A method for unmanned aerial vehicle complex scene cruising attitude control

Abstract

This invention discloses a method for cruise attitude control in complex scenarios for unmanned aerial vehicles (UAVs), relating to the field of UAV technology. The invention acquires cruise attitude data of the UAV as it travels along a target cruise path in real time; models the cruise attitude data to obtain a cruise attitude data model; sets up a global perception unit for the UAV; acquires a cruise attitude data analysis model of the cruise attitude data model in real time; analyzes the cruise attitude data based on the cruise attitude data analysis model to obtain the corresponding observation location nodes; then obtains the observation cruise path for the observation location nodes; corrects the observation cruise path; and effectively improves the accuracy of cruise attitude data monitoring.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and more specifically to a method for cruise attitude control of UAVs in complex scenarios. Background Technology

[0002] Unmanned aerial vehicles (UAVs), also known as drones, are unmanned aircraft controlled by radio remote control equipment and their own program control devices. The term "UAV" is actually a general term for unmanned aerial vehicles, which, from a technical perspective, can be categorized as: unmanned fixed-wing aircraft, unmanned vertical takeoff and landing aircraft, unmanned airships, unmanned helicopters, unmanned multi-rotor aircraft, unmanned paragliders, etc. Compared to manned aircraft, UAVs have advantages such as small size, low cost, ease of use, low requirements for the combat environment, and strong battlefield survivability. As drone operation scenarios gradually become more complex, diversified, and all-encompassing, the traditional standardized operation mode in open airspace can no longer meet the actual operation needs. Currently, drones often need to complete long-distance, continuous autonomous cruise missions in complex and harsh environments such as urban canyons, mountain valleys, low-altitude turbulence, multi-obstacle building clusters, wind, rain, and low temperatures. However, traditional technologies only collect basic attitude data of UAVs without achieving spatiotemporal fusion. This makes it impossible to accurately bind temporal attitude changes with spatial flight position, and it is difficult to quantify instantaneous attitude deviations caused by wind disturbances and airflow disturbances, resulting in low attitude data accuracy and poor real-time performance. Secondly, existing perception methods have limited sensing range, mostly relying on unidirectional imaging acquisition, which cannot achieve full-domain scene coverage of the cruise path. It is also difficult to accurately identify static obstacles and dynamic interference sources in the airspace, resulting in insufficient scene perception completeness and inaccurate attitude data analysis. Consequently, it is impossible to accurately control the attitude data.

[0003] Therefore, the present invention provides a method for cruise attitude control of unmanned aerial vehicles in complex scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a method for cruise attitude control of unmanned aerial vehicles in complex scenarios, so as to solve the problems of the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for cruise attitude control of unmanned aerial vehicles (UAVs) in complex scenarios, the method comprising the following steps: Step S1: Acquire real-time cruise attitude data of the UAV as it travels along the preset target cruise path; model the cruise attitude data to obtain a cruise attitude data model; Step S2: Set up the UAV's global perception unit; analyze the cruise attitude data model by collecting cruise attitude data in real time; Step S3: Analyze the cruise attitude data based on the cruise attitude data analysis model to obtain the observation and acquisition position nodes corresponding to the cruise attitude data; then obtain the observation and acquisition position nodes for the cruise path to be observed. Step S4: Correct the cruise path to be observed.

[0006] Furthermore, the process of acquiring real-time cruise attitude data of the UAV as it travels along a preset target cruise path, and then modeling this cruise attitude data to obtain a cruise attitude data model, includes: Set up attitude data acquisition nodes for the UAV. The UAV uses the GPS system to collect the acquisition location nodes of each attitude data acquisition node on the target cruise path in real time, thereby obtaining the cruise attitude data of the acquisition location nodes. Obtain the center node of the UAV and map the center node in parallel to the origin of the 3D model formed by the target cruise path, and denote it as the cruise origin; A three-dimensional spatial model of the cruise origin is established, and the acquired location nodes are mapped to the three-dimensional spatial model to generate a three-dimensional spatial position model; then, a cruise attitude data model of the cruise attitude data in the three-dimensional spatial position model is constructed.

[0007] Furthermore, the process of constructing a cruise attitude data model that represents a three-dimensional spatial location model of the cruise attitude data includes: Map the cruise attitude data to the corresponding acquisition location nodes of the 3D spatial location model; A cruise attitude data model is constructed by taking the acquisition location node as the origin, the acquisition time point as the x-axis, and the cruise attitude data as the y-axis to construct a three-dimensional model of the cruise attitude data in spatial location.

[0008] Furthermore, the process of setting up the drone's global perception unit includes: A global perception unit is set in the GPS system of the drone, and the global perception unit is equipped with a camera subunit and an imaging subunit; The camera subunit is equipped with cameras in four directions: east, south, west, and north; the cameras in the four directions form a square, which is referred to as the camera square. When the center point of the camera square coincides with the center node of the cruise attitude data model, the acquisition position point farthest from the preset acquisition direction of each camera is obtained and recorded as the camera movement radius of the corresponding camera in the acquisition direction. The camera movement range in the corresponding acquisition direction is obtained based on the camera movement radius; the camera range with the camera movement radius is obtained based on the camera movement range. The camera movement range is equipped with a sampling direction control point; the sampling direction control point is used to control the control position direction of each camera; The imaging subunit is used to divide the camera range into control position direction ranges to obtain corresponding direction control acquisition ranges; then the camera images the first range of the control position direction acquisition direction control acquisition range according to the control position direction acquisition direction.

[0009] Furthermore, the process of real-time acquisition of cruise attitude data model and analysis model includes: The UAV, based on the global perception unit, collects the first range image of the cruise scene model at the central node in real time along the target cruise path; the first range image is stitched together according to the acquisition direction control point of the acquisition range to form the corresponding camera range image. The imaging of the camera range is stitched together according to the midpoint of the corresponding camera movement to construct a cruise scene model; based on the central node, the cruise attitude data model and the cruise scene model are fused to generate a cruise attitude data analysis model in real time.

[0010] Furthermore, the process of analyzing the cruise attitude data based on the cruise attitude data analysis model to obtain the observation and acquisition position nodes corresponding to the cruise attitude data includes: Obstacles around the target cruise route are obtained based on the cruise attitude data analysis model; nodes at each acquisition location that are perpendicular to the surrounding obstacles are obtained based on the cruise attitude data model, and then mapped to the spatial three-dimensional model in parallel to obtain obstacle nodes; The system acquires the straight-line distance between the acquisition location node and each obstacle node in real time and records it as the current straight-line distance. A straight-line distance threshold is set, and the current straight-line distance that is less than the threshold is recorded as the first current straight-line distance; otherwise, it is recorded as the abnormal current straight-line distance. The corresponding acquisition location node is then marked as an abnormal acquisition location node. Acquire the cruise attitude data of the acquisition location nodes, and set the instantaneous maximum cruise attitude data corresponding to the acquisition time point; then, based on the spatial three-dimensional model, obtain the acquisition location node corresponding to the instantaneous maximum cruise attitude data, and record it as the maximum acquisition location node; Obtain the straight-line distance between the node with the maximum acquisition position and each obstacle node, and record it as the maximum amplitude distance; The acquisition location node whose maximum amplitude distance is less than or equal to any first current straight-line distance is recorded as an abnormal acquisition location node; then the cruise attitude data corresponding to the abnormal acquisition location node is marked as abnormal cruise attitude data. Conversely, the nodes at the locations to be observed and collected are marked.

[0011] Furthermore, the process of obtaining the acquisition location node corresponding to the instantaneous maximum cruise attitude data based on the spatial three-dimensional model, and denoting it as the maximum acquisition location node, includes: The nodes of the cruise attitude data are denoted as data nodes; the three-dimensional data nodes mapped to the spatial three-dimensional model are obtained based on the cruise attitude data model; similarly, the three-dimensional data nodes corresponding to the data nodes of the instantaneous maximum cruise attitude data are obtained and denoted as the maximum three-dimensional data nodes. Obtain the maximum offset data between the maximum 3D data node and the 3D data node; update the acquisition position node based on the maximum offset data to obtain the maximum acquisition position node.

[0012] Furthermore, the process of obtaining the observation cruise path for the nodes at the locations to be observed includes: The abnormal acquisition location node generates an abnormal signal and sends it to the preset control unit; Abnormal cruise attitude data from abnormal acquisition location nodes is obtained through the control unit, and then normal cruise attitude data is obtained by controlling the abnormal cruise attitude data through the control unit. Based on the collection time point, connect the nodes of the same observation location with straight lines to generate the corresponding time-observation location node model; The straight-line trajectory of each observation location node of the UAV is obtained based on the time-observation location node model, and is denoted as the observation cruise path. Furthermore, the process of correcting the observed cruise path includes: Set the maximum yaw value of the target cruise path, and then obtain the yaw range of the target cruise path based on the maximum yaw value; Determine whether the cruise path to be observed is within the yaw range; If not, then acquire the observation location nodes where the cruise path to be observed is not within the yaw range, and generate a correction signal to send to the control unit; Conversely, no action is taken; The cruise attitude data of the node to be observed and collected is adjusted by the control unit.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention acquires the cruise attitude data of the UAV as it travels along the target cruise path in real time; models the cruise attitude data to obtain a cruise attitude data model; sets up a global perception unit for the UAV; acquires a cruise attitude data analysis model of the cruise attitude data model in real time; analyzes the cruise attitude data based on the cruise attitude data analysis model to obtain the observation and acquisition position nodes corresponding to the cruise attitude data; and then obtains the observation and acquisition position nodes to carry out the observation cruise path. This effectively solves the defects of traditional modeling, such as lack of spatial correlation, inaccurate attitude offset quantification, and missed or false risk judgments, and greatly improves the accuracy and real-time performance of UAV attitude anomaly identification in complex disturbance scenarios. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0015] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, a method for cruise attitude control of a UAV in complex scenarios includes the following steps: Step S1: Acquire real-time cruise attitude data of the UAV as it travels along the preset target cruise path; model the cruise attitude data to obtain a cruise attitude data model; Step S2: Set up the UAV's global perception unit; analyze the cruise attitude data model by collecting cruise attitude data in real time; Step S3: Analyze the cruise attitude data based on the cruise attitude data analysis model to obtain the observation and acquisition position nodes corresponding to the cruise attitude data; then obtain the observation and acquisition position nodes for the cruise path to be observed. Step S4: Correct the cruise path to be observed.

[0018] Further clarification is needed for step S1, specifically the real-time acquisition of the UAV's cruise attitude data as it traverses the preset target cruise path; the process of modeling the cruise attitude data to obtain a cruise attitude data model includes: Step S101: Set the attitude data acquisition nodes of the UAV. The UAV collects the acquisition location nodes of each attitude data acquisition node on the target cruise path in real time based on the GPS system, and then obtains the cruise attitude data of the acquisition location nodes. Step S102: Obtain the center node of the UAV and map the center node in parallel to the origin of the three-dimensional model formed by the target cruise path, and record it as the cruise origin. Step S103: Establish a spatial three-dimensional model of the cruise origin and map the acquired position nodes to the spatial three-dimensional model to generate a spatial position three-dimensional model; then construct a cruise attitude data model of the cruise attitude data in the spatial position three-dimensional model. Further clarification is needed for step S103, which involves constructing a cruise attitude data model that represents the cruise attitude data in a three-dimensional spatial position model, including: Step S1031: Map the cruise attitude data to the corresponding acquisition location nodes of the 3D spatial position model; Step S1032: Construct a cruise attitude data model in a three-dimensional spatial location model with the acquisition location node as the origin, the acquisition time point as the x-axis, and the cruise attitude data as the y-axis.

[0019] Further clarification is needed for steps S101-S103, whereby the attitude data acquisition nodes include, but are not limited to, pitch angle acquisition nodes (acquiring pitch angles and pitch angular velocities of the aircraft's head-up and head-down directions), roll angle acquisition nodes (acquiring roll angles and roll angular velocities of the aircraft's left and right roll directions), yaw angle acquisition nodes (acquiring yaw angles and yaw angular velocities of the aircraft's nose-up and yaw velocities), etc. The UAV's center node is used to represent the geometric center of the UAV body. The acquisition position node is used to represent the real-time spatial positioning displayed by the GPS system, i.e., the geodetic GPS point location. Furthermore, the process of establishing the spatial three-dimensional model includes: firstly, reading the actual spatial positioning of the central node based on the GPS system, and then mapping the central node parallelly to the target cruise path, which is O (0, 0, 0), and constructing a northeast-central rectangular coordinate system with the cruise origin; wherein the X-axis is eastward, the Y-axis is northward, and the Z-axis is the spatial three-dimensional model vertically upward; wherein, the parallel mapping is used to indicate that only the actual spatial positioning corresponding to the central node is offset parallelly, without applying rotation or scaling transformation, to ensure that the spatial orientation and scale corresponding to the cruise origin and the central node of the UAV are completely consistent; then, each acquisition location node is mapped; for example, the actual spatial positioning of the cruise origin in the GPS system is latitude 30.5200°N, longitude 117.0200°E, and altitude 42. The location 00m is set as the cruise origin O(0,0,0) after parallel mapping. The real-time spatial positioning of the collected location node in the GPS system is then determined to be 30.5202°N, 117.0203°E, and an altitude of 43.50m. The difference between the real-time spatial positioning of the collected location point and the GPS real-time spatial positioning of the cruise origin is obtained. The azimuth rotation matrix of the ground where the origin is located is then calculated, and the geocentric offset is converted into intuitive eastward, northward, and upward measurements in meters. The calculation method is not detailed here. The coordinates of the collected location node are then converted to obtain the spatial three-dimensional coordinates relative to the cruise origin: E=32.6m, N=22.1m, U=1.5m. These coordinates (32.6m, 22.1m, 1.5m) are then parallel mapped in the spatial three-dimensional model. Furthermore, the collected time point corresponds to the real-time collected time point. Furthermore, the target cruise path is typically obtained through historical scene data.

[0020] Further clarification is needed for step S2, which includes the process of setting up the UAV's global perception unit: Step S201: Set up a global perception unit in the GPS system of the UAV, wherein the global perception unit is equipped with a camera subunit and an imaging subunit; Step S202: The camera subunit is equipped with cameras in the four directions of east, south, west, and north; wherein, the cameras in the four directions form a square, which is denoted as the camera square; Step S203: When the center point of the camera square coincides with the center node of the cruise attitude data model, obtain the acquisition position point that is farthest from the preset acquisition direction of each camera, and record it as the camera movement radius of the corresponding camera in the acquisition direction. Step S204: Obtain the camera movement range in the corresponding acquisition direction based on the camera movement radius; obtain the camera range with the camera movement radius based on the camera movement range; Step S205: The camera movement range is equipped with a sampling direction control point; the sampling direction control point is used to control the control position direction of each camera; Step S206: The imaging subunit is used to divide the camera range into control position direction ranges to obtain corresponding direction control acquisition ranges; then the camera images the first range of the control position direction acquisition direction control acquisition range. Further clarification is needed for steps S201-S207, where the camera movement radius is the farthest acquisition position node in the acquisition direction; this allows for better acquisition of the range imaging of the optimal acquisition position node; the farthest acquisition position point from each camera's preset acquisition direction is obtained only when the camera square coincides with the center node of the cruise attitude data model, and then acquisition is performed; wherein, the acquisition direction is the direction that the camera can acquire imaging in advance, including several directions. Further, the process of setting the camera range includes: marking the semicircular arcs intersecting at the midpoint of the camera movement as the camera movement range using the camera movement radius, and then completing the camera movement range into a circle using the camera movement radius to obtain the camera range; the acquisition direction control point is used to represent the control point within the camera range; when the camera moves to the acquisition direction control point, the corresponding control position direction is towards the center of the circle; the process of obtaining the direction control acquisition range includes: obtaining the diameter of the camera range using the control position direction, denoted as the acquisition diameter, setting the outer radius of the acquisition diameter, the outer radius being used to represent the radius perpendicular to the acquisition radius; the outer radius is the same within the camera range to generate the direction control acquisition range. Further, the first range image acquired by the camera is a three-dimensional image.

[0021] It should be further explained that the process of real-time acquisition of cruise attitude data model and analysis model includes: Step S208: Based on the global perception unit, the UAV collects real-time images of the cruise scene model at the central node along the target cruise path. Step S209: The first range image is stitched together according to the acquisition direction control points of the corresponding direction control acquisition range to form the corresponding camera range image; Step S210: The images of the camera range are stitched together according to the midpoint of the corresponding camera movement to construct a cruise scene model; Step S211: Based on the central node, fuse the cruise attitude data model and the cruise scenario model to generate a cruise attitude data analysis model in real time; Further clarification is needed for steps S208-S210. The image stitching can be performed layer by layer using three core algorithms: a feature-matching-based stitching algorithm, a Poisson fusion-based image stitching algorithm, and a deep learning U-Ne-based semantic stitching algorithm. These will not be elaborated upon here. Furthermore, the model fusion involves aligning the center node with the camera center point, and then displaying and fusing the cruise attitude data model within the cruise scene model to generate a cruise attitude data analysis model.

[0022] Further clarification is needed for step S3, which involves analyzing the cruise attitude data based on the cruise attitude data analysis model to obtain the observed acquisition position nodes corresponding to the cruise attitude data, including: Step S301: Obtain surrounding obstacles along the target cruise route based on the cruise attitude data analysis model; Step S302: Based on the cruise attitude data model, obtain the nodes of each acquisition location that are perpendicular to the surrounding obstacles, and map them in parallel to the spatial three-dimensional model to obtain the obstacle nodes; Step S303: Real-time acquisition of the straight-line distance between the acquisition location node and each obstacle node, recorded as the current straight-line distance; Step S304: Set a straight-line distance threshold, and record the current straight-line distance that is less than the straight-line distance threshold as the first current straight-line distance; otherwise, record it as an abnormal current straight-line distance; and mark the corresponding acquisition location node as an abnormal acquisition location node; Step S305: Obtain the cruise attitude data of the acquisition location node, and set the instantaneous maximum cruise attitude data corresponding to the acquisition time point; then obtain the acquisition location node corresponding to the instantaneous maximum cruise attitude data based on the spatial three-dimensional model, and record it as the maximum acquisition location node; Step S306: Obtain the straight-line distance between the maximum acquisition position node and each obstacle node, and record it as the maximum amplitude distance; Step S307: Record the acquisition position nodes whose maximum amplitude distance is less than or equal to any first current straight-line distance as abnormal acquisition position nodes; then mark the cruise attitude data corresponding to the abnormal acquisition position nodes as abnormal cruise attitude data; Step S308: Conversely, mark the node as the location to be observed and collected.

[0023] Further clarification is needed for steps S301-S308. The surrounding obstacles refer to obstacles around the target cruise route, including but not limited to high-rise buildings, transmission towers, high-voltage lines, wind turbines, bridges, mountains, trees, birds, other low-altitude aircraft, construction cranes, and high-altitude floating objects—static and dynamic spatial entities posing a collision risk. The node perpendicular to the surrounding obstacles at the acquisition location node represents a point where a perpendicular line is drawn from any acquisition location node in the three-dimensional spatial model to the three-dimensional contour surface of the surrounding obstacles, perpendicular to the tangent plane at the intersection point. The intersection point of the perpendicular line and the obstacle tangent plane is the obstacle node corresponding to that acquisition location node. The node's spatial positioning is consistent with the actual spatial positioning of the aforementioned GPS system, and is then converted into an obstacle node based on the three-dimensional spatial model. The current straight-line distance is the straight-line distance between the acquisition location node and the obstacle. Furthermore, the instantaneous maximum cruise attitude data represents the cruise attitude data corresponding to the maximum amplitude of the cruise attitude data at the same acquisition time point. If the maximum amplitude distance is less than or equal to any current straight-line distance, it indicates that the drone's fuselage has intruded into the safety buffer zone, posing a collision risk. Therefore, this data acquisition location node is marked as an abnormal data acquisition location node. Furthermore, the straight-line distance threshold refers to a threshold for straight-line distance, i.e., the threshold for the current straight-line distance. Furthermore, the distance is calculated using the formula for the straight-line distance between two points.

[0024] Further clarification is needed for step S305, specifically the process of obtaining the acquisition location node corresponding to the instantaneous maximum cruise attitude data based on the spatial three-dimensional model, denoted as the maximum acquisition location node, which includes: Step S3051: Record the nodes of the cruise attitude data as data nodes; based on the cruise attitude data model, obtain the 3D data nodes mapped to the spatial 3D model; Step S3052: Similarly, obtain the three-dimensional data node corresponding to the data node of the instantaneous maximum cruise attitude data, and denot it as the maximum three-dimensional data node; Step S3053: Obtain the maximum offset data between the largest 3D data node and the largest 3D data node; Step S3054: Update the acquisition location node based on the maximum offset data to obtain the maximum acquisition location node.

[0025] Further constraints are needed for steps S3051-S3054. If the coordinates of the three-dimensional data node are (33.6, 23.1, 1.5), and the coordinates of the maximum three-dimensional data node are (33.4, 22.7, 1.8), then the maximum offset data is 33.4−33.6=-0.2m, 22.7−23.1=-0.4m, 1.8−1.5=0.3m; the coordinates of the maximum acquisition position node are 32.6-0.2=32.4m, 22.1-0.4=21.7m, 1.5+0.3=1.8m; that is, (32.4, 21.7, 1.8). Here, the cruise attitude data is transformed from two-dimensional to three-dimensional so that the attitude change data has spatial position attributes.

[0026] It should be further explained that the process of obtaining the observation and collection location node's navigation path includes: Step S309: The abnormal acquisition location node generates an abnormal signal and sends it to the preset control unit; Step S310: Obtain abnormal cruise attitude data from abnormal acquisition location nodes through the control unit, and then adjust the abnormal cruise attitude data through the control unit to obtain normal cruise attitude data. Step S311: Connect the nodes of the same observation location with straight lines according to the acquisition time point to generate the corresponding time-observation location node model; Step S312: Based on the time-observable acquisition location node model, obtain the straight-line trajectory of each observation acquisition location node of the UAV, and record it as the observation cruise path; Further limitations are needed for steps S309-S312: first, the abnormal cruise attitude data of the abnormal acquisition location nodes are adjusted so that the UAV can fly normally on the target cruise route; then, the acquisition location nodes to be observed are connected in a straight line according to time to generate the cruise path to be observed.

[0027] Further clarification is needed for step S4, which includes the process of correcting the observed cruise path: Step S401: Set the maximum yaw value of the target cruise path, and then obtain the yaw range of the target cruise path based on the maximum yaw value; Step S402: Determine whether the cruise path to be observed is within the yaw range; Step S403: If not, acquire the observation location nodes where the cruise path to be observed is not within the yaw range, and generate a correction signal to send to the control unit; Step S404: Conversely, do not perform any processing; Step S405: Adjust the cruise attitude data of the node to be observed and collected through the control unit.

[0028] Further clarification is needed for steps S401-S405, namely, that the maximum yaw value is not the current straight-line distance, but the distance to the target cruise path; the yaw range is the rectangle enclosed by the maximum yaw values ​​on both sides of the target cruise path; and the control unit is used to set up manual system operation of the UAV.

[0029] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling a cruising attitude of a UAV in a complex scene, characterized in that, The method includes the following steps: Step S1: Acquire real-time cruise attitude data of the UAV as it travels along the preset target cruise path; model the cruise attitude data to obtain a cruise attitude data model; Step S2: Set up the UAV's global perception unit; analyze the cruise attitude data model by collecting cruise attitude data in real time; Step S3: Analyze the cruise attitude data based on the cruise attitude data analysis model to obtain the observation and acquisition position nodes corresponding to the cruise attitude data; then obtain the observation and acquisition position nodes for the cruise path to be observed. Step S4: Correct the cruise path to be observed.

2. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 1, characterized in that, Real-time acquisition of the drone's cruise attitude data as it travels along a preset target cruise path; The process of modeling cruise attitude data to obtain a cruise attitude data model includes: Set up attitude data acquisition nodes for the UAV. The UAV uses the GPS system to collect the acquisition location nodes of each attitude data acquisition node on the target cruise path in real time, thereby obtaining the cruise attitude data of the acquisition location nodes. Obtain the center node of the UAV and map the center node in parallel to the origin of the 3D model formed by the target cruise path, and denote it as the cruise origin; A three-dimensional spatial model of the cruise origin is established, and the acquired location nodes are mapped to the three-dimensional spatial model to generate a three-dimensional spatial position model; then, a cruise attitude data model of the cruise attitude data in the three-dimensional spatial position model is constructed.

3. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 2, characterized in that, The process of constructing a cruise attitude data model that represents a three-dimensional spatial location of the cruise attitude data includes: Map the cruise attitude data to the corresponding acquisition location nodes of the 3D spatial location model; A cruise attitude data model is constructed by taking the acquisition location node as the origin, the acquisition time point as the x-axis, and the cruise attitude data as the y-axis to construct a three-dimensional model of the cruise attitude data in spatial location.

4. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 3, characterized in that, The process of setting up the global perception unit for a drone includes: A global perception unit is set in the GPS system of the drone, and the global perception unit is equipped with a camera subunit and an imaging subunit; The camera subunit is equipped with cameras in four directions: east, south, west, and north; the cameras in the four directions form a square, which is referred to as the camera square. When the center point of the camera square coincides with the center node of the cruise attitude data model, the acquisition position point farthest from the preset acquisition direction of each camera is obtained and recorded as the camera movement radius of the corresponding camera in the acquisition direction. The camera movement range in the corresponding acquisition direction is obtained based on the camera movement radius; the camera range with the camera movement radius is obtained based on the camera movement range. The camera movement range is equipped with a sampling direction control point; the sampling direction control point is used to control the control position direction of each camera; The imaging subunit is used to divide the camera range into control position direction ranges to obtain corresponding direction control acquisition ranges; then the camera images the first range of the control position direction acquisition direction control acquisition range according to the control position direction acquisition direction.

5. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 4, characterized in that, The process of real-time acquisition and analysis of cruise attitude data models includes: The UAV, based on the global perception unit, collects the first range image of the cruise scene model at the central node in real time along the target cruise path; the first range image is stitched together according to the acquisition direction control point of the acquisition range to form the corresponding camera range image. The imaging of the camera range is stitched together according to the midpoint of the corresponding camera movement to construct a cruise scene model; based on the central node, the cruise attitude data model and the cruise scene model are fused to generate a cruise attitude data analysis model in real time.

6. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 5, characterized in that, The process of analyzing cruise attitude data based on a cruise attitude data analysis model to obtain the corresponding observation and acquisition location nodes includes: Obstacles around the target cruise route are obtained based on the cruise attitude data analysis model; nodes at each acquisition location that are perpendicular to the surrounding obstacles are obtained based on the cruise attitude data model, and then mapped to the spatial three-dimensional model in parallel to obtain obstacle nodes; The system acquires the straight-line distance between the acquisition location node and each obstacle node in real time and records it as the current straight-line distance. A straight-line distance threshold is set, and the current straight-line distance that is less than the threshold is recorded as the first current straight-line distance; otherwise, it is recorded as the abnormal current straight-line distance. The corresponding acquisition location node is then marked as an abnormal acquisition location node. Acquire the cruise attitude data of the acquisition location nodes, and set the instantaneous maximum cruise attitude data corresponding to the acquisition time point; then, based on the spatial three-dimensional model, obtain the acquisition location node corresponding to the instantaneous maximum cruise attitude data, and record it as the maximum acquisition location node; Obtain the straight-line distance between the node with the maximum acquisition position and each obstacle node, and record it as the maximum amplitude distance; The acquisition location node whose maximum amplitude distance is less than or equal to any first current straight-line distance is recorded as an abnormal acquisition location node; then the cruise attitude data corresponding to the abnormal acquisition location node is marked as abnormal cruise attitude data. Conversely, the nodes at the locations to be observed and collected are marked.

7. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 6, characterized in that, The process of obtaining the acquisition location node corresponding to the instantaneous maximum cruise attitude data based on the spatial three-dimensional model, and denoting it as the maximum acquisition location node, includes: The nodes of the cruise attitude data are denoted as data nodes; the three-dimensional data nodes mapped to the spatial three-dimensional model are obtained based on the cruise attitude data model; similarly, the three-dimensional data nodes corresponding to the data nodes of the instantaneous maximum cruise attitude data are obtained and denoted as the maximum three-dimensional data nodes. Obtain the maximum offset data between the maximum 3D data node and the 3D data node; update the acquisition position node based on the maximum offset data to obtain the maximum acquisition position node.

8. The method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 6, characterized in that, The process of obtaining the observation and collection location node and the observation cruise path includes: The abnormal acquisition location node generates an abnormal signal and sends it to the preset control unit; Abnormal cruise attitude data from abnormal acquisition location nodes is obtained through the control unit, and then normal cruise attitude data is obtained by controlling the abnormal cruise attitude data through the control unit. Based on the collection time point, connect the nodes of the same observation location with straight lines to generate the corresponding time-observation location node model; The straight-line trajectory of each observation location node of the UAV is obtained based on the time-observation location node model, and is denoted as the observation cruise path.

9. A method for cruise attitude control of an unmanned aerial vehicle in complex scenarios according to claim 8, characterized in that, The process of correcting the observed cruise path includes: Set the maximum yaw value of the target cruise path, and then obtain the yaw range of the target cruise path based on the maximum yaw value; Determine whether the cruise path to be observed is within the yaw range; If not, then acquire the observation location nodes where the cruise path to be observed is not within the yaw range, and generate a correction signal to send to the control unit; Conversely, no action is taken; The cruise attitude data of the node to be observed and collected is adjusted by the control unit.

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