion, etc.; the slave drones are responsible for performing mapping tasks under the guidance of the leading drone, and obtaining the position information (longitude, latitude, altitude) of each drone in real time through the high-precision positioning system and uploading it to the leading drone. The leading drone dynamically divides the grid according to the real-time position and distribution of the drone group and in combination with the four-dimensional grid. For example, when the drone group is concentrated in an area, the grid of this area can be refined to improve the grid resolution; when the drone group is scattered in different areas, the grid size can be appropriately enlarged to improve the calculation efficiency. The leading drone matches the real-time position of each drone with the dynamic grid and establishes an association relationship between the drone and the dynamic grid. For example, information such as the ID of the dynamic grid where each drone is located, the medium type in the grid, and the risk level is recorded. The leading drone updates the association information between the drone and the dynamic grid in real time and shares it with the slave drones. The slave drones adjust their own flight or navigation strategies according to this information to ensure the smooth progress of the mapping task.

[0037] S203, the leading drone generates a global path and bubble labels, and guides the slave drones according to the bubble labels to achieve the collaborative mapping path of the drone group; Specifically, according to the requirements of the surveying and mapping task, set task objectives in the control system of the leading aircraft, such as the surveying area range, accuracy requirements, etc. The leading aircraft combines the information in the dynamic grid database, considers factors such as medium type, risk level, and UAV position, and uses the path planning algorithm A* algorithm to generate a global path. It preferentially selects dynamic grids with a lower risk level and a suitable medium type as part of the path, and marks the positions of the medium switching points (entering water / exiting water). The UAV group flies according to the planned global path. When approaching the medium switching point, the leading aircraft uses sensors to continuously monitor the surrounding environmental information, including wave conditions (wave height, wave energy, etc.), water flow velocity, etc. Based on the information such as the medium type and risk indicators in the dynamic grid, as well as the real-time monitored environmental data, the leading aircraft determines the initial deployment position and time window of the bubble marker near the medium switching point. For example, at the gas-liquid grid junction and meeting specific wave conditions (wave height < 1.2 m and wave energy < 5 J / m²), it is determined to deploy the bubble marker near this position. The leading aircraft determines the water entry and exit times of different UAVs according to the actual situation of the dynamic grid and the position of the UAVs. For example, for UAVs close to the medium switching point, their water entry or exit operations are preferentially arranged; for UAVs at a greater distance, their water entry and exit times are arranged according to the global path and task progress. The leading aircraft sends water entry and exit commands to the slave aircraft through communication equipment, informing the slave aircraft of the specific time and operation requirements.

[0038] The leading aircraft determines the activation duration of the bubble marker according to the critical point type. When entering the water, T 入 = 1.2 × Tw, when exiting the water, T 出 = 0.8 × Tw, where Tw is the wave period. The leading aircraft obtains the value of Tw through the built-in wave period measuring device, and then calculates the activation duration. At the same time, it controls parameters such as the diameter and tilt angle of the bubble marker to adapt to different medium switching requirements. The leading aircraft starts the bubble generation device and generates bubble markers according to the set parameters. The leading aircraft sends guiding commands to the slave aircraft through communication equipment, informing the slave aircraft of the position, activation duration of the bubble marker, and the best path to enter the bubble marker area. The slave aircraft adjusts its flight or navigation attitude according to the guiding commands of the leading aircraft and gradually enters the bubble marker area. During the entry process, the slave aircraft maintains communication with the leading aircraft and real-time feedbacks its position and status information. After the bubble marker is deployed, it is affected by the vector superposition of the wind speed and water flow velocity, resulting in a drift displacement. The calculation formula for the drift distance is: )×t, where, represents the wind speed, with the direction based on true north and eastward being positive, represents the water flow velocity, with the direction based on true north and eastward being positive, Let \(\theta\) be the angle between the wind speed and the water flow speed, and \(t\) be the time interval from the deployment of the bubble marker to the arrival of the slave aircraft. To offset the drift error, the pilot aircraft adjusts the initial deployment position. The pilot aircraft deploys the bubble marker in the reverse drift direction of the initial deployment position so that the bubble marker reaches the target position after drifting. The calculation formula for the deployment position is: , where \(P\) is the deployment position, \(P_0\) is the initial deployment position, \(d\) is the drift distance, \(\vec{u}\) is the unit vector in the drift direction, which is determined by the vector sum of the wind speed and the water flow speed. , The slave aircraft detects the density distribution of the bubble marker through lidar, and selects the position with a large density as the water entry point. Under the guidance of the bubble marker, the unmanned aircraft group performs the mapping task according to the preset path. The slave aircraft uses the mapping equipment carried by itself to collect data, such as the water depth, topography and landforms of the water area, or the geological structure and surface features of the land. The slave aircraft transmits the collected data to the pilot aircraft in real time. The pilot aircraft performs preliminary processing and storage of the data, and dynamically adjusts the flight or navigation path of the unmanned aircraft group according to the data situation to ensure the comprehensiveness and accuracy of the mapping task.

[0039] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: By comprehensively considering space, medium type and risk indicators through the four-dimensional grid, more comprehensive and detailed information is provided for the path planning of the unmanned aircraft group, and more accurate scanning strategies can be formulated according to the characteristics of different grids, avoiding the blindness of traditional fixed path planning. The bubble marker provides accurate guidance for the synchronous actions of the unmanned aircraft group, greatly improving the coordination efficiency of medium switching and reducing the synchronous error, thereby improving the accuracy and safety of mapping, especially in complex water environments. The dynamic grid can be dynamically adjusted according to the real-time position of the unmanned aircraft and environmental changes, enabling the unmanned aircraft group to adapt to complex and changing environments in a timely manner, improving the flexibility and adaptability of mapping, realizing the path optimization of the unmanned aircraft group for group collaborative land and sea mapping in complex coastal zones and waters, improving mapping efficiency, accuracy and safety, and reducing energy consumption.

[0040] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing mapping routes based on drone remote sensing, characterized in that, Including: S101, collect historical data, disassemble the historical data into components, calculate the component differences, generate a dynamic offset vector, calculate a stability index based on the dynamic offset vector, the wind speed time series, and the wind direction change rate, and generate a segmented path based on the stability index; S102, obtain the actual captured images of the unmanned aerial vehicle (UAV), generate a grid based on the actual captured images and the critical points of the segmented path, and adjust the path based on the grid; S103, obtain real-time water area data, generate a water area grid based on the water area data, calculate the feasibility index of the grid, and plan the optimal water entry trajectory based on the feasibility index and the model; S104, obtain the optimal water exit trajectory based on the feasibility index, extract the water exit data according to the water exit trajectory, and convert the water exit data and the aerial data; S105, integrate the optimal water entry trajectory, the underwater survey path, and the optimal water exit trajectory into an optimized path.

2. The method for optimizing a mapping route based on drone remote sensing according to claim 1, wherein The formula for calculating the stability index is as follows: , where represents the stability index, and its value range is between 0 and 1. , represents the standard deviation of wind speed; represents the wind direction change rate; represents the covariance.

3. The optimized mapping route method based on UAV remote sensing according to claim 1, characterized in that The segmented path includes a high-risk period path, a medium-risk period path, and a low-risk period path.

4. A mapping route optimization method based on UAV remote sensing according to claim 1, characterized in that Adjust the path based on the grid, detect the grid, when a high-risk grid is detected, add an avoidance point at the high-risk grid, and update the flight path based on the avoidance point.

5. The surveying and mapping route optimization method based on UAV remote sensing according to claim 1, characterized in that The formula for calculating the feasibility index is as follows: , where , and are weight coefficients, corresponding to the influence degrees of wave height, flow velocity, and energy loss respectively. The value range is between 0 and 1, and it satisfies α + β + γ = 1. is the feasibility index, is the wave height data, representing the vertical height of the wave. is the flow velocity data, representing the horizontal velocity of the water flow. is the energy loss data, representing the energy consumed by the drone during the water entry process.

6. A method for optimizing a surveying route based on UAV remote sensing according to claim 1, characterized in that S201, construct a four-dimensional grid according to historical data and elevation data; S202, obtain the positions of the UAV group, associate the UAV positions with the four-dimensional grid, and the UAV group includes a pilot UAV and slave UAVs; S203, the pilot UAV generates a global path and a bubble label, and guides the slave UAVs according to the bubble label to realize the cooperative surveying path of the UAV group.

7. The optimized mapping route method based on UAV remote sensing according to claim 6, wherein, The elevation data refers to the height information data of the mission area in the vertical direction. The medium types are distinguished according to the obtained elevation data, and the medium types include land, water area, and coastal zone.

8. The optimized mapping route method based on UAV remote sensing according to claim 6, characterized in that The leading aircraft determines the activation duration of the bubble marker. When entering the water, T 入 = 1.2 × Tw. When leaving the water, T 出 = 0.8 × Tw, where Tw is the wave period. The leading aircraft obtains the value of Tw through the built-in wave period measuring device and then calculates the activation duration.

9. The method for optimizing a mapping route based on UAV remote sensing according to claim 8, wherein, After deployment, the bubble marker is affected by wind speed and water flow speed, resulting in a drift displacement. The calculation formula for the drift distance is: ) × t, where represents the wind speed, represents the water flow speed, is the included angle between the wind speed and the water flow speed, and t is the time interval from the deployment of the bubble marker to the arrival of the slave device.

10. A mapping route optimization method based on UAV remote sensing according to claim 9, characterized in that, The calculation formula for the deployment position of the bubble marker is: , where is the deployment position, is the initial deployment position, is the drift distance, is the unit vector of the drift direction, determined by the vector sum of the wind speed and the water flow speed.

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