A surveying and mapping route optimization method based on UAV remote sensing

Abstract

The present application discloses a surveying and mapping route optimization method based on unmanned aerial vehicle (UAV) remote sensing, comprising: collecting historical data, decomposing the historical data into components, calculating component differences, generating a dynamic offset vector, calculating a stability index based on the dynamic offset vector, a wind speed time series, and a wind direction change rate, and generating a segmented path; obtaining real-time images of the UAV, generating a grid based on the real-time images and critical points of the segmented path, and adjusting the path; obtaining real-time water area data to generate a water area grid, calculating a feasibility index of the grid, and planning an optimal water entry trajectory based on the feasibility index and a model; deriving an optimal water exit trajectory based on the feasibility index, extracting water exit data based on the water exit trajectory, and converting the water exit data with aerial data; integrating the optimal water entry trajectory, the underwater surveying and mapping path, and the optimal water exit trajectory into an optimized path, thereby reducing the impact of wind on the UAV flight path and improving surveying and mapping accuracy and mission success rate.

Description

Technical Field

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

[0002] With the rapid development of drone technology, drones are increasingly being used in surveying and mapping. Drone remote sensing mapping, with its efficiency, flexibility, and low cost, can rapidly acquire surface information over large areas, providing crucial data support for a wide range of fields, including urban planning, resource exploration, and environmental monitoring. However, actual drone mapping missions face numerous complex environmental factors and challenges, significantly impacting both accuracy and efficiency.

[0003] Traditional surveying and mapping route planning methods often ignore the dynamic changes in wind fields and adopt fixed flight paths. This makes drones susceptible to wind influences during flight and unable to fly along the expected path, thus reducing surveying and mapping accuracy. Therefore, how to optimize the surveying and mapping route based on changes in wind fields and reduce the impact of wind on the drone's flight path has become a key issue in improving surveying and mapping accuracy. Topographic factors also have a significant impact on drone surveying and mapping. Complex terrain can cause unstable drone flight and increase flight risks. For example, in areas with large terrain fluctuations, such as mountainous areas and hills, drones may encounter problems such as turbulent airflow and obstructed vision during flight, affecting the quality of image acquisition. When dealing with complex terrain, existing surveying and mapping methods lack real-time monitoring of terrain parameters and dynamic path adjustment mechanisms. They are unable to adjust the flight path in a timely manner according to the actual terrain conditions, thereby increasing flight risks and reducing surveying and mapping efficiency. Summary of the Invention

[0004] This application provides a surveying and mapping route optimization method based on UAV remote sensing. By disassembling historical data and optimizing the segmented path, the impact of wind on the UAV flight path is reduced, the surveying and 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 the surveying and mapping data is improved.

[0005] This application provides a surveying and mapping route optimization method based on UAV remote sensing, including:

[0006] S101, collecting historical data, breaking the historical data into components, calculating 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 a segmented path based on the stability index;

[0007] S102, obtaining a real-shot image of the drone, generating a grid based on the real-shot image and critical points of the segmented path, and adjusting the path based on the grid;

[0008] S103, acquiring real-time water area data, generating a water area grid based on the water area data, calculating a feasibility index of the grid, and planning an optimal water entry trajectory based on the feasibility index and the model;

[0009] S104, determining an optimal water discharge trajectory based on the feasibility index, extracting water discharge data according to the water discharge trajectory, and converting the water discharge data into aerial data;

[0010] S105 , integrating the optimal water entry trajectory, the underwater mapping path, and the optimal water exit trajectory into an optimized path.

[0011] Preferably, the formula for calculating the stability index is: ,in, Represents the stability index, with a value range from 0 to 1. , represents the standard deviation of wind speed; Indicates the rate of change of wind direction; represents the covariance.

[0012] Preferably, the segmented path includes a high-risk period path, a medium-risk period path, and a low-risk period path.

[0013] Preferably, the path is adjusted based on the grid, the grid is detected, and when a high-risk grid is detected, an avoidance point is added at the high-risk grid, and the flight path is updated based on the avoidance point.

[0014] Preferably, the formula for calculating the feasibility index is: ,in, 、 and are weight coefficients, corresponding to the influence of wave height, flow velocity and energy loss respectively, with a value range of 0 to 1 and satisfying α+β+γ=1. is the feasibility index, is the wave height data, indicating the vertical height of the wave, is the flow velocity data, indicating the horizontal velocity of the water flow, is the energy loss data, which indicates the energy consumed by the drone during the water entry process.

[0015] Preferably,

[0016] S201, constructing a four-dimensional grid based on historical data and elevation data;

[0017] S202, obtaining the position of the drone group and associating the drone position with the four-dimensional grid. The drone group includes a pilot and a slave.

[0018] In step S203 , the leader aircraft generates a global path and bubble markers, and guides the slave aircraft according to the bubble markers to achieve a collaborative mapping path for the UAV group.

[0019] Preferably, the elevation data refers to the height information data of the mission area in the vertical direction, and the medium type is distinguished according to the acquired elevation data, and the medium type includes land, water and coastal zone.

[0020] Preferably, the pilot aircraft determines the activation time of the bubble mark. When entering the water, Tin = 1.2× Tw, and when exiting the water, Tout = 0.8 × Tw, where Tw is the wave period. The pilot aircraft obtains the value of Tw through the built-in wave period measuring device and then calculates the activation time.

[0021] Preferably, after deployment, the bubble marker is affected by wind speed and water flow speed, resulting in drift displacement. The calculation formula for the drift distance is: )×t, where Indicates wind speed, Indicates the water flow velocity, is the angle between wind speed and water flow speed, and t is the time interval from the deployment of the bubble marker to the arrival of the slave aircraft.

[0022] Preferably, the calculation formula for the bubble marker deployment position is: ,in, For deployment location, 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 wind speed and water velocity.

[0023] 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 impact of wind on the UAV flight path is reduced, and the surveying and mapping accuracy and mission success rate are improved. By increasing shelter points and adjusting the flight speed, the flight deviation during high-risk periods is significantly reduced, and the accuracy of the surveying and mapping data is improved. Based on real-time wind field and terrain data, the risk grid is divided and the path is optimized to reduce the UAV flight risk, improve flight stability and image acquisition quality. By dividing the risk grid, a more refined risk assessment can be performed on different areas, so that high-risk areas can be identified more accurately. According to the real-time risk assessment results, the flight path can be adjusted to avoid high-risk areas and reduce flight risks. Based on real-time water area data, the cross-media land and sea integrated surveying and mapping path is optimized to reduce the risk during medium switching, improve surveying and mapping accuracy and efficiency, and realize seamless fusion of water and air data. Through the feasibility index, more refined control of the medium switching process is achieved, reducing risks. By optimizing the surveying and mapping path, the surveying and mapping accuracy and efficiency are improved.

[0024] The four-dimensional grid comprehensively considers space, medium type and risk indicators, providing more comprehensive and detailed information for the path planning of the drone 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 mark provides precise guidance for the synchronous action of the drone group, greatly improving the collaborative efficiency of medium switching, reducing synchronization errors, and thus improving the accuracy and safety of surveying and mapping, especially in complex water environments. The dynamic grid can be dynamically adjusted according to the real-time position of the drone and environmental changes, so that the drone group can adapt to the complex and changing environment in time, improve the flexibility and adaptability of surveying and mapping, and realize the path optimization of the drone group's collaborative sea and land surveying in complex coastal areas and waters, improve surveying and mapping efficiency, accuracy and safety, and reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a surveying and mapping route optimization method based on UAV remote sensing according to the present invention;

[0026] Figure 2 The figure is a flow chart of the collaborative mapping process of drone groups implemented in the present invention. DETAILED DESCRIPTION

[0027] To facilitate understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0028] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are for illustrative purposes only and do not represent the only implementation method.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only 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 associated listed items.

[0030] Example 1: Figure 1 1 is a flow chart of a surveying and mapping route optimization method based on UAV remote sensing according to an embodiment of the present invention, comprising:

[0031] S101, collecting historical data, breaking the historical data into components, calculating 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 a segmented path based on the stability index;

[0032] Specifically, the collected historical data includes historical wind speed and wind direction data. The historical wind speed and wind direction records are obtained from the meteorological department, and the expected flight direction vector is obtained from the surveying and mapping mission planning stage. The collected data is cleaned and converted, and the historical wind speed and wind direction data are split into horizontal wind components and vertical wind components. The formula for splitting the historical wind speed and wind direction data is: =U ,in, represents the horizontal wind component, U represents the historical wind speed data, Represents historical wind direction data; =U ,in, represents the vertical wind component, U represents the historical wind speed data, Represents historical wind direction data. Calculates horizontal and vertical deviations based on the horizontal and vertical wind components. Horizontal and vertical deviations reflect the offsets caused by the wind on the drone's flight in the horizontal and vertical directions, respectively. The formulas for calculating horizontal and vertical deviations are: ,in, Indicates the horizontal deviation, represents the horizontal wind component, represents the horizontal component of the intended flight direction vector; ,in, Indicates 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 caused by the wind on the UAV flight path in the horizontal and vertical directions. The calculation formula of the dynamic offset vector is: ,in, represents the dynamic offset vector, Indicates the horizontal deviation, It represents the vertical deviation. According to the size and direction of the dynamic offset vector, the flight path of the UAV can be adjusted to reduce the impact of wind on the flight. During the flight, the flight attitude and speed of the UAV are dynamically adjusted according to the real-time monitored wind parameters and dynamic offset vector to ensure stable flight.

[0033] Wind farm stability is an important process for evaluating the degree of change of the wind farm over different time periods. The stability of the wind farm is quantified by calculating the wind speed standard deviation, wind direction change rate, covariance, and stability index. The calculation formula for wind speed standard deviation is: ,in, Represents the standard deviation of wind speed, which measures the degree of fluctuation of wind speed. represents the average wind speed, represents 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: ,in, Indicates the wind direction change rate, which measures how fast the wind direction changes. Indicates the change in wind direction, represents the time interval, and the covariance is calculated as: ,in, represents the covariance, which measures the correlation between the standard deviation of wind speed and the rate of change of wind direction, represents the standard deviation of wind speed, represents the average value of the standard deviation of wind speed, represents the wind direction change rate, Represents the average value of the wind direction change rate. The stability index is calculated based on the covariance. The formula is: ,in, It represents the stability index, and its value range is between 0 and 1. The closer the value is to 1, the more stable the wind farm is. represents the covariance, represents the standard deviation of wind speed; Indicates the wind direction change rate; the time period is divided according to the calculated stability index. <0.3, the location or time period is determined to be a high-risk period. When ≤0.7, it is determined to be a medium-risk period. When the risk is >0.7, it is determined to be a low-risk period. In the process of dividing the period, the starting and ending positions or time points of each period type are recorded. For adjacent different risk periods, the actual coordinates corresponding to the boundary positions between them are selected as switching points. For example, when transitioning from a high-risk period to a medium-risk period, the coordinates of the end position of the high-risk period and the starting position of the medium-risk period are found, and the boundary position is taken as the switching point. The segmented path includes a high-risk period path, a medium-risk period path, and a low-risk period path. For the generation of the high-risk period path, the starting and ending coordinates of the generated high-risk path are determined according to the path segments or time periods corresponding to the divided high-risk periods to determine the path range, that is, the boundary coordinates of the high-risk period. On the basis of the original surveying and mapping path, the path planning algorithm A* algorithm is used to increase shelter points. According to meteorological data and geographic information, shelter areas are selected as shelter points. By calculating the path length before and after the detour, the position and number of shelter points are adjusted to meet the detour distance requirements. The speed adjustment coefficient is set to generate the high-risk period path. Including the coordinate point sequence, speed adjustment coefficient and other information on the path, recorded as the segmented path P1; for the generation of the medium-risk period path, the starting and ending positions of the medium-risk path are determined according to the boundary coordinates of the medium-risk period. Based on the original surveying path, some small detours or detours are added without changing the direction of the main path to increase the redundant distance. This is achieved by adding some tiny path segments to the path, setting the speed adjustment coefficient, and recording the medium-risk period path as the segmented path P2, which includes information such as path coordinates and speed adjustment coefficient; for the generation of the low-risk period path, the starting and ending positions of the low-risk path are determined according to the boundary coordinates of the low-risk period. Using the shortest path planning algorithm, the shortest path from the starting coordinates to the ending coordinates of the low-risk period is directly generated, maintaining normal speed, and recording the low-risk period path as the segmented path P3, which includes information such as path coordinates and speed adjustment coefficient. The high-risk period path, medium-risk period path and low-risk period path are integrated, and the generated segmented paths P1, P2, and P3 are connected in sequence according to the order of the switching point coordinates to form a complete path set. , ensuring the continuity and smoothness of the path at the switching point, avoiding path mutations or unreasonable turns.

[0034] S102, obtaining a real-shot image of the drone, generating a grid based on the real-shot image and critical points of the segmented path, and adjusting the path based on the grid;

[0035] Furthermore, real-time drone images are obtained. The real-time 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 100 meters in all directions to form a square grid with a side length of 200 meters. The terrain parameters within the grid are extracted, and all elevation data points within the grid are collected. The standard deviation of the elevation data is calculated. The standard deviation reflects the degree of dispersion of the elevation data. The larger the standard deviation, the more uneven the terrain undulation and the higher the terrain complexity. Multiple points are selected within the grid to calculate the slope. The formula is: ,in, is the slope, is the elevation change between adjacent points, The slope value is the change in the horizontal distance between adjacent points. The average value of multiple slope values ​​is taken to represent the slope of the grid. The larger the slope, the greater the terrain inclination, and the higher the challenges and risks faced by drones during flight.

[0036] The wind speed data is monitored in real time within each grid, and the average wind speed of the current grid is calculated. The average wind speed reflects the overall strength of the wind within the grid. The force acting on the drone 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 within 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 drone 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 current grid average wind speed, 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 based on 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).

[0037] During the flight of the drone, the risk value of the current grid and other relevant 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. The flight path is updated based on the avoidance point, and the original path A→B→C is adjusted to A→B1 (avoidance point)→C. At the same time, the length of the adjusted path 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°, add a new avoidance point B1 near grid B, and determine its coordinate offset Δx = +150m, Δy = -80m according to actual conditions. After updating the flight path, the path length increases from the original 1.0km to 1.3km, and the flight altitude is adjusted by 20m to avoid possible turbulence and improve flight safety. The flight speed is reduced from the original 8m / s to 5m / s to improve the flight stability of the UAV in complex environments.

[0038] S103, acquiring real-time water area data, generating a water area grid based on the water area data, calculating a feasibility index of the grid, and planning an optimal water entry trajectory based on the feasibility index and the model;

[0039] Specifically, the drone uses the satellite remote sensing receiving equipment on board to obtain a real-time water map of the target sea area, and extracts key data such as wave height and flow rate from the water map. At the same time, the drone queries a historical database that records the energy consumption data of the drone when entering the water in different waters. By comparing the current water data with the historical data, the drone evaluates the energy loss during the entry process, and integrates the water data obtained by satellite remote sensing with the historical energy loss data. The drone determines the sea surface range through the GPS positioning system, and divides the sea surface range and 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.

[0040] The feasibility index is calculated based on the collected wave height data, flow velocity data, and energy consumption data. The formula is: ,in, 、 and are weight coefficients, corresponding to the influence of wave height, flow velocity and energy loss respectively, with a value range of 0 to 1 and satisfying α+β+γ=1. is the feasibility index, is the wave height data, indicating the vertical height of the wave, is the flow velocity data, indicating the horizontal velocity of the water flow, The energy loss data represents the energy consumed by the UAV during the water entry process. The feasibility index is calculated and associated with the water grid to form a The water grid of the value attribute, according to The water grids are divided into risk levels based on the size of the value. If FI ≥ 0.7, it is a safe grid (green area), indicating that the water conditions of the water grid are suitable for drones to enter the water directly; 0.4 ≤ FI < 0.7 is an auxiliary grid (yellow area), indicating that the water conditions of the water grid require the drone to take auxiliary measures before entering the water; FI < 0.4 is a prohibited grid (red area), indicating that the water conditions of the water grid are too severe and drones should avoid entering the water in this area. The entry and exit range of the water is within the range of the safe grid and auxiliary grid.

[0041] Data such as the drone's mass, dimensions, and surface roughness, as well as fluid environment data such as water density and gravitational acceleration, are obtained. Based on the classical rigid-body dynamics model, these data are input into the model. A fluid dynamics model for the drone is constructed using the principles of the classical rigid-body dynamics model. The entry angle and velocity control curve are then set based on the model. Regarding the entry angle, when planning the drone's trajectory, based on the principles of fluid dynamics, after extensive theoretical calculations and simulation experiments, the entry angle is constrained to be within the range of 34° ≤ θ ≤ 36°. When the entry angle is greater than 36°, the contact area between the drone and the water surface increases dramatically, and the impact force exerted on the drone by the water increases exponentially. This excessive impact force not only causes serious damage to the drone's exterior, but also, if the entry angle is less than 34°, the drone will glide on the water surface for a distance, like a water ski, making it difficult to successfully enter the water. Regarding the velocity control curve, during the dive, the velocity control follows the square law of decreasing velocity over time, i.e., ,in, The moment when the drone starts to dive ( ), k is a constant. When the UAV enters the water, the speed remains at a relatively stable value, that is, .

[0042] After the drone enters the water through the optimal path, it performs mapping according to the set mapping path.

[0043] S104, determining an optimal water discharge trajectory based on the feasibility index, extracting water discharge data according to the water discharge trajectory, and converting the water discharge data into aerial data;

[0044] Furthermore, the drone calculates the time to exit the water based on the acquired spectral characteristics and drone performance. The spectral characteristics are obtained using the drone's own millimeter-wave radar. The optimal water exit trajectory is formed based on the calculated water exit time, water entry and exit range, and wing surface tension. The wing surface tension is regulated using electrowetting materials, which increases the surface tension at the moment of exiting the water, helping the drone to quickly leave the water.

[0045] The water discharge data is extracted according to the obtained optimal water discharge trajectory, and the coordinates of the water discharge data are converted into the aerial coordinate system through the joint matrix. The joint matrix is: ,in, and They represent the medium switching position compensation. During the medium switching process when the drone enters the air from the water, the refraction and reflection characteristics of the water and the change of the drone's own motion state will cause deviations in the data obtained by the sensor. These two compensation amounts are determined through experiments to correct the position error in the coordinate conversion process. is the wave height data, indicating the vertical height of the wave, is the flow velocity data, indicating the horizontal velocity of the water flow, It is used to scale the x-axis coordinate in the underwater coordinate system and adjust the underwater x-axis coordinate to match the aerial coordinate system. 1.18 is used to scale the y-axis coordinate in the underwater coordinate system and adjust the underwater y-axis coordinate by this factor to adapt to the aerial coordinate system. 1 means that during the coordinate conversion process, the z-axis coordinate is not scaled or translated. Through the joint matrix, the points in the underwater coordinate system are converted to the points in the aerial coordinate system to achieve coordinate unification of the water and air data.

[0046] S105 , integrating the optimal water entry trajectory, the underwater mapping path, and the optimal water exit trajectory into an optimized path.

[0047] Specifically, the optimized paths of the three phases are integrated to form a continuous, complete cross-medium flight path. During the integration process, full consideration is given to the connection and transition between the various phases. For example, at the end of underwater mapping, it is necessary to ensure that the drone can smoothly adjust its attitude and position to prepare for the out-of-water phase. By setting up transition zones, the drone's speed, attitude, and data can be adjusted within these zones to make the transition between different phases smoother and more efficient. The integrated path is optimized based on the global environmental information obtained through the fusion of water and air data. Environmental information obtained through equipment such as underwater sonar and aerial optical sensors can be processed through data fusion to obtain a more comprehensive and accurate environmental map. If a potential danger is found in a certain area, the path is adjusted to avoid it.

[0048] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: by decomposing historical data and optimizing the segmented path, the impact of wind on the UAV flight path is reduced, and the surveying and mapping accuracy and mission success rate are improved. By increasing shelter points and adjusting the flight speed, the flight deviation during high-risk periods is significantly reduced, and the accuracy of the surveying and mapping data is improved. Based on real-time wind field and terrain data, the risk grid is divided and the path is optimized to reduce the flight risk of the UAV, improve flight stability and image acquisition quality. By dividing the risk grid, a more refined risk assessment can be performed on different areas, so as to more accurately identify high-risk areas. According to the real-time risk assessment results, the flight path can be adjusted to avoid high-risk areas and reduce flight risks. Based on real-time water area data, the cross-media sea and land integrated surveying and mapping path is optimized to reduce the risk during medium switching, improve surveying and mapping accuracy and efficiency, and realize seamless integration of water and air data. Through the feasibility index, more refined control of the medium switching process is achieved, reducing risks. By optimizing the surveying and mapping path, the surveying and mapping accuracy and efficiency are improved.

[0049] Example 2: The above example optimizes the path of a single drone. This example constructs a four-dimensional grid, associates the four-dimensional grid with the position of the drone group, dynamically adjusts the grid according to the actual situation of the drone position and the grid, divides the dynamic grid, and performs synchronous actions at critical points (such as entering or exiting the water at the same time) based on the grid division and bubble mark guidance, and realizes collaborative scanning through grid association data, such as Figure 2 shown.

[0050] S201, constructing a four-dimensional grid based on historical data and elevation data;

[0051] According to the requirements of the surveying and mapping task, the geographical boundaries of the task area are clarified, and the task area is divided into 200m×200m sizes using geographic information system (GIS) software to form a grid. A unique ID is assigned to each grid. The elevation data refers to the vertical height information data of the task area. The medium type is distinguished according to the acquired elevation data. The elevation threshold range is set according to geographical common sense and the requirements of the surveying and mapping task. The elevation threshold range contains two boundary values, the minimum threshold and the maximum threshold. The area with elevation data greater than or equal to the maximum threshold is determined as land, the area with elevation data between the minimum threshold and the maximum threshold is determined as water, and the area with elevation data less than the minimum threshold and close to the land is determined as coastal zone. The elevation data and grid data are imported into the geographic information system software and GIS is used to calculate the elevation data. The spatial analysis function of the software superimposes the elevation data with the grid, and determines the medium type of the grid according to the set elevation threshold based on the elevation distribution within each grid. For a grid, the values ​​of all the elevation points within it are counted. If most of the elevation points are greater than or equal to the highest threshold of the range, the grid is preliminarily judged to be land; if most of the elevation points are between the lowest threshold and the highest threshold of the range, it is judged to be a water area; if the elevation points mainly meet the area less than the lowest threshold of the range and close to the land, it is judged to be a coastal zone. Based on the calculated risk coefficient, a four-dimensional grid database is created using the database management system to store the ID, medium type, risk level, geographic location information, and related environmental data of each grid to form a four-dimensional grid. The four dimensions include longitude, latitude, elevation, and time dimensions. The time dimension is reflected by the dynamic changes of risk level with the environment.

[0052] S202, obtaining the position of the drone group and associating the drone position with the four-dimensional grid. The drone group includes a pilot and a slave.

[0053] Furthermore, the drones in the drone group are divided into pilot and slave, and each drone is equipped with a high-precision positioning system, multiple sensors and communication equipment. A bubble generating device is set in the pilot, and the bubble generating device includes an air supply pipeline, a porous pipe 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 pilot is responsible for global path planning, bubble mark generation and control, data fusion, etc. The slave is responsible for carrying out surveying and mapping tasks under the guidance of the pilot, and obtains the position information (longitude, latitude, elevation) of each drone in real time through the high-precision positioning system, and uploads it to the pilot. The pilot calculates the position information (longitude, latitude, elevation) of each drone according to the real-time position and distribution of the drone group. Based on the situation, the grid is dynamically divided in combination with the four-dimensional grid. For example, when the drone group is concentrated in one area, the grid in 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 expanded to improve the computing efficiency. The pilot aircraft matches the real-time position of each drone with the dynamic grid and establishes an association between the drone and the dynamic grid. For example, the dynamic grid ID of each drone, the medium type in the grid, the risk level and other information are recorded. The pilot aircraft updates the association information between the drone and the dynamic grid in real time and shares it with the slave aircraft. The slave aircraft adjusts its own flight or navigation strategy based on this information to ensure the smooth progress of the surveying and mapping mission.

[0054] S203: The leader aircraft generates a global path and bubble markers, and guides the slave aircraft according to the bubble markers to achieve a collaborative mapping path for the UAV group.

[0055] Specifically, according to the requirements of the surveying and mapping mission, the mission objectives are set in the control system of the pilot aircraft, such as the surveying area range and accuracy requirements. The pilot aircraft combines the information in the dynamic grid database, considers factors such as medium type, risk level, and drone position, and uses the path planning algorithm A* algorithm to generate a global path. Dynamic grids with lower risk levels and suitable medium types are preferentially selected as part of the path, and the position of the medium switching point (into / out of the water) is marked. The drone group flies according to the planned global path. When approaching the medium switching point, the pilot aircraft monitors the surrounding environment information in real time through sensors, including wave conditions (wave height, wave energy, etc.), water flow speed, etc. Based on information such as medium type and risk indicators in the dynamic grid, as well as real-time monitored environmental data, the pilot aircraft determines the initial deployment position and time window of the bubble marker near the medium switching point. For example, at the junction of the gas-liquid grid and meeting specific wave conditions (wave height <1.2m and wave energy <5 J / m²), the pilot aircraft determines the deployment of bubble markers near that location. The pilot aircraft determines the entry and exit timing of different drones based on the actual situation of the dynamic grid and the positions of the drones. For example, drones near the medium switching point are given priority for entry or exit operations. For drones farther away, their entry and exit times are arranged based on the global path and mission progress. The pilot aircraft sends entry and exit commands to the slave aircraft through communication equipment, informing the slave aircraft of the specific timing and operation requirements.

[0056] The pilot determines the activation time of the bubble mark according to the critical point type. When entering the water, T 入 = 1.2× Tw, when water is discharged, T 出 = 0.8 × Tw, where Tw is the wave period. The pilot aircraft obtains the value of Tw through the built-in wave period measurement device and then calculates the activation time. At the same time, it controls the diameter, tilt angle and other parameters of the bubble marker to adapt to different media switching requirements. The pilot aircraft starts the bubble generation device and generates a bubble marker according to the set parameters. The pilot aircraft sends guidance instructions to the slave aircraft through the communication equipment, informing the slave aircraft of the position of the bubble marker, the activation time and the best path to enter the bubble marker area. The slave aircraft adjusts its own flight or navigation attitude according to the guidance instructions of the pilot aircraft and gradually enters the bubble marker area. During the entry process, the slave aircraft maintains communication with the pilot aircraft and provides real-time feedback of its own position and status information. After deployment, the bubble marker is affected by the vector superposition of wind speed and water speed, resulting in drift displacement. The calculation formula for the drift distance is: )×t, where Indicates wind speed, direction is based on true north, east is positive, Indicates the water flow speed, with the direction based on true north and east as positive. is the angle between wind speed and water flow speed, t is the time interval from the deployment of the bubble marker to the arrival of the slave aircraft. In order 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 of the deployment position is: ,in, For deployment location, is the initial deployment location, is the drift distance, is the unit vector of the drift direction, which is determined by the vector sum of wind speed and water velocity, The slave aircraft detects the density distribution of bubble markers through lidar and selects the location with high density as the entry point. Under the guidance of the bubble markers, the drone group performs surveying and mapping tasks according to the preset path. The slave aircraft uses the surveying and mapping equipment it carries to collect data, such as water depth, topography and other information of the water area, or geological structure, surface features and other data 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 drone group according to the data situation to ensure the comprehensiveness and accuracy of the surveying and mapping tasks.

[0057] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: the four-dimensional grid comprehensively considers the space, medium type and risk indicators, provides more comprehensive and detailed information for the path planning of the drone group, and can formulate more accurate scanning strategies according to the characteristics of different grids, avoiding the blindness of traditional fixed path planning. The bubble mark provides precise guidance for the synchronous action of the drone group, greatly improves the collaborative efficiency of medium switching, reduces synchronization errors, and thus improves the accuracy and safety of surveying and mapping, especially in complex water environments. The dynamic grid can be dynamically adjusted according to the real-time position of the drone and environmental changes, so that the drone group can adapt to the complex and changing environment in time, improve the flexibility and adaptability of surveying and mapping, and realize the path optimization of group collaborative sea and land surveying of the drone group in complex coastal areas and waters, improve surveying and mapping efficiency, accuracy and safety, and reduce energy consumption.

[0058] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A surveying and mapping route optimization method based on UAV remote sensing, characterized in that: include: S101, collecting historical data, breaking the historical data into components, calculating 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 a segmented path based on the stability index; S102, obtaining a real-shot image of the drone, generating a grid based on the real-shot image and critical points of the segmented path, and adjusting the path based on the grid; S103, acquiring real-time water area data, generating a water area grid based on the water area data, calculating a feasibility index of the grid, and planning an optimal water entry trajectory based on the feasibility index and the model; The formula for calculating the feasibility index is: ,in, 、 and are weight coefficients, corresponding to the influence of wave height, flow velocity and energy loss respectively, with a value range of 0 to 1 and satisfying α+β+γ=1. is the feasibility index, is the wave height data, indicating the vertical height of the wave, Flow velocity data, indicating the horizontal velocity of water flow, is the energy loss data, which indicates the energy consumed by the drone during the water entry process; S104, determining an optimal water discharge trajectory based on the feasibility index, extracting water discharge data according to the water discharge trajectory, and converting the water discharge data into aerial data; S105 , integrating the optimal water entry trajectory, the underwater mapping path, and the optimal water exit trajectory into an optimized path.

2. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 1, wherein: The formula for calculating the stability index is: ,in, Represents the stability index, with a value range from 0 to 1. , represents the covariance, represents the standard deviation of wind speed, Indicates the rate of change of wind direction.

3. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 1, wherein: The segmented paths include high-risk period paths, medium-risk period paths, and low-risk period paths.

4. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 1, wherein: The path is adjusted based on the grid, and the grid is detected. When a high-risk grid is detected, an avoidance point is added at the high-risk grid, and the flight path is updated based on the avoidance point.

5. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 1, wherein: S201, constructing a four-dimensional grid based on historical data and elevation data; S202, obtaining the position of the drone group and associating the drone position with the four-dimensional grid. The drone group includes a pilot and a slave. In step S203 , the leader aircraft generates a global path and bubble markers, and guides the slave aircraft according to the bubble markers to achieve a collaborative mapping path for the UAV group.

6. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 5, characterized in that: The elevation data refers to the height information data of the mission area in the vertical direction. The medium type is distinguished according to the acquired elevation data, and the medium type includes land, water and coastal zone.

7. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 5, wherein: The pilot determines the activation time of the bubble mark. When entering the water, T 入 = 1.2× Tw, when water is discharged, T 出 = 0.8 × Tw, where Tw is the wave period. The pilot aircraft obtains the value of Tw through the built-in wave period measuring device and then calculates the activation duration.

8. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 7, wherein: After deployment, the bubble marker is affected by wind speed and water flow speed, resulting in drift displacement. The calculation formula for the drift distance is: ,in, Indicates wind speed, Indicates the water flow velocity, is the angle between wind speed and water flow speed, and t is the time interval from the deployment of the bubble marker to the arrival of the slave aircraft.

9. The method for optimizing surveying and mapping routes based on UAV remote sensing according to claim 8, characterized in that: The calculation formula for the bubble marker deployment position is: ,in, For deployment location, 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 wind speed and water velocity.

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