Automatic line point location drawing method based on RTK

Abstract

The invention relates to the technical field of point location drawing analysis, in particular to an automatic line point location drawing method based on RTK. The method comprises the following steps: performing geographical high-precision real-time positioning and local plane coordinate system conversion on key points on a navigation line of the unmanned aerial vehicle through RTK positioning equipment to generate unmanned aerial vehicle line coordinate point data; line point location path automatic drawing is carried out on the unmanned aerial vehicle line coordinate point location data, shape and direction identification analysis is carried out, transition tortuosity measurement calculation and line comparison judgment are carried out at the same time, and an unmanned aerial vehicle RTK straight transition line segment and an unmanned aerial vehicle RTK tortuosity transition line segment are obtained; and performing line error fitting smooth optimization and visual display on the unmanned aerial vehicle RTK straight transition line section and the unmanned aerial vehicle RTK tortuous transition line section to generate an unmanned aerial vehicle RTK point location real-time line visual graph. According to the method, the RTK data can be efficiently processed and converted into the line graph.

Description

Technical Field

[0001] The present invention relates to the technical field of point position mapping analysis, and particularly to a method for automatically mapping line point positions based on RTK. Background Art

[0002] In modern engineering construction, especially in the laying and monitoring of unmanned aerial vehicle lines on infrastructure such as power, communication, and transportation, the precise positioning and mapping of lines are important working links. At the same time, RTK technology is a method for obtaining high-precision positioning information through real-time differential correction, and its positioning accuracy can reach the centimeter level, which is suitable for measurement tasks with high-precision requirements. By combining RTK technology with an automated mapping system, and collecting RTK positioning data in real time during line construction or inspection, the data is transmitted to the automatic mapping platform, and a line point position map is automatically drawn according to the coordinate data obtained in real time. Specifically, using an RTK device to collect positioning information and transmitting the data to a backend computer or cloud platform in real time through a wireless network, combined with advanced map drawing algorithms, an accurate line map can be automatically generated.

[0003] Chinese Patent with publication number CN117235944A proposes a method, device, electronic device, and storage medium for automatically generating a water supply and drainage system diagram, including respectively drawing the main pipeline routing diagram and branch pipeline diagram of the water supply and drainage system in a floor plan and connecting them; based on the main pipeline routing diagram and the branch pipeline diagram, reading the water supply routing information, and based on the water supply routing information and the point position information of the water supply points, designing an automatic drawing process, enabling designers to only need to perform simple parameter settings to achieve the automatic drawing of the water supply and drainage system diagram, so that the drawing efficiency can be effectively improved and the reliability is higher.

[0004] Although the technical solution proposed in the above patent can quickly generate a water supply and drainage system diagram, thereby improving the drawing efficiency and reliability, it mainly relies on a pre-designed system diagram and point position information, which means that this method depends on the existing building floor plan and cannot dynamically process changes in the real-time site. If the site conditions change (such as line position changes or route changes), the system diagram needs to be redrawn or adjusted, increasing the need for manual intervention. Especially in the surveying and mapping process of complex terrains and long-distance lines, the difficulty and workload of manual operations are greater, resulting in problems of low drawing efficiency and low accuracy. Summary of the Invention

[0005] Based on this, it is necessary for the present invention to provide a method for automatically mapping line point positions based on RTK to solve at least one of the above technical problems.

[0006] To achieve the above object, a method for automatically mapping line point positions based on RTK includes the following steps:

[0007] Step S1: Use an RTK positioning device to perform high-precision real-time geolocation on key points on the UAV flight route to obtain the geolocation coordinate data corresponding to each key point on the UAV route. The geolocation coordinate data includes longitude and latitude coordinates, vertical height, and elevation direction. Transmit the geolocation coordinate data corresponding to each key point on the UAV route to the UAV terminal device through a wireless transmission device, and use the UAV terminal device to perform a local plane coordinate system conversion on the geolocation coordinate data corresponding to each key point on the UAV route to generate the UAV route coordinate point data corresponding to the local plane coordinate system at the terminal.

[0008] Step S2: Automatically draw the route point path for the UAV route coordinate point data corresponding to the local plane coordinate system at the terminal to generate the initial UAV RTK point route map. Perform shape and direction recognition analysis on each automatically fitted route trajectory in the initial UAV RTK point route map to obtain the trajectory shape and trajectory direction corresponding to each automatically fitted UAV RTK route. Based on the trajectory shape and trajectory direction corresponding to each automatically fitted UAV RTK route, calculate the transition tortuosity measure for the corresponding automatically fitted route trajectory in the initial UAV RTK point route map to obtain the trajectory transition tortuosity corresponding to each automatically fitted UAV RTK route.

[0009] Step S3: Based on the trajectory transition tortuosity corresponding to each automatically fitted UAV RTK route, perform a route comparison and judgment on the corresponding automatically fitted route trajectory to obtain the UAV RTK straight transition route segment and the UAV RTK tortuous transition route segment. Perform route error fitting and smoothing optimization on the UAV RTK straight transition route segment and the UAV RTK tortuous transition route segment in the initial UAV RTK point route map to generate the UAV RTK route optimization map.

[0010] Step S4: Visualize the UAV RTK route optimization map through the friendly graphical interface in the UAV terminal device to generate the real-time route visualization graph of the UAV RTK points.

[0011] Further, Step S1 includes the following steps:

[0012] Step S11: Obtain the digital terrain model and digital surface model corresponding to the UAV flight route, and perform spatial structure topology analysis on the UAV flight route based on the digital terrain model and digital surface model to generate the UAV route terrain spatial structure topology model.

[0013] Step S12: Calculate the terrain slope change rate of the corresponding UAV flight route in the UAV route terrain spatial structure topology model to obtain the terrain slope change rate corresponding to the UAV flight route at different coordinate positions; determine the route terrain support points for the corresponding UAV flight route in the UAV route terrain spatial structure topology model based on the terrain slope change rate corresponding to the UAV flight route at different coordinate positions, so as to obtain the UAV route terrain support points;

[0014] Step S13: Obtain the position area of the environmental obstacles corresponding to the UAV route terrain spatial structure topology model, and perform route obstacle turning point constraint analysis on the corresponding UAV flight route in the UAV route terrain spatial structure topology model based on the position area of the environmental obstacles to obtain the UAV route obstacle turning points;

[0015] Step S14: Perform route network space segmentation on the corresponding UAV flight route in the UAV route terrain spatial structure topology model to obtain the UAV flight route space segmentation network; perform route overlapping intersection point identification analysis on the UAV flight route space segmentation network to obtain the UAV route overlapping intersection points;

[0016] Step S15: Determine the key points on the UAV flight route as the UAV route terrain support points, the UAV route obstacle turning points, and the UAV route overlapping intersection points, and perform high-precision real-time geographic positioning on the key points on the UAV flight route through the RTK positioning device to obtain the geographic positioning coordinate data corresponding to each key point on the UAV route, where the geographic positioning coordinate data includes longitude and latitude coordinates, vertical height, and elevation direction;

[0017] Step S16: Transmit the geographic positioning coordinate data corresponding to each key point on the UAV route to the UAV terminal device through the wireless transmission device, and use the UAV terminal device to perform local plane coordinate system conversion on the geographic positioning coordinate data corresponding to each key point on the UAV route to generate the UAV route coordinate point data corresponding to the local plane coordinate system of the terminal.

[0018] Further, the local plane coordinate system conversion of the geographic positioning coordinate data corresponding to each key point on the UAV route by using the UAV terminal device in Step S16 includes the following steps:

[0019] Use the UAV terminal device to obtain the corresponding device virtual approximate local plane coordinate system;

[0020] Use the UAV terminal device to perform coordinate translation and reconstruction on the geographic positioning coordinate data corresponding to each key point on the UAV route to obtain the longitude and latitude plane reconstruction coordinates, the vertical height plane reconstruction coordinates, and the elevation direction plane reconstruction coordinates corresponding to each key point on the UAV route;

[0021] Cross-domain coordinate system synchronization fusion conversion is performed on the reconstructed longitude and latitude plane coordinates, reconstructed vertical height plane coordinates, and reconstructed elevation direction plane coordinates corresponding to each key point on the UAV line based on the device virtual approximate local plane coordinate system, so as to generate the UAV line coordinate point data corresponding to the local plane coordinate system of the terminal.

[0022] Further, step S2 includes the following steps:

[0023] Step S21: Perform coordinate normalization on the UAV line coordinate point data corresponding to the local plane coordinate system of the terminal to obtain the standardized UAV line point data corresponding to the local plane coordinate system of the terminal;

[0024] Step S22: Calculate the point distance and azimuth angle between different UAV line points in the standardized UAV line point data corresponding to the local plane coordinate system of the terminal to obtain the geometric curve distance and geometric azimuth angle between different UAV line points in the local plane coordinate system of the terminal;

[0025] Step S23: Obtain the UAV line flight constraint conditions, and automatically draw the line point path based on the geometric curve distance and geometric azimuth angle between different UAV line points in the local plane coordinate system of the terminal according to the UAV line flight constraint conditions, so as to generate the initial UAV RTK point line map;

[0026] Step S24: Perform shape and direction recognition analysis on each automatically fitted line trajectory in the initial UAV RTK point line map to obtain the trajectory shape and trajectory direction corresponding to each automatically fitted UAV RTK line;

[0027] Step S25: Calculate the transition tortuosity measure of the automatically fitted line trajectory corresponding to each automatically fitted UAV RTK line in the initial UAV RTK point line map based on the trajectory shape and trajectory direction corresponding to each automatically fitted UAV RTK line, so as to obtain the trajectory transition tortuosity corresponding to each automatically fitted UAV RTK line.

[0028] Further, step S25 includes the following steps:

[0029] Step S251: Perform quantitative analysis of the direction angle offset of the automatically fitted line trajectory corresponding to each automatically fitted UAV RTK line in the initial UAV RTK point line map based on the trajectory direction corresponding to each automatically fitted UAV RTK line to obtain the trajectory direction angle offset amount between each UAV fitted line;

[0030] Step S252: Calculate the trajectory direction change rate statistics for the trajectory direction angle offset between each segment of the UAV fitting line to obtain the trajectory direction change rate corresponding to each segment of the UAV RTK automatic fitting line;

[0031] Step S253: Calculate the trajectory transition curvature for the corresponding automatic fitting line trajectory in the initial UAV RTK point location map based on the trajectory direction change rate corresponding to each segment of the UAV RTK automatic fitting line to obtain the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatic fitting line;

[0032] Step S254: Calculate the transition tortuosity measurement for the corresponding automatic fitting line trajectory in the initial UAV RTK point location map by using the trajectory transition tortuosity measurement formula based on the trajectory shape and the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatic fitting line to obtain the trajectory transition tortuosity corresponding to each segment of the UAV RTK automatic fitting line.

[0033] Further, Step S253 includes the following steps:

[0034] Extract the line trajectory points for the corresponding automatic fitting line trajectory in the initial UAV RTK point location map to obtain the line trajectory point set corresponding to each segment of the UAV RTK automatic fitting line;

[0035] Conduct a trajectory direction angle increment analysis on the corresponding line trajectory point set based on the trajectory direction change rate corresponding to each segment of the UAV RTK automatic fitting line to obtain the trajectory direction angle increment data corresponding to each segment of the UAV RTK automatic fitting line;

[0036] Smooth the trajectory direction angle increment data corresponding to each segment of the UAV RTK automatic fitting line to obtain the trajectory angle increment transition smooth data corresponding to each segment of the UAV RTK automatic fitting line;

[0037] Calculate the trajectory transition curvature for the corresponding automatic fitting line trajectory in the initial UAV RTK point location map based on the trajectory angle increment transition smooth data corresponding to each segment of the UAV RTK automatic fitting line to obtain the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatic fitting line.

[0038] Further, the trajectory transition tortuosity measurement formula in Step S254 is specifically:

[0039]

[0040] In the formula, C j is the trajectory transition tortuosity corresponding to the j-th segment of the UAV RTK automatic fitting line, j is the item index parameter of the UAV RTK automatic fitting line, t1 is the starting time of the trajectory corresponding to the automatically fitted line of the UAV RTK, t 2 is the ending time of the trajectory corresponding to the automatically fitted line of the UAV RTK, t is the time variable parameter, κ j κ(t) is the trajectory direction transition curvature of the j-th automatically fitted line of the UAV RTK at time t, is the average curvature of the j-th automatically fitted line of the UAV RTK corresponding to the time interval [t 1 , t 2 , p is the curvature change weighting exponent, α is the curvature change rate weighting coefficient, β is the trajectory shape influence weighting coefficient, η j ξ(t) is the trajectory shape size of the j-th automatically fitted line of the UAV RTK at time t, ξ is the correction coefficient of the trajectory transition tortuosity.

[0041] Furthermore, step S3 includes the following steps:

[0042] Step S31: Compare and judge the trajectory transition tortuosity of each automatically fitted line of the UAV RTK according to the preset trajectory transition tortuosity threshold. If the corresponding trajectory transition tortuosity is less than the preset trajectory transition tortuosity threshold, then the corresponding automatically fitted line of the UAV RTK is determined as the UAV RTK straight transition line segment; if the corresponding trajectory transition tortuosity is greater than or equal to the preset trajectory transition tortuosity threshold, then the corresponding automatically fitted line of the UAV RTK is determined as the UAV RTK tortuous transition line segment;

[0043] Step S32: Smoothly optimize the straight transition line segments of the UAV RTK in the initial line map of the UAV RTK points to obtain the smoothly optimized straight transition line segments of the UAV;

[0044] Step S33: Segment and fit and optimize the tortuous transition line segments of the UAV RTK in the initial line map of the UAV RTK points to obtain the fitted and optimized tortuous transition line segments of the UAV;

[0045] Step S34: Synchronously optimize the line matching of the initial line map of the UAV RTK points based on the smoothly optimized straight transition line segments of the UAV and the fitted and optimized tortuous transition line segments of the UAV to generate the optimized line map of the UAV RTK.

[0046] Furthermore, step S32 includes the following steps:

[0047] Step S321: Analyze the straightness index of the straight transition line segments of the UAV RTK in the initial line map of the UAV RTK points to obtain the straightness index of the straight transition line segments of the UAV RTK;

[0048] Step S322: Based on the straightness index of the UAV straight-transition line segment, perform line straightness constraint analysis on the corresponding UAV RTK straight-transition line segment to obtain the UAV RTK straightness-constrained line segment;

[0049] Step S323: Perform line curvature smoothing on the UAV RTK straightness-constrained line segment to obtain the UAV RTK straight-curvature smoothed line segment;

[0050] Step S324: Obtain the real-time flight process data of the UAV, and based on the real-time flight process data of the UAV, perform local fine-tuning correction and optimization on the UAV RTK straight-curvature smoothed line segment to obtain the UAV straight-transition smoothed and optimized line segment.

[0051] Furthermore, step S33 includes the following steps:

[0052] Perform trajectory point curvature anomaly detection on the UAV RTK zigzag-transition line segment in the initial line diagram of the UAV RTK points to obtain the path curvature anomaly index corresponding to the trajectory points of the UAV zigzag-transition line segment;

[0053] Based on the path curvature anomaly index corresponding to the trajectory points of the UAV zigzag-transition line segment, perform anomaly segmentation processing on the corresponding UAV RTK zigzag-transition line segment to obtain each UAV zigzag-transition abnormal line segment;

[0054] Perform least squares local fitting optimization on each UAV zigzag-transition abnormal line segment to obtain each UAV zigzag-transition locally fitted and optimized line segment;

[0055] Perform adjacent segment transition connection fitting on each UAV zigzag-transition locally fitted and optimized line segment to obtain the UAV zigzag-transition fitted and optimized line segment.

[0056] Advantages of the present invention:

[0057] The automatic line point drawing method based on RTK proposed by the present invention, compared with the prior art, the beneficial effects of the present application are as follows:

[0058] First, use an RTK positioning device to perform high-precision real-time geolocation on the key points (including support points, turning points, and intersection points) on the UAV flight route. These key points are directly related to the UAV's flight trajectory, mission execution efficiency, and flight safety. RTK (Real-Time Kinematic) technology can provide high-precision geolocation data to ensure that the UAV can accurately determine the position of each key point during flight, including longitude and latitude coordinates, vertical height, and elevation direction, thus avoiding the drawbacks of insufficient accuracy in traditional GPS positioning. This can provide an accurate navigation basis for the UAV, enabling it to achieve autonomous flight in complex environments, reducing the need for manual intervention, which is particularly important for long-duration and long-distance autonomous navigation missions. At the same time, it can also improve the success rate of the mission and ensure that the UAV completes the mission smoothly according to the predetermined trajectory. At the same time, use a wireless transmission device to transmit the geolocation coordinate data corresponding to each key point on the UAV route to the UAV terminal device, and use the UAV terminal device to perform local plane coordinate system conversion on the geolocation coordinate data corresponding to each key point on the UAV route. This is an important step to ensure that the UAV navigation and control system can work efficiently and accurately within the local coordinate framework. Local plane coordinate system conversion can convert the geodetic coordinate system (such as longitude, latitude, and elevation) into a plane coordinate system suitable for flight control and path planning, facilitating the UAV's real-time monitoring and adjustment of the flight path. This step not only improves the UAV's navigation accuracy but also reduces errors caused by coordinate system conversion. Through the collaborative work of the wireless transmission device and the terminal device, it can ensure the real-time update and accurate transmission of data, thereby enhancing the UAV's autonomous navigation and mapping capabilities in complex environments and long-distance routes, providing basic data guarantee for the subsequent trajectory positioning and processing process.

[0059] Secondly, by automatically plotting the path of the UAV line coordinate points corresponding to the local plane coordinate system of the terminal, the flight of the UAV is not only affected by the geographical environment, but also restricted by various constraints, such as maximum flight altitude, speed limit, obstacle avoidance requirements, energy consumption limit, etc. Obtaining and considering these flight constraints is crucial for generating a practical flight path. By combining these constraints with geometric distance and azimuth, a more reasonable and executable flight route can be generated. In this process, automatic path plotting can effectively reduce the time cost of manual planning and avoid errors caused by human factors. The initial UAV RTK point line diagram generated can provide a flight path and, combined with flight constraints, ensure the safety and efficiency of UAV flight, enabling the UAV to dynamically respond to environmental changes and flight mission requirements during actual flight, which helps to optimize the UAV flight path in real time. Additionally, by analyzing the shape and direction of each automatically fitted line trajectory in the initial UAV RTK point line diagram, a more refined analysis of the flight path can be conducted to identify the geometric characteristics of the path. For each fitted line segment, by identifying the trajectory shape, it can be determined whether the path is a straight line, curve, or broken line. The analysis of the trajectory direction helps to identify the heading adjustments required by the UAV during flight to ensure flight stability and path accuracy, further optimizing the flight route, reducing unnecessary navigation errors, and can prompt path adjustments in advance to avoid excessive maneuverability requirements during flight, thereby reducing the energy consumption and wear of the aircraft and ensuring high-precision and low-error flight.

[0060] Thirdly, by calculating the transitional tortuosity measure for the automatically fitted line trajectories corresponding to each segment of the UAV RTK based on the trajectory shape and trajectory direction, the transitional tortuosity of each automatically fitted path can be quantitatively calculated to evaluate the smoothness of the flight path and identify sharp turns and unnecessary path bends during flight. A high transitional tortuosity means there are significant curvature changes in the path during flight, resulting in higher maneuverability requirements and greater energy consumption for the UAV, and even affecting flight safety; while a low transitional tortuosity indicates a smoother path, with more stable and efficient flight. The transitional tortuosity calculation can also help identify potential risk points in the path, such as path selection near complex terrains or obstacles, thus enabling a safer flight strategy to be adopted during planning. This calculation can not only improve the execution efficiency of flight missions, but also ensure the stability and safety of the UAV during flight, contributing to enhancing the success rate and quality of flight line point plotting.

[0061] Then, by comparing and judging the corresponding automatically fitted line trajectories based on the trajectory transition tortuosity corresponding to each section of the UAV RTK, the transition characteristics of the UAV RTK automatically fitted lines can be effectively distinguished, and the "relatively straight" or "tortuous" transition sections can be clearly identified. This judgment not only helps to refine the management of the UAV flight path but also provides a clear direction for subsequent optimization steps. If the transition tortuosity is low, it indicates that the line is relatively straight and easy to smooth; on the contrary, it indicates that the line has a large curvature and requires piecewise fitting to improve the trajectory accuracy. By smoothing and optimizing the relatively straight transition line segments, the flight instability or energy waste caused by the overly straight path during the UAV flight can be effectively reduced. In the relatively straight transition line segments, due to the overly straight route, it will cause vibrations or sudden turns during the flight, affecting the flight accuracy and stability. Through the smoothing optimization technology, while maintaining the flight accuracy, the transition line can be made more stable and the economy of the trajectory can be improved. Also, by performing piecewise fitting optimization on the tortuous transition line segments, the aim is to solve the influence of excessive tortuosity on the flight path. The tortuous transition line segments usually involve relatively complex terrains or flight environments. If not effectively optimized, it will lead to the flight trajectory not meeting the actual requirements or affecting the flight speed and accuracy. Through piecewise fitting, local optimization can be carried out according to the characteristics of each tortuous area, and a highly adaptable fitting algorithm can be used to ensure that each tortuous path can achieve the best flight trajectory and reduce unnecessary flight deviations. This process can effectively reduce the uncertainty during flight, thereby improving the efficiency and accuracy of line point plotting.

[0062] Finally, the visualization display of the RTK line optimization diagram is realized through the friendly graphical interface of the UAV terminal device, which is an important part of the UAV flight system. Visualizing the optimized flight path in real-time on the graphical interface not only helps the pilot and operators to monitor the flight state of the UAV throughout the process but also provides clear flight route information, helping the operators to adjust the path and attitude of the UAV in real-time during the flight. In this way, it is easier to carry out path adjustment and operation, enabling the UAV to complete tasks in a highly dynamic environment without excessive manual intervention. Brief Description of the Drawings

[0063] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0064] Figure 1 It is a schematic flow chart of the steps of the method for automatically plotting line points based on RTK of the present invention;

[0065] Figure 2 For Figure 1Schematic diagram of the detailed step flow of step S1 in Specific implementation mode

[0066] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0067] Embodiment 1:

[0068] To achieve the above object, please refer to Figures 1 to 2 , the present invention provides a method for automatically drawing line point positions based on RTK. In the embodiments of the present invention, please refer to Figure 1 shown, which is a schematic diagram of the step flow of the method for automatically drawing line point positions based on RTK of the present invention. In this example, the method for automatically drawing line point positions based on RTK includes the following steps:

[0069] Step S1: Use an RTK positioning device to perform high-precision real-time geographic positioning on the key points on the UAV flight route to obtain the geographic positioning coordinate data corresponding to each key point on the UAV route, where the geographic positioning coordinate data includes longitude and latitude coordinates, vertical height, and elevation direction; transmit the geographic positioning coordinate data corresponding to each key point on the UAV route to the UAV terminal device through a wireless transmission device, and use the UAV terminal device to perform a local plane coordinate system conversion on the geographic positioning coordinate data corresponding to each key point on the UAV route to generate the UAV route coordinate point data corresponding to the local plane coordinate system of the terminal;

[0070] In an embodiment of the present invention, by obtaining the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) of the area where the UAV flight route is located, these two models can provide the terrain elevation and surface coverage information of the area, further analyze the spatial topological structure of the UAV flight route, identify the key geographical elements (such as slope, obstacles, etc.) that affect the UAV flight path, and calculate the rate of change of the terrain slope of the UAV flight route. By analyzing the elevation changes of each coordinate point in the Digital Terrain Model (DTM), the slope change between adjacent coordinate points can be calculated using a discretization method. A slope algorithm can be used, such as calculating the gradient value or derivative of the ground elevation, to determine which positions belong to steeper or gentler terrain areas. For each coordinate point, when the rate of change of the slope exceeds a certain threshold, mark this point as a terrain support point for the UAV flight. These support points are usually areas with large terrain changes, and further generate terrain support points containing support point information. After obtaining the topological model of the terrain space structure of the UAV flight route, it is also necessary to analyze the area where the environmental obstacles are located along this route. By comparing the Digital Surface Model (DSM) with the environmental obstacle data, the positions of the obstacles are automatically identified, and the constraint analysis of the obstacle avoidance turning points is carried out to obtain the obstacle avoidance turning points. At the same time, by performing spatial segmentation on the area where the UAV flight route is located, the flight path is meshed or divided by an algorithm to form a flight path network with spatial grids as units. In the segmented spatial network, points that may overlap or cross will be automatically identified, and by using RTK (Real-Time Kinematic) technology, based on a high-precision geolocation system, real-time positioning is performed on the key points (such as terrain support points, obstacle avoidance turning points, overlapping and crossing points) on the UAV flight route. The RTK positioning device uses the differential correction between the base station and the rover to provide centimeter-level positioning accuracy. During implementation, the UAV is equipped with an RTK positioning system and receives the differential signal transmitted by the base station in real time to ensure that each key point on the UAV flight path can obtain high-precision longitude and latitude coordinates, vertical height, and elevation data. At this time, the RTK system continuously updates the precise coordinate data of the key points through dynamic monitoring and positioning correction of the flight path, so as to obtain the geographical positioning coordinate data corresponding to each key point on the UAV route, including longitude and latitude coordinates, vertical height, and elevation direction.Then, the high-precision geolocation coordinate data previously obtained by using the wireless transmission device is transmitted to the UAV terminal device. The wireless transmission device usually uses Wi-Fi, 4G / 5G communication, or a dedicated short-range communication protocol (such as LoRa) for data transmission. During the transmission process, the security and reliability of data transmission are ensured. After receiving these data, the UAV terminal device, according to specific mission requirements, converts the geolocation coordinate data (latitude, longitude, and altitude information) into coordinate point data in the local plane coordinate system through a built-in coordinate conversion algorithm. The local plane coordinate system conversion uses common conversion algorithms (such as UTM projection conversion) to convert the high-precision geolocation coordinates into plane coordinate data suitable for the UAV navigation control system, and finally generates the corresponding UAV route coordinate point data in the terminal local plane coordinate system.

[0071] Step S2: Automatically draw the route point path of the UAV route coordinate point data corresponding in the terminal local plane coordinate system to generate an initial UAV RTK point route map; perform shape and direction recognition and analysis on each automatically fitted route trajectory in the initial UAV RTK point route map to obtain the trajectory shape and trajectory direction corresponding to each automatically fitted UAV RTK route; calculate the transition tortuosity measure of the automatically fitted route trajectory corresponding in the initial UAV RTK point route map based on the trajectory shape and trajectory direction corresponding to each automatically fitted UAV RTK route to obtain the trajectory transition tortuosity corresponding to each automatically fitted UAV RTK route.

[0072] In the embodiment of the present invention, the corresponding UAV route point data is obtained from the flight control system of the UAV. These point data are represented in a specific coordinate system form, and these coordinate points are converted to the terminal local plane coordinate system. This conversion usually involves plane coordinate transformation technology, such as using a plane rectangular coordinate system (such as the UTM coordinate system) for standardization processing. Specifically, during operation, it can be selected to convert the data of the global coordinate system to the applicable local coordinate system through projection conversion (such as Gauss-Kruger projection). At the same time, the geometric distance and azimuth angle between each point in the UAV route are calculated based on the different UAV route points in the standardized UAV route point data corresponding in the terminal local plane coordinate system. First, based on the standardized coordinate points, the straight-line distance between different points is calculated using plane geometry formulas (such as the Euclidean distance formula). The calculation method is as follows: Let the coordinates of two points be (x1, y1) and (x2, y2), then the geometric distance d between them can be calculated by the formula: Secondly, calculate the azimuth angle between point and point. The azimuth angle refers to the direction angle from one point to another point, and is usually calculated using the following formula: According to the coordinate differences between each pair of points, calculate the distances and azimuth angles between all points, and automatically generate a preliminary graph of the UAV flight path based on flight constraints. The flight constraints of the UAV include flight altitude, minimum safety interval, flight speed limit, climate condition limit, etc. These conditions need to be considered in the automatic drawing algorithm. Specifically, during operation, first determine the feasibility of the flight path between every two points through the constraints. Based on the azimuth angle and distance information, use the shortest path algorithm (such as A* algorithm, Dijkstra algorithm, etc.) or the constraint-based path planning algorithm to automatically generate a route map that meets the flight requirements. The generated route map will show the preliminary trajectory of the UAV flight. Then, through a detailed shape and direction analysis of each automatically fitted line trajectory in the initial route map of the UAV RTK points, extract each segment of the trajectory in the initial route map of the UAV RTK points and perform automatic fitting. The fitting algorithm usually selects curve fitting techniques, such as least squares fitting, spline curve fitting, etc., to fit each segment of trajectory points into a continuous curve. After fitting, identify the shape characteristics of each segment of the trajectory, such as straight line segments, curved segments, smooth segments, etc. At the same time, it is also necessary to analyze the driving direction of the trajectory. The azimuth angle between the starting point and the ending point can be calculated, and the smoothness of the line can be analyzed by calculating the transition tortuosity of each segment of the trajectory. When measuring the transition tortuosity of the trajectory, first, based on the recognition results of the shape and direction of the trajectory, calculate the change in turning angle and the change in curvature in each fitted trajectory segment. For each fitted trajectory segment, calculate the angle change between adjacent two points to obtain the transition tortuosity of this segment. The tortuosity is usually defined as a comprehensive index of the turning angle change rate or the curvature change rate, and the calculation method can be achieved through derivatives or piecewise curve fitting. Finally, obtain the transition tortuosity corresponding to each segment of the UAV RTK automatically fitted line.

[0073] Step S3: Based on the transition tortuosity corresponding to each segment of the UAV RTK automatically fitted line, conduct a line comparison and judgment on the corresponding automatically fitted line trajectory to obtain the UAV RTK relatively straight transition line segment and the UAV RTK tortuous transition line segment; perform line error fitting and smoothing optimization on the UAV RTK relatively straight transition line segment and the UAV RTK tortuous transition line segment in the initial route map of the UAV RTK points to generate the UAV RTK route optimization map;

[0074] In the embodiments of the present invention, by judging the trajectory transition tortuosity of each segment of the UAV RTK automatic fitting line according to a preset trajectory transition tortuosity threshold, the trajectory transition tortuosity refers to the change frequency of the line segment or the change amount of the angle, which can be obtained by calculating the curvature or derivative change of the line. For example, if the angle change in a line segment is small and relatively smooth, the trajectory transition tortuosity of this segment is low. For each segment of the UAV RTK automatic fitting line, calculate the angle change between its adjacent points. If the change rate is less than the preset threshold, this segment is determined as a "UAV RTK straight transition line segment". On the contrary, if the angle change is greater than or equal to the threshold, it is determined as a "UAV RTK tortuous transition line segment". By performing a smoothing optimization process on the part determined as the "UAV RTK straight transition line segment", the purpose of this optimization process is to reduce or eliminate the linear transition of this line segment and make it more in line with the smooth requirements of flight. Specifically, during implementation, first perform coordinate processing on each RTK point of the straight transition line segment, calculate the distance and angle difference between the starting point and the ending point connected by each straight line segment. According to this difference, use a smoothing algorithm such as B-spline curve or Bezier curve for optimization, recalculate each curve point by interpolation method, and generate a smooth trajectory based on the original line, ensuring that the smoothness of the optimized line meets the design requirements and there are no obvious straight line segments or abrupt turns. And for the part determined as the "UAV RTK tortuous transition line segment", further segmented fitting optimization of the tortuous line is required. The purpose of this step is to remove excessive curvature changes while maintaining the tortuous characteristics of the line, making it more natural and in line with flight requirements. During implementation, first analyze each segment of the tortuous transition line, calculate the curvature change of this segment of the line, especially the angle size and turning speed of each turning point, and perform segmented fitting on the tortuous line by using curve fitting methods (such as least squares fitting, spline interpolation method or high-order polynomial fitting), approximate each turning point of each segment with a higher-precision mathematical model to optimize the smoothness of the curve, so that the irregular changes caused by the tortuosity are corrected, thereby generating a more smooth and trajectory-compliant optimized line. Then, by combining the optimization results of the previous line segments, synchronously optimize the initial line map of the UAV RTK points. The implementation of this step requires overall matching of the optimized smooth line and the fitted line to ensure that the connection of each segment of the line is smooth and the transition is natural, and by adjusting the connection points between each segment, ensure that all line segments can be seamlessly docked. The optimized path needs to meet the actual requirements of UAV flight, such as the maximum turning angle of the aircraft, the minimum flight radius, etc., and finally generate a UAV RTK line optimization map.

[0075] Step S4: Visualize the optimized RTK route map of the drone through the friendly graphical interface in the drone terminal device to generate a real-time route visualization graph of the drone RTK points.

[0076] In the embodiment of the present invention, after the above optimization process, the generated optimized RTK route map of the drone will be visually displayed through the graphical user interface (GUI) of the drone terminal device. Specifically, during implementation, the optimized route map will be presented on the display screen of the terminal device. The user can view the status of the drone's current flight route and its optimized path in real time. The visualization process uses graphics rendering technology to convert the coordinate data in the local plane coordinate system into a path map in the graphical interface. This graphical interface usually includes multiple view elements, such as key points of the flight route, the shape and direction of each section of the path, a progress bar for flight, etc. The user can view specific path information through the interaction interface. In addition, the graphical interface can also dynamically display real-time flight data, such as the current geographical location, speed, altitude, etc. of the drone. The real-time update of all these data and graphics can help the operator monitor the navigation status of the drone and adjust the flight strategy or path as needed to ensure that the flight path of the drone can be automatically adjusted and displayed as the data is updated, thereby achieving precise route control and planning.

[0077] Reference Figure 2 shown in Figure 1 is the detailed step flow schematic diagram of step S1 in

[0078] Step S11: Obtain the digital terrain model and the digital surface model corresponding to the drone navigation route, and perform spatial structure topology analysis on the drone navigation route based on the digital terrain model and the digital surface model to generate a drone route terrain spatial structure topology model;

[0079] In an embodiment of the present invention, by obtaining the digital terrain model (DTM) and the digital surface model (DSM) of the area where the UAV flight route is located, these two models can provide the terrain elevation and surface coverage information of the area. The acquisition methods usually include means such as lidar (LiDAR) scanning, aerial photography, and satellite remote sensing. Using professional software such as ArcGIS, QGIS, or Envi for data processing and model generation. After obtaining the digital terrain model, the ground elevation information is extracted using this model; after obtaining the digital surface model, the elevation information including the ground and all obstacles (such as buildings, trees, etc.) can be obtained, and by calculating the difference between the digital terrain model and the digital surface model, the spatial topological structure of the UAV flight route is further analyzed. Through spatial topological analysis, combined with the coordinate data of the flight route, the key geographical elements (such as slopes, obstacles, etc.) affecting the UAV flight path are identified. At this time, through the geographical information system (GIS) tool, the topological structure analysis of the UAV flight route is realized, and the corresponding terrain spatial structure topological model is generated, and finally the UAV route terrain spatial structure topological model is obtained.

[0080] Step S12: Calculate the terrain slope change rate of the corresponding UAV flight route in the UAV route terrain spatial structure topological model to obtain the terrain slope change rate corresponding to the UAV flight route at different coordinate positions; based on the terrain slope change rate corresponding to the UAV flight route at different coordinate positions, determine the route terrain support points of the corresponding UAV flight route in the UAV route terrain spatial structure topological model to obtain the UAV route terrain support points;

[0081] In an embodiment of the present invention, by calculating the terrain slope change rate of the UAV flight route, by analyzing the elevation changes of each coordinate point in the digital terrain model (DTM), the discrete method is used to calculate the slope change between adjacent coordinate points. Specifically, a slope algorithm can be adopted, such as calculating the gradient value or derivative of the ground elevation, so as to obtain the terrain slope change rate corresponding to the UAV flight route at different coordinate positions. By calculating the continuous slope change rate for each section of the flight route, it can be judged which positions belong to the steeper or gentler terrain areas. For each coordinate point, when the slope change rate exceeds a certain threshold, mark this point as the terrain support point for the UAV flight. These support points are usually areas with large terrain changes and need special attention. According to the different slope change rates, the key support points can be automatically determined, and further generate the terrain support points including the support point information, and finally determine the UAV route terrain support points.

[0082] Step S13: Obtain the position area of environmental obstacles corresponding in the topological model of the UAV route terrain spatial structure, and perform route obstacle avoidance turning point constraint analysis on the corresponding UAV navigation route in the topological model of the UAV route terrain spatial structure to obtain the UAV route obstacle avoidance turning points;

[0083] In the embodiment of the present invention, after obtaining the topological model of the UAV navigation route terrain spatial structure, it is necessary to analyze the position area of environmental obstacles passed by this route. By comparing the digital surface model (DSM) and environmental obstacle data, the positions of obstacles are automatically identified and intersection analysis is performed with the UAV navigation route. During specific operations, using GIS software or computer vision algorithms, the UAV navigation route is scanned to calibrate the area overlapping with obstacles. Subsequently, route obstacle avoidance turning point constraint analysis is carried out, and necessary turning point designs are made at the places where the route intersects with obstacles. According to the specific positions and shapes of environmental obstacles, the turning angles or curvatures of the route are automatically adjusted to ensure that the UAV avoids obstacles during navigation. The setting of this turning point ensures that the UAV can reasonably avoid obstacles in a dynamic environment, while ensuring the continuity and effectiveness of the navigation route, and finally obtains the UAV route obstacle avoidance turning points.

[0084] Step S14: Perform air route network space segmentation on the corresponding UAV navigation route in the topological model of the UAV route terrain spatial structure to obtain the UAV navigation route space segmentation network; perform air route overlapping intersection point identification analysis on the UAV navigation route space segmentation network to obtain the UAV route overlapping intersection points;

[0085] In the embodiment of the present invention, by performing space segmentation on the area where the UAV navigation route is located, the air route is meshed or divided through an algorithm to form an air route network with spatial grids as units. This process can be carried out with the help of air route planning software or custom programs. Through comprehensive analysis of terrain information and air route requirements, efficient space segmentation is performed. In the segmented space network, points that may overlap or intersect will be automatically identified. Especially when multiple UAV navigation routes converge, it will cause air route overlap. By using the intersection analysis algorithm, the segmented air route space is compared with other air route networks to identify these overlapping intersection points and record their specific coordinate positions, and finally obtain the UAV route overlapping intersection points.

[0086] Step S15: Determine the UAV route terrain support points, UAV route obstacle avoidance turning points, and UAV route overlapping intersection points as key points on the UAV navigation route, and perform high-precision real-time geographic positioning on the key points on the UAV navigation route through RTK positioning equipment to obtain the geographic positioning coordinate data corresponding to each key point on the UAV route, where the geographic positioning coordinate data includes longitude and latitude coordinates, vertical height, and elevation direction;

[0087] In the embodiment of the present invention, by using the RTK (Real - Time Kinematic) technology, based on a high - precision geolocation system, real - time positioning is performed on key points (such as terrain support points, obstacle - avoidance turning points, overlapping intersection points) on the flight path of the unmanned aerial vehicle (UAV). The RTK positioning device uses differential correction between the base station and the rover station to provide centimeter - level positioning accuracy. During implementation, the UAV is equipped with an RTK positioning system and receives differential signals transmitted by the base station in real - time, ensuring that each key point on the UAV's flight path can obtain high - precision longitude and latitude coordinates, vertical height, and elevation data. At this time, the RTK system continuously updates the precise coordinate data of the key points through dynamic monitoring and positioning correction of the flight path, and transmits these data to the control system of the UAV in real - time, ensuring that the UAV can accurately reach each preset key point during flight, and finally obtaining the geolocation coordinate data corresponding to each key point on the UAV's line, including longitude and latitude coordinates, vertical height, and elevation direction.

[0088] Step S16: Transmit the geolocation coordinate data corresponding to each key point on the UAV's line to the UAV terminal device through a wireless transmission device, and use the UAV terminal device to perform a local plane coordinate system conversion on the geolocation coordinate data corresponding to each key point on the UAV's line to generate UAV line coordinate point data corresponding to the local plane coordinate system of the terminal.

[0089] In the embodiment of the present invention, the high - precision geolocation coordinate data obtained by previous positioning is transmitted to the UAV terminal device through a wireless transmission device. The wireless transmission device usually uses Wi - Fi, 4G / 5G communication, or a dedicated short - range communication protocol (such as LoRa) for data transmission. During the transmission process, the security and reliability of data transmission are ensured. After receiving these data, the UAV terminal device, according to specific task requirements, converts the geocoordinate data (longitude, latitude, and altitude information) into coordinate point data in the local plane coordinate system through a built - in coordinate conversion algorithm. The local plane coordinate system conversion uses common conversion algorithms (such as UTM projection conversion) to convert high - precision geocoordinates into plane coordinate data suitable for use by the UAV flight control system, and finally generates UAV line coordinate point data corresponding to the local plane coordinate system of the terminal.

[0090] The local plane coordinate system conversion of the geolocation coordinate data corresponding to each key point on the UAV's line by using the UAV terminal device in step S16 includes the following steps:

[0091] Use the UAV terminal device to obtain the corresponding device virtual approximate local plane coordinate system;

[0092] In an embodiment of the present invention, through the high-precision RTK positioning system carried by the UAV terminal device, the geographical coordinate information of the current position of the UAV is first obtained. These geographical coordinate information is usually provided in the form of longitude, latitude, and elevation. However, since the flight of the UAV is usually carried out on a specific route, and the position of this route may be within a relatively small geographical range, a virtual approximate local plane coordinate system can be established by translating and transforming the real-time obtained geographical coordinates. The specific operation is as follows: Select the starting point of the UAV flight as the origin of the local coordinate system, then convert the longitude and latitude of the starting point into plane coordinates in the local coordinate system, and through coordinate translation, convert the geographical coordinates of all subsequent key points into coordinates in this local plane coordinate system. This process makes the subsequent coordinate processing no longer affected by factors such as the earth's curvature, thus simplifying the calculation and processing of coordinate data, and finally obtaining the device virtual approximate local plane coordinate system.

[0093] Preferably, use the UAV terminal device to perform coordinate translation and reconstruction on the geographical positioning coordinate data corresponding to each key point on the UAV route, so as to obtain the longitude and latitude plane reconstruction coordinates, vertical height plane reconstruction coordinates, and elevation direction plane reconstruction coordinates corresponding to each key point on the UAV route.

[0094] In an embodiment of the present invention, by using the UAV terminal device to reconstruct the geographical positioning coordinates of each key point on the UAV flight route, it is ensured that they can be accurately represented in the set local coordinate system. During specific implementation, using the high-precision positioning data received by the RTK of the UAV terminal device, the longitude and latitude coordinates of each key point on the UAV path are obtained. After these longitude and latitude coordinates are converted into the virtual local coordinate system, first perform longitude and latitude plane reconstruction, that is, convert the longitude and latitude coordinates into x and y coordinates in the plane coordinate system, and then calculate the plane reconstruction coordinates of each key point in the vertical height direction through the relative position relationship with the starting point. Reconstruct the elevation data (i.e., the height relative to the ground or reference point) of each key point to obtain the coordinates of this point in the vertical height direction, ensuring the accurate position of this point in three-dimensional space. In this way, the spatial position of each key point can be more accurately reflected, and finally the longitude and latitude plane reconstruction coordinates, vertical height plane reconstruction coordinates, and elevation direction plane reconstruction coordinates corresponding to each key point on the UAV route are obtained.

[0095] Preferably, based on the device virtual approximate local plane coordinate system, perform cross-domain coordinate system synchronization and fusion conversion on the longitude and latitude plane reconstruction coordinates, vertical height plane reconstruction coordinates, and elevation direction plane reconstruction coordinates corresponding to each key point on the UAV route, so as to generate the UAV route coordinate point data corresponding to the terminal local plane coordinate system.

[0096] In the embodiments of the present invention, by synchronously fusing and transforming the coordinate data of each obtained key point in the longitude-latitude plane, the vertical height plane, and the elevation direction plane based on the local coordinate system to generate unified UAV route coordinate data, the core of this operation is cross-domain coordinate transformation, that is, integrating the data under three different coordinate systems into a unified coordinate representation through a mathematical model (such as a coordinate transformation matrix or an interpolation algorithm). In specific implementation, first, use a virtual approximate local plane coordinate system to perform plane transformation on the reconstructed longitude-latitude coordinates of each key point, and perform relative positioning correction on the coordinate data in the vertical height and elevation directions as needed. Then, use a specific coordinate transformation method (such as affine transformation or projection transformation) to fuse all the data into data points in the same coordinate system. Through the synchronous fusion of the coordinate system, a complete set of UAV flight route point data is generated, ensuring the consistency of the coordinates of all key points in the terminal local coordinate system and enabling them to be recognized and utilized by subsequent mapping systems or path planning systems, and finally generating the corresponding UAV route coordinate point data in the terminal local plane coordinate system.

[0097] Embodiment 2:

[0098] Further, on the basis of Embodiment 1, step S2 includes the following steps:

[0099] Step S21: Perform coordinate normalization on the UAV route coordinate point data corresponding to the terminal local plane coordinate system to obtain the UAV route standardized point data corresponding to the terminal local plane coordinate system;

[0100] In the embodiments of the present invention, by obtaining the corresponding UAV route point data from the flight control system of the UAV, these point data are represented in a specific coordinate system form, such as the WGS-84 coordinate system or other coordinate systems suitable for UAV flight tracks. For the convenience of subsequent processing, it is necessary to convert these coordinate points to the terminal local plane coordinate system. This conversion usually involves plane coordinate transformation techniques, such as using a plane rectangular coordinate system (such as the UTM coordinate system) for standardization processing. In specific operation, it can be selected to convert the data of the global coordinate system to the applicable local coordinate system through projection transformation (such as the Gauss-Kruger projection). When performing the conversion, special attention should be paid to selecting a reasonable reference point (such as the takeoff point or the task starting point), and perform coordinate normalization on each point through the plane coordinate transformation formula to eliminate the influence of elevation errors and ensure the accurate mapping of all points in the local coordinate system, and finally obtain the UAV route standardized point data corresponding to the terminal local plane coordinate system.

[0101] Step S22: Calculate the point distance and azimuth angle between different UAV line points in the UAV line standardized point data corresponding to the terminal local plane coordinate system, so as to obtain the geometric curve distance and geometric azimuth angle between different UAV line points in the terminal local plane coordinate system;

[0102] In the embodiment of the present invention, by calculating the geometric distance and azimuth angle between each point in the UAV line based on different UAV line points in the UAV line standardized point data corresponding to the terminal local plane coordinate system, first, based on the standardized coordinate points, the straight-line distance between different points is calculated based on the plane geometry formula (such as the Euclidean distance formula). The calculation method is as follows: Let the coordinates of two points be (x1, y1) and (x2, y2), then the geometric distance d between them can be calculated by the formula: Secondly, calculate the azimuth angle between points. The azimuth angle refers to the direction angle from one point to another point, and is usually calculated using the following formula: According to the coordinate differences between each pair of points, calculate the distance and azimuth angle between all points, and finally obtain the geometric curve distance and geometric azimuth angle between different UAV line points in the terminal local plane coordinate system.

[0103] Step S23: Obtain the UAV line flight constraint conditions, and automatically draw the line point path for the geometric curve distance and geometric azimuth angle between different UAV line points in the terminal local plane coordinate system based on the UAV line flight constraint conditions, so as to generate the initial UAV RTK point line map;

[0104] In the embodiment of the present invention, by automatically generating a preliminary graph of the UAV flight line based on the flight constraint conditions, the flight constraint conditions of the UAV include flight altitude, minimum safety interval, flight speed limit, climate condition limit, etc. These conditions need to be considered in the automatic drawing algorithm. Specifically, during operation, first determine the feasibility of the flight path between every two points through the constraint conditions. Based on the azimuth angle and distance information, use the shortest path algorithm (such as the A* algorithm, Dijkstra algorithm, etc.) or the constraint-based path planning algorithm to automatically generate a line map that meets the flight requirements. These constraint conditions ensure that each line in the line map meets the flight safety standards and effectively avoids obstacles or flight restricted areas. The automatic drawing of the line map is usually carried out through a simulation tool or automated drawing software (such as a GIS system or CAD tool). The generated line map will display the preliminary trajectory of the UAV flight, and finally draw and generate the initial UAV RTK point line map.

[0105] Step S24: Perform shape and direction recognition analysis on each automatically fitted line trajectory in the initial line diagram of the UAV RTK points to obtain the trajectory shape and trajectory direction corresponding to each automatically fitted line of the UAV RTK.

[0106] In the embodiment of the present invention, through detailed shape and direction analysis of each automatically fitted line trajectory in the automatically drawn initial line diagram of the UAV RTK points, each trajectory in the initial line diagram of the UAV RTK points is extracted and automatically fitted. The fitting algorithm usually selects curve fitting techniques, such as least squares fitting, spline curve fitting, etc., to fit each trajectory point into a continuous curve. After the fitting is completed, the shape characteristics of each trajectory, such as straight line segments, curved segments, smooth segments, etc., will be recognized. At the same time, it is also necessary to analyze the driving direction of the trajectory. The direction of the trajectory can be further determined by calculating the azimuth angle between the starting point and the ending point and combining local geometric features. Through shape recognition, the flight characteristics of each segment, such as turning radius, smoothness, etc., can be determined, and finally the trajectory shape and trajectory direction corresponding to each automatically fitted line of the UAV RTK are obtained.

[0107] Step S25: Calculate the transitional tortuosity measure of the automatically fitted line trajectory corresponding to each automatically fitted line of the UAV RTK in the initial line diagram of the UAV RTK points to obtain the trajectory transitional tortuosity corresponding to each automatically fitted line of the UAV RTK.

[0108] In this embodiment, by calculating the transitional tortuosity of each trajectory, the smoothness of the line is analyzed. When performing the trajectory transitional tortuosity measure, first, based on the shape and direction recognition results of the trajectory, the turning angle change and curvature change in each fitted trajectory are calculated. For each fitted trajectory, the transitional tortuosity of this segment is obtained by calculating the angle change between adjacent two points. The tortuosity is usually defined as a comprehensive index of the turning angle change rate or the curvature change rate. The calculation method can be achieved through derivatives or piecewise curve fitting. Finally, the trajectory transitional tortuosity corresponding to each automatically fitted line of the UAV RTK is obtained.

[0109] Embodiment 3:

[0110] Further, on the basis of Embodiment 2, Step S25 includes the following steps:

[0111] Step S251: Perform quantitative analysis of the direction angle offset of the automatically fitted line trajectory corresponding to each automatically fitted line of the UAV RTK in the initial line diagram of the UAV RTK points to obtain the trajectory direction angle offset amount between each UAV fitted line.

[0112] In the embodiments of the present invention, by analyzing the direction angles of the trajectory data of each fitted line obtained based on the UAV RTK system, the specific operation is as follows: through coordinate transformation of the original trajectory points collected by the UAV RTK, converting them into longitude and latitude information in the geographical coordinate system, then calculating the connection direction between the starting point and the ending point of each trajectory, and further obtaining the direction angle of each trajectory. The calculation formula for these direction angles can use the bearing angle, that is, the angle between the straight line connecting two trajectory points and the geographical north direction. For each fitted line, it is necessary to conduct a quantitative analysis of the angle offset of the trajectory direction to obtain the angle difference between the fitted trajectory of this section and the corresponding line in the initial line map. By calculating, the angle offset of each trajectory is obtained, and the direction deviation between the automatically fitted line and the initial planned line is obtained. Finally, the trajectory direction angle offset between each UAV fitted line is obtained.

[0113] Step S252: Calculate the statistical change rate of the trajectory direction for the trajectory direction angle offset between each UAV fitted line to obtain the trajectory direction change rate corresponding to each automatically fitted line of the UAV RTK.

[0114] In the embodiments of the present invention, by conducting statistical calculation of the direction change rate for the trajectory direction angle offset between each UAV fitted line. Specifically, the previously obtained angle offsets are arranged in the order of the trajectories, and the angle change amount between adjacent two fitted lines is calculated, that is, the direction angle difference between every two consecutive trajectories. Based on these angle differences, the trajectory direction change rate is statistically calculated. The trajectory direction change rate refers to the degree of change of the trajectory direction angle within a unit time (or within a unit trajectory length), usually calculated as the ratio of the angle change to the time or the length of the trajectory segment. Finally, the trajectory direction change rate corresponding to each automatically fitted line of the UAV RTK is obtained.

[0115] Step S253: Calculate the trajectory transition curvature for the automatically fitted line trajectory corresponding to each UAV RTK in the initial line map based on the trajectory direction change rate corresponding to each automatically fitted line of the UAV RTK to obtain the trajectory direction transition curvature corresponding to each automatically fitted line of the UAV RTK.

[0116] In an embodiment of the present invention, by calculating the trajectory transition curvature of the corresponding automatically fitted line trajectory based on the change rate of the trajectory direction corresponding to each segment of the UAV RTK automatically fitted line. Specifically, first, the curve fitting algorithm (such as spline interpolation method or least squares method) is used to smooth the trajectory curve of each fitted trajectory to ensure that each segment of trajectory data can smoothly transition to the next segment. And by calculating the second derivative of the curve, the trajectory curvature is obtained. The trajectory curvature is a physical quantity describing the degree of curve change, indicating the degree of bending of the curve at a certain point. According to the trajectory direction change rate calculated in the previous step, the turning points of the trajectory can be found in each fitted line, and the curvature values of these turning points are calculated. The calculation of the trajectory transition curvature can help analyze the bending degree of each segment of the trajectory, and then adjust the flight path to avoid situations of excessive bending or too sudden curve changes, and finally obtain the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatically fitted line.

[0117] Step S254: Based on the trajectory shape and the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatically fitted line, use the trajectory transition tortuosity measurement calculation formula to calculate the transition tortuosity of the corresponding automatically fitted line trajectory in the initial line map of the UAV RTK points, so as to obtain the trajectory transition tortuosity corresponding to each segment of the UAV RTK automatically fitted line.

[0118] In an embodiment of the present invention, by combining the time variable parameter, trajectory direction transition curvature, average curvature, curvature change weighted index, curvature change rate weighted coefficient, trajectory shape influence weighted coefficient, trajectory shape size and related parameters corresponding to the UAV RTK automatically fitted line, a suitable trajectory transition tortuosity measurement calculation formula is formed to calculate the transition tortuosity of the corresponding automatically fitted line trajectory in the initial line map of the UAV RTK points, so as to quantitatively calculate the transition tortuosity of each fitted line. This measurement value represents the smoothness or bending degree of the trajectory. A higher transition tortuosity indicates a larger curvature change in this segment of the trajectory and a more complex flight path. Finally, the trajectory transition tortuosity corresponding to each segment of the UAV RTK automatically fitted line is obtained.

[0119] Embodiment 4:

[0120] Further, on the basis of Embodiment 3, step S253 includes the following steps:

[0121] Extract the line trajectory points of the corresponding automatically fitted line trajectory in the initial line map of the UAV RTK points to obtain the line trajectory point set corresponding to each segment of the UAV RTK automatically fitted line;

[0122] In an embodiment of the present invention, by extracting the trajectory point set of each automatically fitted line from the initial line diagram of the UAV RTK points. Specifically, a high-precision RTK positioning system is used to obtain the coordinate data collected by the UAV during flight, and a preliminary line diagram is drawn based on these data. Then, an automated algorithm is used to fit these points to determine each automatically fitted line in the UAV flight path. The fitting algorithm can adopt spline interpolation or polynomial fitting methods to ensure the smoothness and accuracy of the fitting result. After the fitting is completed, the trajectory points of each automatically fitted line are extracted to form a point set, ensuring that the flight path and heading of the UAV can be accurately reflected in the subsequent steps, and finally obtaining the line trajectory point set corresponding to each UAV RTK automatically fitted line.

[0123] Preferably, based on the trajectory direction change rate corresponding to each UAV RTK automatically fitted line, a trajectory direction angle increment analysis is performed on the corresponding line trajectory point set to obtain the trajectory direction angle increment data corresponding to each UAV RTK automatically fitted line;

[0124] In an embodiment of the present invention, by performing a trajectory direction angle increment analysis on the corresponding line trajectory point set based on the trajectory direction change rate of each UAV RTK automatically fitted line. First, for each fitted trajectory point set, the heading angle is calculated according to the coordinates of adjacent points. The heading angle can be obtained by performing an arctangent calculation on the coordinate difference of adjacent trajectory points, usually expressed as the angle relative to the north (with 0 degrees being north and increasing clockwise). Then, the change value between adjacent heading angles, that is, the trajectory direction change rate, is calculated. By solving the angle difference between consecutive trajectory points, the direction angle increment data of each fitted trajectory is obtained. These data can describe the heading change situation during the UAV flight. Specifically, if the UAV flies in a straight line, the direction increment change is small; if there are turns or large changes in the flight path, the direction increment change is large. Finally, the trajectory direction angle increment data corresponding to each UAV RTK automatically fitted line is obtained.

[0125] Preferably, the trajectory direction angle increment data corresponding to each UAV RTK automatically fitted line is smoothed to obtain the trajectory angle increment transition smooth data corresponding to each UAV RTK automatically fitted line;

[0126] In the embodiments of the present invention, by smoothing the trajectory direction angle increment data of each segment of the UAV RTK automatic fitting line, in order to eliminate the angle fluctuations caused by measurement errors, dynamic instabilities or environmental interferences, a smoothing filtering algorithm is used for data processing. Common smoothing methods include Gaussian filtering, weighted moving average, Kalman filtering, etc. Among them, the Gaussian filtering method applies a Gaussian kernel function to the trajectory direction angle increment data, performs weighted average processing on the data, reduces the influence of mutation points, ensures the smooth change of the trajectory direction angle, and the smoothed data set presents a relatively smooth heading change trend, which can effectively remove noise interference and further improve the accuracy and reliability of subsequent calculations. After smoothing, a transition angle increment data set for each segment of the trajectory is obtained, and these data sets will be used as inputs in the calculation of the trajectory transition curvature, and finally, the trajectory angle increment transition smooth data corresponding to each segment of the UAV RTK automatic fitting line is obtained.

[0127] Preferably, based on the trajectory angle increment transition smooth data corresponding to each segment of the UAV RTK automatic fitting line, the trajectory transition curvature of the corresponding automatic fitting line trajectory in the UAV RTK point position initial line map is calculated to obtain the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatic fitting line.

[0128] In the embodiments of the present invention, by calculating the trajectory transition curvature based on the smoothed trajectory direction angle increment data, the curvature is an important parameter describing the trajectory change rate and can reflect the bending degree of the UAV's path during flight. In the specific implementation process, first, the curvature of each segment of the trajectory is calculated according to the direction angle increment transition data of each segment of the trajectory. The calculation of the curvature can be obtained by solving the second derivative of the trajectory to obtain the change rate of the trajectory at each point. The calculation method is generally to obtain the curve expression of the path through a curve fitting algorithm based on the trajectory point set on each segment of the fitting path, and then calculate the curvature of the path based on this expression. For each segment of the UAV RTK automatic fitting line, the direction angle increment transition data of the trajectory can reflect its path bending characteristics, and then the curvature is accurately calculated through numerical methods (such as the derivative method, finite difference method, etc.), and finally, the trajectory direction transition curvature corresponding to each segment of the UAV RTK automatic fitting line is obtained.

[0129] Further, the specific formula for the trajectory transition tortuosity measurement in step S254 is:

[0130]

[0131] In the formula, C j is the trajectory transition tortuosity corresponding to the j-th segment of the UAV RTK automatic fitting line, j is the item index parameter of the UAV RTK automatic fitting line, t 1is the starting time of the trajectory corresponding to the automatically fitted route of the UAV RTK, t 2 is the ending time of the trajectory corresponding to the automatically fitted route of the UAV RTK, t is the time variable parameter, κ j (t) is the trajectory direction transition curvature of the j-th segment of the automatically fitted route of the UAV RTK at time t, is the average curvature corresponding to the j-th segment of the automatically fitted route of the UAV RTK in the time interval [t 1 , t 2 , p is the curvature change weighting exponent, α is the curvature change rate weighting coefficient, β is the trajectory shape influence weighting coefficient, η j (t) is the trajectory shape size of the j-th segment of the automatically fitted route of the UAV RTK at time t, and ξ is the correction coefficient of the trajectory transition tortuosity.

[0132] In the present invention, a trajectory transition tortuosity measurement calculation formula is obtained through the use of a specific mathematical model and verification, which is used to calculate the transition tortuosity of the automatically fitted route trajectory corresponding to the initial route map of the UAV RTK points. This trajectory transition tortuosity measurement calculation formula is represented by κ j (t) represents the direction transition curvature of the j-th segment of the trajectory at time t, which reflects the direction change rate of the trajectory at a specific moment. The transition curvature of the trajectory is crucial for the stability and accuracy of UAV flight. Especially in a complex environment, excessive curvature changes may lead to flight instability or increase the control difficulty. is the average curvature of this segment of the trajectory in the time interval [t 1 , t 2 , which can provide the overall trend of the direction change of this segment of the trajectory and is used for the evaluation of smoothness. If the curvature deviates too much from the average curvature, it means that the direction change is too drastic, which will affect the smoothness of the flight path.

[0133] The curvature change weighting exponent is used to adjust the sensitivity of the curvature change. Through the weighting coefficient, the influence of the curvature change on the trajectory transition can be amplified or reduced according to actual needs. For example, if p is large, it indicates that the system is more sensitive to the curvature change, which helps to finely control the small changes in the flight trajectory; while the curvature change rate weighting coefficient mainly considers the influence of the curvature change rate on the flight path. When the curvature change rate is too large, it may mean that there is a sharp turn in the path, increasing the flight risk. By adjusting this coefficient, the smoothness of the trajectory can be optimized and the sharp turns during flight can be reduced; the trajectory shape influence weighting coefficient weights the influence of the trajectory shape itself. If the trajectory shape is complex, such as having a large curvature, the morphological change of the trajectory may affect the flight stability. By adjusting, the influence of the trajectory shape on the flight path can be strengthened or weakened. Secondly, η j(t) represents the size of the trajectory shape, which can quantify the "complexity" or "rate of change" of the trajectory itself. It directly affects the smoothness of flight. If η j (t) changes too drastically, stronger dynamic disturbances will be felt during the flight process, and the trajectory design needs to be adjusted to make it smoother. Additionally, by introducing a correction coefficient for the tortuosity of the trajectory transition, it is usually used to finally adjust this metric value, making the calculation result more in line with the requirements of the actual flight mission. Its existence ensures the flexibility and adjustability of the formula to adapt to different flight environments and requirements. This formula integrates the weighted effects of multiple factors, comprehensively considering the tortuosity of the trajectory transition from multiple dimensions such as direction change, curvature change rate, and trajectory shape. Through this multi-dimensional analysis, not only can the overall smoothness and stability of the flight trajectory be quantified, but also potential flight path discontinuities or parts with unsmooth transitions can be deeply identified and optimized. In summary, this formula fully considers the tortuosity of the trajectory transition C j corresponding to the j-th segment of the UAV RTK automatic fitting line, the item index parameter j of the UAV RTK automatic fitting line, the starting time t 1 of the trajectory corresponding to the UAV RTK automatic fitting line, the ending time t 2 of the trajectory corresponding to the UAV RTK automatic fitting line, the time variable parameter t, the trajectory direction transition curvature κ j (t) of the j-th segment of the UAV RTK automatic fitting line at time t, the average curvature 1 of the j-th segment of the UAV RTK automatic fitting line corresponding in the time interval [t 2 The curvature change weighted exponent p, the curvature change rate weighted coefficient α, the trajectory shape influence weighted coefficient β, the size η j (t) of the trajectory shape of the j-th segment of the UAV RTK automatic fitting line at time t, the correction coefficient ξ of the trajectory transition tortuosity, and based on the mutual correlation relationship between the tortuosity of the trajectory transition C j corresponding to the j-th segment of the UAV RTK automatic fitting line and the above various parameters, a functional relationship is formed:

[0134]

[0135] This formula can implement the calculation process of the tortuosity of the trajectory transition of the automatic fitting line corresponding to the initial line map of the UAV RTK point position. At the same time, by introducing the correction coefficient ξ of the trajectory transition tortuosity, it can be adjusted according to the error situation in the calculation process, thereby improving the accuracy and applicability of the formula for calculating the tortuosity of the trajectory transition.

[0136] Example 5:

[0137] Further, on the basis of Example 1, step S3 includes the following steps:

[0138] Step S31: Compare and judge each UAV RTK automatic fitting line according to the preset trajectory transition tortuosity threshold for the trajectory transition tortuosity. If the corresponding trajectory transition tortuosity is less than the preset trajectory transition tortuosity threshold, the corresponding UAV RTK automatic fitting line is determined as the UAV RTK straight transition line segment; if the corresponding trajectory transition tortuosity is greater than or equal to the preset trajectory transition tortuosity threshold, the corresponding UAV RTK automatic fitting line is determined as the UAV RTK tortuous transition line segment.

[0139] In the embodiment of the present invention, by judging the trajectory transition tortuosity of each UAV RTK automatic fitting line according to the preset trajectory transition tortuosity threshold, the trajectory transition tortuosity refers to the change frequency of the line segment or the change amount of the angle, which can be obtained by calculating the curvature or derivative change of the line. For example, if the angle change in a line segment is small and relatively smooth, the trajectory transition tortuosity of this segment is low. For each UAV RTK automatic fitting line, calculate the angle change between its adjacent points. If the change rate is less than the preset threshold, this segment is determined as the "UAV RTK straight transition line segment". On the contrary, if the angle change is greater than or equal to the threshold, it is determined as the "UAV RTK tortuous transition line segment". This threshold is usually set according to the actual flight environment and mission requirements. For example, for short-distance flights or stable paths, the threshold can be set to 0.5°; for complex or irregular paths, the threshold may be set to a larger value. This step mainly relies on mathematical operations on the angle differences of the line points and completes the classification by comparing the curvature change with the preset threshold.

[0140] Step S32: Smoothly optimize the UAV RTK straight transition line segments in the initial line map of the UAV RTK points to obtain the UAV straight transition smooth optimized line segments.

[0141] In the embodiment of the present invention, by performing a smoothing optimization process on the part determined as the "UAV RTK straight transition line segment", the purpose of this optimization process is to reduce or eliminate the linear transition of this line segment and make it more in line with the smooth requirements of flight. Specifically, during implementation, first perform coordinate processing on each RTK point of the straight transition line segment, calculate the distance and angle difference between the starting point and the ending point of each straight line connection, and according to this difference, use a smoothing algorithm such as B-spline curve or Bezier curve for optimization, recalculate each curve point by interpolation method, and generate a smooth trajectory on the basis of the original line. For the optimization result, by calculating the derivative and curvature of each new point after optimization, ensure that the smoothness of the optimized line meets the design requirements and no obvious straight line segments or abrupt turns appear, and finally obtain the UAV straight transition smooth optimized line segments.

[0142] Step S33: Perform piecewise fitting optimization on the RTK zigzag transition line segments in the initial route map of the UAV RTK points to obtain the optimized UAV zigzag transition fitting line segments;

[0143] In the embodiment of the present invention, the part determined as the "UAV RTK zigzag transition line segment" needs to be further optimized by piecewise fitting of the zigzag line. The purpose of this step is to remove excessive curvature changes while maintaining the zigzag characteristics of the line, making it more natural and meeting the flight requirements. During implementation, first analyze each zigzag transition line segment, calculate the curvature change of this line segment, especially the angle size and turning speed of each turning point, and perform piecewise fitting on the zigzag line by using curve fitting methods (such as least squares fitting, spline interpolation method, or high-degree polynomial fitting). Approximate the turning points of each segment with a higher-precision mathematical model to optimize the smoothness of the curve. In this way, the irregular changes caused by the zigzag are corrected, resulting in a more smooth and trajectory-compliant optimized line, and finally obtaining the optimized UAV zigzag transition fitting line segments.

[0144] Step S34: Based on the UAV straight transition smooth optimized line segments and the UAV zigzag transition fitting optimized line segments, perform line matching and synchronization optimization on the initial route map of the UAV RTK points to generate the optimized UAV RTK route map.

[0145] In the embodiment of the present invention, by combining the optimization results of the previous line segments, based on the UAV straight transition smooth optimized line segments and the zigzag transition fitting optimized line segments, perform synchronous optimization on the initial route map of the UAV RTK points. The implementation of this step requires overall matching of the optimized smooth line and the fitting line to ensure smooth connection and natural transition of each line segment. During specific implementation, first align the optimized straight transition line segment and the zigzag transition line segment with the route map of the original RTK points respectively, calculate the relative position and angle difference between each line segment, and then ensure seamless docking of all line segments by adjusting the connection points between each segment. The optimized path needs to meet the actual requirements of UAV flight, such as the maximum turning angle and minimum flight radius of the aircraft, etc., and finally generate the optimized UAV RTK route map.

[0146] Step S32 includes the following steps:

[0147] Step S321: Analyze the straightness index of the UAV RTK straight transition line segments in the initial route map of the UAV RTK points to obtain the straightness index of the UAV straight transition line segments;

[0148] In an embodiment of the present invention, a straightness index analysis is required for the "straight transition line segment" appearing in the initial route map of the UAV RTK point. Straightness refers to the degree of deviation between a line and its ideal straight path. The calculation method of the straightness index can be achieved by comparing the geometric difference between the actual flight path and the ideal straight path. Usually, the straightness index calculation formula can be set to a comprehensive value of multiple factors such as the curvature, offset, and angle difference between the line and the ideal trajectory. During implementation, a curve fitting algorithm (such as spline interpolation) can be used to obtain the ideal straight line, and then the Euclidean distance is used to calculate the deviation value of each measuring point to the fitted ideal straight line. By accumulating these deviations, the straightness index of each transition segment is obtained. This analysis can effectively identify transition segments with large deviations, and finally obtain the straightness index of the straight transition line segment of the UAV.

[0149] Step S322: performing line straightness constraint analysis on the corresponding UAV RTK straight transition line segment based on the straightness index of the UAV straight transition line segment to obtain the UAV RTK straight constraint line segment;

[0150] In an embodiment of the present invention, a line straightness constraint analysis is performed on the corresponding UAV RTK straight transition line segment based on the previously calculated straightness index. The core goal of this process is to mark those transition line segments whose straightness index exceeds the set threshold as "straightness constrained line segments". In specific implementation, a deviation range can be set according to the size of the straightness index. When the straightness index exceeds the preset standard value, a straightness constraint is automatically applied to the corresponding line segment. This constraint can be achieved in the following way: in subsequent flight plans, stricter trajectory planning is set for these line segments, the curvature range of the flight trajectory is limited, and it is ensured that the UAV avoids excessive heading deviations as much as possible during flight. This process can be achieved using computer-aided design (CAD) software or a special trajectory optimization tool. The path is optimized and corrected under the constraints to ensure that the flight route meets the requirements of navigation safety and accuracy, and finally the UAV RTK straightness constraint line segment is obtained.

[0151] Step S323: performing line curvature smoothing processing on the UAV RTK straight constraint line segment to obtain the UAV RTK straight curvature smoothed line segment;

[0152] In an embodiment of the present invention, by performing line curvature smoothing processing on the previously identified straightness-constrained line segments, the goal of this step is to eliminate sharp turns caused by excessive deviation in the transition segments, ensuring that the UAV flight path is smoother and more continuous. In specific operations, first, numerical optimization algorithms (such as Bezier curve fitting or B-spline interpolation) are used to smooth these line segments. These algorithms can optimize each segment of the curve, reduce unnecessary sharp bends, thereby reducing the curvature. The smoothing is achieved by adjusting the distribution of control points, making the change of the curve tend to be gentle. For each curve segment, the smoothing algorithm can, based on the known start and end positions, adjust the positions of the intermediate nodes so that the path curvature always remains within a reasonable range. During this process, the dynamic characteristics of the UAV flight also need to be considered to ensure that the flight control system can fly stably on the smoothed line, ensuring that the energy consumption during the UAV flight is minimized and the flight speed is stable. Finally, the RTK straightness-curvature smoothed line segment of the UAV is obtained.

[0153] Step S324: Obtain the real-time flight process data of the UAV, and perform local fine-tuning correction and optimization on the RTK straightness-curvature smoothed line segment of the UAV based on the real-time flight process data of the UAV to obtain the straightness-transition smoothed and optimized line segment of the UAV.

[0154] In an embodiment of the present invention, local fine-tuning correction is performed by obtaining the real-time flight process data of the UAV to further optimize the straightness-transition smoothed line segment. The real-time flight data of the UAV is usually obtained through an RTK positioning system, which includes information such as the immediate position, speed, acceleration, and flight state of the flight. By analyzing these data in real time, it can be determined in real time whether the current flight state deviates from the predetermined smoothed path. During this process, first, the current position and speed information of the UAV need to be collected in real time and compared with the preset optimized line. If the deviation exceeds the allowable error range, a new correction path will be automatically calculated and generated. The correction process can use dynamic programming algorithms or gradient descent methods to perform local fine-tuning on the path based on the current flight position, heading, and flight speed. The corrected line will be fed back to the UAV control system in real time to ensure that its flight trajectory tends to the ideal smooth route as much as possible, effectively improving the path tracking accuracy and flight stability of the UAV in a complex flight environment. Finally, the straightness-transition smoothed and optimized line segment of the UAV is obtained.

[0155] Furthermore, step S33 includes the following steps:

[0156] Perform trajectory point curvature anomaly detection on the RTK zigzag transition line segment in the initial line map of the UAV RTK points to obtain the path curvature anomaly index corresponding to the trajectory points of the UAV zigzag transition line segment;

[0157] In an embodiment of the present invention, trajectory points of a previously divided RTK meandering transition route segment of a drone are divided, and abnormal meandering is identified by calculating the curvature of each trajectory point. Specifically, the curvature is calculated for a certain number of neighborhood points around each trajectory point, and the "curvature formula where x′(t) and y′(t) are the derivatives of the trajectory point path, and x″(t) and y″(t) are the second derivatives of the trajectory point path" is used. Through this formula, the curvature value of the path at each point can be obtained, and then the path curvature anomaly index is calculated where n is the number of trajectory points on the meandering transition route segment. The trajectory segments with abnormally large curvature changes are identified, and finally, the path curvature anomaly index corresponding to the trajectory points of the drone meandering transition route segment is obtained.

[0158] Preferably, based on the path curvature anomaly index corresponding to the trajectory points of the drone meandering transition route segment, the corresponding RTK meandering transition route segment of the drone is processed for abnormal segmentation to obtain each abnormal meandering route segment of the drone;

[0159] In an embodiment of the present invention, the RTK meandering transition route segment of the drone is processed for abnormal segmentation by using the previously obtained curvature anomaly index. Specifically, the trajectory points with a curvature anomaly index greater than the set threshold are considered as abnormal points, and thus the abnormal meandering route segments are delimited. During the segmentation process, the rule that the continuous curvature anomaly index exceeds the threshold is adopted, and an abnormal meandering line segment is delimited as an abnormal segment. The division criterion for the abnormal segment is based on a certain curvature change range, and the general value range can be between 0.1 and 1.0, which is specifically set according to the actual curvature characteristics of the path. Each abnormal segment is composed of several consecutive curvature abnormal points, and the interval between these abnormal points does not exceed the set maximum distance limit. Finally, each abnormal meandering route segment of the drone is obtained.

[0160] Preferably, least squares local fitting optimization is performed on each abnormal meandering route segment of the drone to obtain each locally fitted and optimized meandering route segment of the drone;

[0161] In the embodiments of the present invention, by performing least - squares local fitting optimization on each identified abnormal line segment. First, extract the trajectory point coordinates on each abnormal line segment, regard them as a local data set, and through the use of the least - squares fitting method, by minimizing the fitting error of the trajectory points, optimize the smoothness of the line. During the fitting process, set the degree of fitting and the error tolerance. Usually, quadratic or cubic polynomial fitting is selected to ensure that the fitted path can better approximate the original trajectory and eliminate the influence of abnormal points. In specific implementation, adjust the fitting parameters through numerical optimization algorithms (such as the gradient descent method or the Gauss - Newton method) until the fitting error is minimized. The obtained fitting result is a smooth curve that can better replace the abnormal line segment, and finally obtain the local fitting optimization line segments of the zigzag transition of each drone.

[0162] Preferably, perform adjacent segment transition connection fitting on the local fitting optimization line segments of the zigzag transition of each drone to obtain the fitted optimization line segments of the zigzag transition of the drone.

[0163] In the embodiments of the present invention, by performing transition connection fitting for every two adjacent abnormal line segments. First, pair the starting points and ending points of adjacent segments, calculate the contact points of these segments, and then use smooth connection techniques. According to the tangent directions and curvature values of the starting points and ending points, perform local transition fitting. Commonly used techniques include cubic spline interpolation. By constructing a smooth cubic spline curve to connect adjacent segments, ensure the smoothness and continuity of the transition. During this process, the key parameters include the starting and ending point positions, direction vectors, and the change amount of local curvature of each segment. By optimizing and adjusting these parameters, make the adjacent segment transitions natural and seamless, so that the obtained line is the fitted optimization line segment of the zigzag transition of the drone, which can provide a smooth and accurate trajectory in the flight path, and finally obtain the fitted optimization line segment of the zigzag transition of the drone.

[0164] The above - mentioned are only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for automatic mapping of route points based on RTK, characterized in that: The following steps are involved: Step S1: perform high-precision real-time geographic positioning of key points on the drone route through an RTK positioning device to obtain geographic positioning coordinate data corresponding to each key point on the drone route, wherein the geographic positioning coordinate data includes latitude and longitude coordinates, vertical height, and elevation direction; transmit the geographic positioning coordinate data corresponding to each key point on the drone route to the drone terminal device through a wireless transmission device, and use the drone terminal device to perform local plane coordinate system conversion on the geographic positioning coordinate data corresponding to each key point on the drone route to generate the corresponding drone route coordinate point data in the terminal local plane coordinate system; Step S2: automatically draw the route point path for the drone route coordinate point data corresponding to the local plane coordinate system of the terminal to generate the drone RTK point initial route map; perform shape and direction recognition analysis on each section of the automatic fitting route trajectory in the drone RTK point initial route map to obtain the trajectory shape and trajectory direction corresponding to each section of the drone RTK automatic fitting route; Based on the trajectory shape and trajectory direction corresponding to each section of the UAV RTK automatic fitting route, the transition tortuosity measurement of the corresponding automatic fitting route trajectory in the initial route map of the UAV RTK point is calculated to obtain the trajectory transition tortuosity corresponding to each section of the UAV RTK automatic fitting route; Step S3: Based on the trajectory transition tortuosity corresponding to each section of the UAV RTK automatic fitting route, the corresponding automatic fitting route trajectory is compared and judged to obtain the UAV RTK straight transition route segment and the UAV RTK tortuous transition route segment; the UAV RTK straight transition route segment and the UAV RTK tortuous transition route segment in the UAV RTK point initial route map are subjected to route error fitting smoothing optimization to generate a UAV RTK route optimization map; Step S4: The UAV RTK route optimization diagram is visualized through a friendly graphical interface in the UAV terminal device to generate a real-time route visualization diagram of the UAV RTK points.

2. The method for automatic line point mapping based on RTK according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining a digital terrain model and a digital surface model corresponding to the UAV navigation route, and performing a spatial structure topological analysis on the UAV navigation route based on the digital terrain model and the digital surface model to generate a spatial structure topological model of the UAV route terrain; Step S12: Calculate the terrain slope change rate of the drone navigation route corresponding to the drone route in the drone route terrain spatial structure topology model to obtain the terrain slope change rate corresponding to the drone navigation route at different coordinate positions; determine the route terrain support point of the drone navigation route corresponding to the drone route in the drone route terrain spatial structure topology model based on the terrain slope change rate corresponding to the drone navigation route at different coordinate positions to obtain the drone route terrain support point; Step S13: Obtain the corresponding environmental obstacle location area in the drone route terrain spatial structure topological model, and perform route obstacle avoidance turning point constraint analysis on the drone navigation route corresponding to the drone route terrain spatial structure topological model based on the environmental obstacle location area to obtain the drone route obstacle avoidance turning point; Step S14: performing spatial segmentation of the route network of the drone navigation route corresponding to the drone route terrain spatial structure topological model to obtain the drone navigation route spatial segmentation network; performing route overlapping intersection recognition analysis on the drone navigation route spatial segmentation network to obtain the drone route overlapping intersection; Step S15: Determine the terrain support points of the drone route, the obstacle avoidance turning points of the drone route, and the overlapping intersections of the drone route as key points on the drone route, and perform high-precision real-time geographic positioning of the key points on the drone route through RTK positioning equipment to obtain geographic positioning coordinate data corresponding to each key point on the drone route, wherein the geographic positioning coordinate data includes latitude and longitude coordinates, vertical height, and elevation direction; Step S16: The geographic positioning coordinate data corresponding to each key point on the drone route is transmitted to the drone terminal device through a wireless transmission device, and the drone terminal device is used to convert the geographic positioning coordinate data corresponding to each key point on the drone route into a local plane coordinate system to generate the corresponding drone route coordinate point data in the terminal local plane coordinate system.

3. The method for automatic line point mapping based on RTK according to claim 2, characterized in that: The method of using the drone terminal device to convert the geographic positioning coordinate data corresponding to each key point on the drone route into a local plane coordinate system in step S16 includes the following steps: Use the UAV terminal device to obtain the corresponding device virtual approximate local plane coordinate system; The UAV terminal device is used to reconstruct the coordinate translation of the geographic positioning coordinate data corresponding to each key point on the UAV route, so as to obtain the latitude and longitude plane reconstruction coordinates, vertical height plane reconstruction coordinates and elevation direction plane reconstruction coordinates corresponding to each key point on the UAV route; Based on the virtual approximate local plane coordinate system of the device, the latitude and longitude plane reconstructed coordinates, vertical height plane reconstructed coordinates and elevation direction plane reconstructed coordinates corresponding to each key point on the UAV route are synchronously fused and transformed across domain coordinate systems to generate the corresponding UAV route coordinate point data in the local plane coordinate system of the terminal.

4. The method for automatic line point mapping based on RTK according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing coordinate normalization processing on the coordinate point data of the drone line corresponding to the local plane coordinate system of the terminal to obtain the standardized point data of the drone line corresponding to the local plane coordinate system of the terminal; Step S22: Calculate the point distance and azimuth between different drone route points in the corresponding drone route standardized point data in the local plane coordinate system of the terminal to obtain the geometric curve distance and geometric azimuth between different drone route points in the local plane coordinate system of the terminal; Step S23: Obtain the UAV route flight constraints, and automatically draw the route point path for the geometric curve distance and geometric azimuth between different UAV route points in the terminal local plane coordinate system based on the UAV route flight constraints, so as to generate the UAV RTK point initial route map; Step S24: performing shape and direction recognition analysis on each segment of the automatic fitting route in the initial route map of the UAV RTK point, and obtaining the trajectory shape and trajectory direction corresponding to each segment of the UAV RTK automatic fitting route; Step S25: Based on the trajectory shape and trajectory direction corresponding to each section of the UAV RTK automatic fitting route, the transition tortuosity measurement of the corresponding automatic fitting route trajectory in the UAV RTK point initial route map is calculated to obtain the trajectory transition tortuosity corresponding to each section of the UAV RTK automatic fitting route.

5. The method for automatic line point mapping based on RTK according to claim 4, characterized in that: Step S25 includes the following steps: Step S251: based on the trajectory direction corresponding to each section of the UAV RTK automatic fitting route, a quantitative analysis of the direction angle offset of the corresponding automatic fitting route trajectory in the UAV RTK point initial route map is performed to obtain the trajectory direction angle offset between each section of the UAV fitting route; Step S252: performing a trajectory direction change rate statistical calculation on the trajectory direction angle offset between each segment of the UAV fitting route, and obtaining the trajectory direction change rate corresponding to each segment of the UAV RTK automatic fitting route; Step S253: calculating the trajectory transition curvature of the corresponding automatic fitting route trajectory in the initial route map of the UAV RTK point based on the trajectory direction change rate corresponding to each section of the UAV RTK automatic fitting route, and obtaining the trajectory direction transition curvature corresponding to each section of the UAV RTK automatic fitting route; Step S254: Based on the trajectory shape corresponding to each section of the UAV RTK automatic fitting route and the trajectory direction transition curvature, the trajectory transition tortuosity calculation formula is used to calculate the transition tortuosity of the corresponding automatic fitting route trajectory in the UAV RTK point initial route map to obtain the trajectory transition tortuosity corresponding to each section of the UAV RTK automatic fitting route.

6. The method for automatic line point mapping based on RTK according to claim 5, characterized in that: Step S253 includes the following steps: Extract the route track points of the automatically fitted route track corresponding to the initial route map of the UAV RTK point, and obtain the route track point set corresponding to each section of the UAV RTK automatically fitted route; Based on the trajectory direction change rate corresponding to each section of the UAV RTK automatic fitting line, the corresponding line trajectory point set is analyzed for the trajectory direction angle increment, and the trajectory direction angle increment data corresponding to each section of the UAV RTK automatic fitting line is obtained; Smoothing the track direction angle increment data corresponding to each section of the UAV RTK automatic fitting line, and obtaining the track angle increment transition smoothing data corresponding to each section of the UAV RTK automatic fitting line; Based on the trajectory angle incremental transition smoothing data corresponding to each section of the UAV RTK automatic fitting line, the trajectory transition curvature of the corresponding automatic fitting line trajectory in the initial route map of the UAV RTK point is calculated to obtain the trajectory direction transition curvature corresponding to each section of the UAV RTK automatic fitting line.

7. The method for automatic line point mapping based on RTK according to claim 5, characterized in that: The calculation formula of the trajectory transition tortuosity in step S254 is specifically: In the formula, C j is the trajectory transition tortuosity corresponding to the jth segment of the UAV RTK automatic fitting line, j is the item index parameter of the UAV RTK automatic fitting line, t1 is the trajectory start time corresponding to the UAV RTK automatic fitting line, t2 is the trajectory end time corresponding to the UAV RTK automatic fitting line, t is the time variable parameter, κ j (t) is the transition curvature of the trajectory direction of the jth segment of the UAV RTK automatic fitting line at time t, is the average curvature of the jth segment of the UAV RTK automatic fitting line corresponding to the time interval [t1, t2], p is the weighted index of curvature change, α is the weighted coefficient of curvature change rate, β is the weighted coefficient of trajectory shape influence, η j (t) is the trajectory shape and size of the jth segment UAV RTK automatic fitting line at time t, and ξ is the correction coefficient of the trajectory transition tortuosity.

8. The method for automatic route point mapping based on RTK according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing line comparison and judgment on the trajectory transition tortuosity corresponding to each section of the UAV RTK automatic fitting line according to the preset trajectory transition tortuosity threshold value; if the corresponding trajectory transition tortuosity is less than the preset trajectory transition tortuosity threshold value, the corresponding UAV RTK automatic fitting line is determined as a UAV RTK straight transition line segment; if the corresponding trajectory transition tortuosity is greater than or equal to the preset trajectory transition tortuosity threshold value, the corresponding UAV RTK automatic fitting line is determined as a UAV RTK tortuous transition line segment; Step S32: performing straight-line smoothing optimization on the UAV RTK straight transition line segment in the UAV RTK point initial line map to obtain the UAV straight transition smoothing optimized line segment; Step S33: performing segmented fitting optimization of the zigzag transition route segment of the UAV RTK in the initial route map of the UAV RTK point to obtain the zigzag transition fitting optimization route segment of the UAV; Step S34: Based on the straight transition smooth optimization route segment of the UAV and the tortuous transition fitting optimization route segment of the UAV, the route matching and synchronous optimization of the initial route map of the UAV RTK points are performed to generate the UAV RTK route optimization map.

9. The method for automatic line point mapping based on RTK according to claim 8, characterized in that: Step S32 includes the following steps: Step S321: performing straightness index analysis on the UAV RTK straight transition line segment in the UAV RTK point initial line map to obtain the straightness index of the UAV straight transition line segment; Step S322: performing line straightness constraint analysis on the corresponding UAV RTK straight transition line segment based on the straightness index of the UAV straight transition line segment to obtain the UAV RTK straight constraint line segment; Step S323: performing line curvature smoothing processing on the UAV RTK straight constraint line segment to obtain the UAV RTK straight curvature smoothed line segment; Step S324: Acquire the real-time flight process data of the UAV, and perform local fine-tuning, correction and optimization on the UAV RTK straightening and curvature smoothing route segment based on the real-time flight process data of the UAV to obtain the UAV straightening transition smoothing optimization route segment.

10. The method for automatic line point mapping based on RTK according to claim 8, characterized in that: Step S33 includes the following steps: Performing curvature anomaly detection on the trajectory points of the zigzag transition line segment of the UAV RTK in the initial route map of the UAV RTK point position, and obtaining the path curvature anomaly index corresponding to the trajectory points of the zigzag transition line segment of the UAV; Based on the path curvature anomaly index corresponding to the trajectory point of the UAV zigzag transition line segment, the corresponding UAV RTK zigzag transition line segment is processed abnormally to obtain the zigzag transition abnormal line segment of each UAV; Perform least square local fitting optimization on the abnormal line segments of each UAV's zigzag transition to obtain the optimized line segments of each UAV's zigzag transition; For each UAV's zigzag transition local fitting optimization route segment, adjacent segment transition connection fitting is performed to obtain the UAV's zigzag transition fitting optimization route segment.

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