Vertical fixed-wing unmanned aerial vehicle and aerial survey method thereof

Abstract

The invention discloses a vertical fixed-wing unmanned aerial vehicle, the vertical fixed-wing unmanned aerial vehicle comprises a vertical fixed-wing unmanned aerial vehicle body, the invention also discloses an aerial survey method of the vertical fixed-wing unmanned aerial vehicle, and the aerial survey method comprises primary aerial survey and advanced aerial survey; the automatic pilot is installed in the vertical fixed-wing unmanned aerial vehicle body and electrically connected with the battery reasonably arranged in the vertical fixed-wing unmanned aerial vehicle body, an effective method is provided for aerial survey of the vertical fixed-wing unmanned aerial vehicle body, and the aerial survey efficiency of the vertical fixed-wing unmanned aerial vehicle body is improved.

Description

Technical Field

[0001] The present invention discloses a vertical take-off and landing fixed-wing unmanned aerial vehicle and an aerial survey method thereof. Background Art

[0002] In China, the research and development and production of unmanned aerial vehicles (UAVs) were carried out in the early years, and large UAVs such as the "ASN" and "Tianyi" series were equipped. Key components and materials required for UAVs, such as carbon fiber materials, special plastics, lithium batteries, and magnetic materials, have complete industrial supporting facilities in places such as Shenzhen and Chengdu. China has a complete industrial chain for the development of UAVs and can achieve complete self-sufficiency in the product supply chain of UAV systems. Among them, the research on vertical take-off and landing fixed-wing UAVs is a type of UAV that effectively combines the vertical take-off and landing capabilities of multi-rotor UAVs and the high-efficiency cruising capabilities of fixed-wing UAVs. Due to its unique take-off / landing - cruising performance, the vertical take-off and landing fixed-wing UAV plays a crucial role in the field of military reconnaissance. However, the existing vertical take-off and landing fixed-wing UAVs lack an autopilot structure that is convenient for assembly during unmanned flight. At the same time, in the mounting frame of the UAV autopilot, the UAV pilot is limited and fixed by a positioning rod. During the flight of the UAV, due to the action of vibration force, the positioning rod is prone to rotate and change its position, resulting in the pilot falling off from the frame plate. There is a lack of a necessary aerial survey method during flight, which makes it easy for the UAV to generate incorrect information during the aerial survey process, resulting in the vertical take-off and landing fixed-wing UAV being unable to complete the aerial survey work smoothly. Summary of the Invention

[0003] In order to solve the existing problems, the present invention provides a vertical take-off and landing fixed-wing UAV, including a vertical take-off and landing fixed-wing UAV body. The vertical take-off and landing fixed-wing UAV body is equipped with a battery, and the battery is installed at the position of the fuselage of the vertical take-off and landing fixed-wing UAV body. An assembly box is installed on the bottom surface of the vertical take-off and landing fixed-wing UAV body, and an autopilot is installed in the assembly box. The fuselage of the vertical take-off and landing fixed-wing UAV body is inlaid with a charging port electrically connected to the battery, and an interface is inlaid on the bottom surface of the fuselage. The autopilot is connected with a power cord, and a socket for inserting into the interface is provided at the end of the power cord. Connecting pipes are provided at the four end corners on the top surface of the assembly box, and screw buttons are installed on the connecting pipes. The bottom surface of the vertical take-off and landing fixed-wing UAV body is provided with a mating connecting pipe with a pressing port, which is mutually buckled and screw-connected through the screw buttons.

[0004] The autopilot includes a mounting bracket, which includes a pilot mounting base plate, a cross-shaped guide rod, and a fixed frame. The pilot mounting base plate is provided with a first cross-shaped through hole, and the fixed frame is provided with a second cross-shaped through hole. The cross-shaped guide rod includes a positioning section and a fixing section. The fixing section is located inside the positioning section and is slidable. The positioning section sequentially passes through the first cross-shaped through hole and the second cross-shaped through hole, and the fixing section enters the inside of the fixed frame through the second cross-shaped through hole. The pilot mounting base plate is provided with a first through hole, the fixed frame is provided with a second through hole, and the positioning section is provided with a third through hole. A screw sequentially passes through the first through hole, the second through hole, and the third through hole and is fixed by matching the internal threads of the through holes to fix the pilot mounting base plate to the fixed frame.

[0005] A partition is provided inside the connecting pipe, and internal threaded pipes are embedded between both sides of the partition and the inner wall of the connecting pipe.

[0006] The screw-mounted buttons are arranged oppositely, and each of them includes a button and a stud connected to the button. The mating connecting pipe and the connecting pipe are respectively provided with corresponding openings, and the stud passes through the opening and is screwed into the internal threaded pipe.

[0007] The aerial survey method includes primary aerial survey and advanced aerial survey. The primary aerial survey includes the following steps: First step, dynamic aerial survey planning based on multi-modal sensor fusion: Integrate LiDAR and visual SLAM to construct a real-time three-dimensional point cloud map, automatically identify obstacles in the survey area and dynamically adjust the flight path; Use a neural network model to intelligently calculate the optimal flight height and resolution combination according to the terrain complexity; Second step, AR-enhanced interactive task planning: Overlay virtual flight tracks and real-scene images through the ground station AR glasses, support gesture adjustment of polygon vertices; Automatically generate a pre-flight health report, including the intelligent frequency pairing verification results of the drone and the camera; Third step, vertical takeoff and landing control in complex environments: Use multi-modal sensor fusion to achieve takeoff and landing on slopes of ±10°, enhancing the adaptability of mountain operations; During flight, adjust the camera exposure interval in real time (dynamic range of 0.3 - 3 seconds) to optimize the image quality; Fourth step, intelligent verification of task parameters: Automatically verify the rationality of the resolution-height combination, and output an error compensation scheme; Generate an aerial survey task feasibility report including three-dimensional terrain analysis.

[0008] The advanced aerial survey includes the following steps: First step, dynamic task scheduling and power optimization: Dynamically adjust the flight speed and altitude based on the battery health prediction model to maximize the coverage area per flight; Support priority allocation for multiple flight tasks and automatically sort them in combination with real-time weather data; Step 2, Multi-aircraft collaborative formation mapping: 3 - 5 UAVs achieve position synchronization through V2X communication, improving the operation efficiency by 40%; the cloud task management platform supports collaborative editing and conflict detection for multiple operation groups; Step 3, AI-driven data quality assurance: The real-time image quality assessment module automatically marks areas that need to be reshot; multi-spectral data fusion generates a 3D ground object classification model (automatically distinguishing vegetation / buildings / water bodies); Step 4, Modular payload rapid switching: Standardized interfaces are used to replace the sensor cabin within 10 minutes, supporting the interchange of orthophoto cameras, multi-spectral spectrometers, and lidar.

[0009] The aerial survey method further includes the following: 5G private network communication: Build a low-latency control link to achieve real-time transmission of 1080p images, with remote operation latency < 50ms; Intelligent obstacle avoidance system: Combine the 3D model and meteorological data to generate a flight risk heat map, automatically avoiding high-risk areas; Blockchain forensics: Upload flight parameters and image metadata to the blockchain for storage to ensure data immutability.

[0010] In the primary aerial survey step, the dynamic obstacle avoidance includes: Real-time update of the 3D point cloud map to detect newly added obstacles (such as construction machinery, temporary buildings); Generate the shortest avoidance path based on the Dijkstra algorithm, with the deviation between the adjusted flight path and the original flight path ≤ 1.5 meters.

[0011] In the multi-aircraft collaborative formation mapping, the UAVs achieve synchronization through the following methods: Periodically broadcast their own position information (frequency ≥ 10Hz); Use the Kalman filter algorithm to eliminate communication delay errors, with the formation position error ≤ 0.5 meters.

[0012] The blockchain forensics module includes: Upload flight parameters (latitude and longitude, attitude angle, battery status) to the blockchain; Store the hash values of image metadata (exposure time, ISO, focal length); The smart contract automatically verifies data integrity, and abnormal data triggers an alarm.

[0013] The technical effect of the present invention: The setting of the cross-shaped guide rod is beneficial to quickly position and install the driver, and at the same time can prevent relative sliding between the installation base plate and the frame.

[0014] In this application, an autopilot is installed inside the vertical takeoff and fixed-wing unmanned aerial vehicle (UAV). It is electrically connected to a battery reasonably installed inside the vertical takeoff and fixed-wing UAV. The present invention also provides an effective method for aerial survey of the vertical takeoff and fixed-wing UAV body, improving the efficiency of aerial survey of the vertical takeoff and fixed-wing UAV body. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic structural diagram of the present invention; Figure 2 is a schematic structural diagram of the vertical takeoff and fixed-wing UAV body; Figure 3 is a schematic structural diagram of the assembly box; Figure 4 is a schematic structural diagram of the connecting pipe; Figure 5 is a schematic structural diagram of the partition board; Figure 6 is a schematic structural diagram of the mounting rack; Figure 7 is a schematic structural diagram of the cross-shaped guide rod; In the figure: vertical takeoff and fixed-wing UAV body 1, battery 12, charging port 13, interface 131, autopilot 14, socket 141, assembly box 2, connecting pipe 21, partition board 211, internal threaded pipe 212, mating connecting pipe 22, through port 23, pressing port 24, screw-mounted button 3, button 31, stud 32, mounting rack 4, mounting base plate 41, cross-shaped guide rod 42, fixed frame 43, first cross-shaped through hole 44, second cross-shaped through hole 45, positioning section 46, fixing section 47, first through hole 48. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in this application will be clearly and completely described in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Usually, the components of this application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of this application. It should be noted that: similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0017] As Figure 1-7As shown in the figure, this embodiment provides a vertical takeoff and landing fixed-wing drone, including a vertical takeoff and landing fixed-wing drone body 1, which can use the brand ZOHD ALTUS. Since this vertical takeoff and landing fixed-wing drone body is an existing structure, it will not be described in detail here. The applicant has made a creative improvement. A battery 12 is installed on the vertical takeoff and landing fixed-wing drone body 1. This battery is a lithium battery. The vertical takeoff and landing fixed-wing drone body 1 is installed with a battery, and the battery is installed at the position of the fuselage of the vertical takeoff and landing fixed-wing drone body. A mounting box 2 is installed on the bottom surface of the vertical takeoff and landing fixed-wing drone body 1. An autopilot 14 is installed in the mounting box 2. The fuselage of the vertical takeoff and landing fixed-wing drone body 1 is embedded with a charging port 13 electrically connected to the battery 2. An interface 131 is embedded in the bottom surface of the fuselage. The autopilot 14 is connected with a power cord, and a socket 141 for inserting into the interface 131 is provided at the end of the power cord to achieve power-on. Four corner ends of the top surface of the mounting box 2 are all provided with connecting pipes 21. A screw button 3 is installed on the connecting pipe 21. The bottom surface of the vertical takeoff and landing fixed-wing drone body 1 is provided with a mating connecting pipe 22 with a button hole 24. They are buckled with each other and are screwed and connected through the screw button 3. Thus, the mounting box 2 is assembled on the bottom surface of the vertical takeoff and landing fixed-wing drone body. A partition 211 is arranged inside the connecting pipe 21. Inner threaded pipes 212 are embedded between both sides of the partition 211 and the inner wall of the connecting pipe 21. The screw buttons 3 are arranged oppositely. Each of them includes a button 31 and a stud 32 connected to the button 31. The connecting pipe 21 is provided with corresponding through holes 23. The stud 32 passes through the button hole 24 and the through hole 23 and is screwed into the inner threaded pipe 212, and the screwing is firm.

[0018] In the development process of the unmanned aerial vehicle technology field, the vertical takeoff and landing fixed-wing drone has gradually become an important research direction because it combines the vertical takeoff and landing ability of a multi-rotor drone and the high-efficiency cruising ability of a fixed-wing drone. However, the existing vertical takeoff and landing fixed-wing drones lack an autopilot structure that is convenient to assemble during unmanned flight. Especially during flight, due to the action of vibration force, the positioning rod of the autopilot is prone to rotate and change its position, resulting in the autopilot falling off from the frame plate. In addition, there are also deficiencies in the existing aerial survey methods, which cause the drone to easily generate incorrect information during the aerial survey process and cannot complete the task smoothly.

[0019] During the flight of the drone, due to the action of vibration force, the traditional autopilot structure is prone to fall off. The present invention solves this problem by installing a mounting box on the bottom surface of the drone body, installing an autopilot in the mounting box, and using the method of fixing with a cross-shaped guide rod 42 and a screw. The setting of the cross-shaped guide rod 42 is beneficial to quickly position and install the autopilot and avoid the relative sliding between the mounting bottom plate 41 and the frame, thereby ensuring the stability and reliability of the autopilot 14 during flight.

[0020] The autopilot 14 includes a mounting bracket 4, which is composed of a pilot mounting base plate 41, a cross-shaped guide rod 42 and a fixed frame 43. The pilot mounting base plate is provided with a first cross-shaped through hole 44, and the fixed frame 43 is provided with a second cross-shaped through hole 45. The cross-shaped guide rod 42 includes a positioning section 46 and a fixed section 47. The fixed section 47 is located inside the positioning section 46 and is slidable. The positioning section 46 sequentially passes through the first cross-shaped through hole 44 and the second cross-shaped through hole 45, and the fixed section 47 enters the inside of the fixed frame 43 through the second cross-shaped through hole 45. The pilot mounting base plate 41 is provided with a first through hole 48, the fixed frame 43 is provided with a second through hole, and the positioning section 46 is provided with a third through hole. A screw sequentially passes through the first through hole 48, the second through hole, and the third through hole and is fixed by matching the internal threads of the through holes to fix the pilot mounting base plate 41 on the fixed frame 43. The cross-shaped guide rod 42 sequentially passes through the first cross-shaped through hole 44 and the second cross-shaped through hole 45 for clamping connection. At this time, the fixed connection between the two has been completed, but this connection is not firm. As Figure 7 shown, there are 4 fixed sections 47 of the cross-shaped guide rod 42, and there are holes in the middle of the 4 fixed sections 47. Insert the head of a screwdriver into the holes and push the 4 fixed sections 47 inward in turn. At this time, the first through hole 48, the second through hole, and the third through hole are aligned and tightened by a screw. In this way, even if one becomes loose during operation, the other 3 fixations will not be affected. Through this design, the cross-shaped guide rod 42 can effectively prevent the relative sliding between the mounting base plate 41 and the fixed frame 43, ensuring that the autopilot will not fall off due to vibration force during flight. In addition, the autopilot is electrically connected to the battery in the UAV body, providing stable power support for the aerial survey of the UAV.

[0021] Compared with the prior art, the present invention solves the problem that the autopilot of a traditional UAV is prone to fall off during flight by improving the mounting structure of the autopilot. The method of fixing the cross-shaped guide rod 42 and the screw ensures the stability and reliability of the autopilot, improving the flight control accuracy and aerial survey efficiency of the UAV.

[0022] The vertical take-off fixed-wing UAV of the present invention solves the problem that the autopilot falls off due to vibration force during flight through a reasonable structural design. The setting of the cross-shaped guide rod 42 is beneficial to quickly position and install the pilot and avoid the relative sliding between the mounting base plate 41 and the frame. Through screw fixation, it is ensured that the autopilot is stable and reliable during flight, thereby improving the flight control accuracy and aerial survey efficiency of the UAV.

[0023] Furthermore, the present application also proposes that a partition is provided inside the connecting pipe, and internal threaded pipes are embedded between both sides of the partition and the inner wall of the connecting pipe.

[0024] By arranging a partition inside the connecting pipe and installing internal threaded pipes between both sides of the partition and the inner wall of the connecting pipe, the internal structural stability of the connecting pipe is enhanced. The partition plays a role in supporting and fixing, while the internal threaded pipe provides a screwing function, making the connection more firm. Through this structural design, the problems of loosening or falling off that may occur to the connecting pipe during the flight of the unmanned aerial vehicle are effectively solved, ensuring the overall stability and safety of the unmanned aerial vehicle.

[0025] The partition can be made of high-strength lightweight materials, such as carbon fiber composite materials or aluminum alloys, to ensure that it does not increase the overall weight of the unmanned aerial vehicle while providing support. The internal threaded pipe can be pre-processed into standard sizes for easy installation between the inner walls of the connecting pipe. The combination of the partition and the internal threaded pipe can be fixed through machining or adhesive bonding processes to further improve the reliability of the structure.

[0026] Thus, this application solves the problem of unstable structure of the connecting pipe in the prior art by arranging a partition inside the connecting pipe and installing internal threaded pipes between both sides of the partition and the inner wall of the connecting pipe. Compared with the prior art, the technical solution of this application provides a more reliable connection structure, avoiding the problems of loosening or falling off caused by vibration during flight, and ensuring the stability and safety of the unmanned aerial vehicle.

[0027] Furthermore, this application also proposes that the screwing buttons are arranged oppositely, each including a button and a stud connected to the button. Corresponding through holes are respectively opened in the mating connecting pipe and the connecting pipe, and the stud passes through the through hole and is screwed onto the internal threaded pipe.

[0028] This design enables the stud to pass through these corresponding through holes and be screwed onto the internal threaded pipe by opening corresponding through holes in the mating connecting pipe and the connecting pipe, thereby achieving a firm connection. This technical feature ensures that the autopilot will not fall off due to the action of vibration force during the flight of the vertical takeoff and landing fixed-wing unmanned aerial vehicle through the screwing connection method, thus solving the problem of the autopilot falling off during flight.

[0029] The screwing button includes a button and a stud. The stud passes through the through hole and is screwed inside the internal threaded pipe. The button and the stud can be made of high-strength materials to ensure that they can withstand large vibration forces during flight. The internal threaded pipe can be installed on the inner wall of the connecting pipe and firmly fixed through threaded connection. As a preferred embodiment, the length and diameter of the stud can be adjusted according to actual needs to ensure the reliability and stability of the connection.

[0030] By adopting the above technical solution, the present application can effectively solve the problem that the autopilot falls off due to vibration force during the flight of a vertical takeoff and landing fixed-wing unmanned aerial vehicle, ensuring the stability and reliability of the autopilot. Compared with the prior art, the technical solution of the present application realizes the firm fixation of the autopilot through screw connection, avoids the problem of the autopilot falling off caused by vibration force, and improves the flight safety and reliability of the unmanned aerial vehicle.

[0031] Furthermore, the present application also proposes a method for aerial survey of a vertical takeoff and landing fixed-wing unmanned aerial vehicle, including primary aerial survey and advanced aerial survey. The primary aerial survey includes the following steps: dynamic aerial survey planning based on multi-modal sensor fusion, integrating LiDAR and visual SLAM to construct a real-time three-dimensional point cloud map, automatically identifying obstacles in the survey area and dynamically adjusting the flight route; using a neural network model to intelligently calculate the optimal flight height and resolution combination according to the terrain complexity; AR-enhanced interactive task planning, superimposing virtual flight tracks and real-scene images through a ground station AR glasses, supporting gesture adjustment of polygon vertices; automatically generating a pre-flight health report, including the intelligent frequency pairing verification results of the unmanned aerial vehicle and the camera; vertical takeoff and landing control in complex environments, using multi-modal sensor fusion to achieve takeoff and landing on a ±10° slope, enhancing the adaptability to mountain operations; adjusting the camera exposure interval in real time during flight to optimize the image quality; intelligent verification of task parameters, automatically verifying the rationality of the resolution-height combination, and outputting an error compensation scheme; generating an aerial survey task feasibility report including three-dimensional terrain analysis. The advanced aerial survey includes the following steps: dynamic task scheduling and power optimization, dynamically adjusting the flight speed and altitude based on a battery health prediction model to maximize the coverage area per flight; supporting the assignment of task priorities for multiple flights, automatically sorting in combination with real-time weather data; multi-aircraft collaborative formation surveying, 3-5 unmanned aerial vehicles achieve position synchronization through V2X communication, improving the operation efficiency by 40%; a cloud task management platform supports collaborative editing and conflict detection for multiple operation groups; AI-driven data quality assurance, a real-time image quality assessment module automatically marks areas that need to be reshot; multi-spectral data fusion to generate a three-dimensional ground object classification model; modular payload quick switching, using a standardized interface to achieve quick replacement of the sensor cabin, supporting the interchange of orthophoto cameras, multi-spectral spectrometers, and lidar.

[0032] The technical solution of this application realizes real-time three-dimensional point cloud map construction and dynamic aerial survey planning through multi-modal sensor fusion, uses a neural network model to intelligently calculate flight parameters, enhances task planning through AR technology, supports vertical takeoff and landing control in complex environments, and conducts intelligent verification of task parameters. Advanced aerial survey improves the surveying and mapping efficiency and data quality through technical means such as dynamic task scheduling and power optimization, multi-aircraft collaborative formation surveying, AI-driven data quality assurance, and rapid switching of modular payloads. These technical features cooperate with each other to solve the problem of improving measurement accuracy and efficiency during the aerial survey of vertical takeoff and landing fixed-wing UAVs while meeting the operation requirements of complex environments. For example, multi-modal sensor fusion and dynamic aerial survey planning can identify and avoid obstacles in real time to ensure flight safety and measurement accuracy; AR-enhanced task planning and intelligent verification improve the operability and reliability of tasks; multi-aircraft collaborative formation and AI-driven data quality assurance significantly improve operation efficiency and data quality.

[0033] Specifically, based on the dynamic aerial survey planning of multi-modal sensor fusion, by integrating LiDAR and visual SLAM technologies, it can construct a real-time three-dimensional point cloud map, automatically identify obstacles in the survey area, and dynamically adjust the flight path according to the position and size of the obstacles. The application of the neural network model can intelligently calculate the optimal combination of flight altitude and resolution according to the terrain complexity, thus improving the measurement accuracy and efficiency. The AR-enhanced interactive task planning, through the ground station AR glasses to overlay virtual flight tracks and real-scene images, supports gesture adjustment of polygon vertices, and automatically generates a pre-flight health report, including the intelligent frequency pairing verification results of the UAV and the camera. These technical means improve the intuitiveness and interactivity of task planning, and enhance the operability and reliability of tasks. In terms of vertical takeoff and landing control in complex environments, through multi-modal sensor fusion technology, it can achieve slope takeoff and landing of ±10°, enhancing the adaptability of the UAV in mountain operations. During the flight, the camera exposure interval is adjusted in real time to optimize the image quality, thus ensuring the accuracy of measurement data. The intelligent verification of task parameters automatically checks the rationality of the resolution-altitude combination, outputs an error compensation scheme, and generates a feasibility report of the aerial survey task with three-dimensional terrain analysis, providing a scientific basis and guarantee for the aerial survey task.

[0034] Dynamic task scheduling and power optimization in advanced aerial surveying. By dynamically adjusting flight speed and altitude based on a battery health prediction model, it maximizes the coverage area per flight, supports priority allocation for multiple flight missions, and automatically sorts them in combination with real-time weather data, improving the scientific and rational nature of task scheduling. Multi-aircraft collaborative formation mapping realizes position synchronization through V2X communication, enhancing operation efficiency. The cloud task management platform supports collaborative editing and conflict detection for multiple operation groups, ensuring the efficient implementation of tasks. AI-driven data quality assurance automatically marks areas that need to be reshot through a real-time image quality assessment module, and generates a 3D ground object classification model through multi-spectral data fusion, improving data quality and classification accuracy. Modular payload quick switching adopts standardized interfaces to achieve rapid replacement of the sensor cabin, supporting the interchange of orthophoto cameras, multi-spectral spectrometers, and lidar, enhancing the adaptability and flexibility of the UAV.

[0035] In summary, the technical solution of this application, through the application of multiple advanced technologies such as multi-modal sensor fusion, neural network models, AR technology, multi-aircraft collaboration, AI drive, and modular design, has achieved a significant improvement in the measurement accuracy and efficiency of vertical takeoff and landing fixed-wing UAVs during aerial surveying, while meeting the operation requirements in complex environments. Compared with the existing technology, this application has obvious advantages in improving measurement accuracy, enhancing operation efficiency, and strengthening the operability and reliability of tasks.

[0036] Furthermore, this application also proposes that the aerial survey method further includes the following: 5G private network communication: Build a low-latency control link to achieve real-time transmission of 1080p images, with a remote operation delay < 50ms; Intelligent obstacle avoidance system: Combine 3D models and meteorological data to generate a flight risk heat map and automatically avoid high-risk areas; Blockchain evidence storage: Upload flight parameters and image metadata to the blockchain for storage to ensure the immutability of data.

[0037] 5G private network communication builds a low-latency control link to achieve real-time transmission of 1080p images, with a remote operation delay of less than 50 milliseconds, effectively solving the problem of data transmission delay. The intelligent obstacle avoidance system combines 3D models and meteorological data to generate a flight risk heat map and automatically avoids high-risk areas to ensure flight safety. Blockchain evidence storage uploads flight parameters and image metadata to the blockchain for storage to ensure the immutability of data and guarantee the authenticity of the data.

[0038] The technical solution of 5G private network communication includes implementing a low-latency control link through a dedicated 5G network to ensure that 1080p images can be transmitted in real time, with the remote operation delay controlled within 50 milliseconds. The intelligent obstacle avoidance system uses 3D models and meteorological data to generate a flight risk heat map, thereby automatically avoiding high-risk areas. Blockchain evidence storage technology ensures the immutability of data by uploading flight parameters and image metadata to the blockchain for storage.

[0039] Specifically, 5G private network communication can ensure low latency and high bandwidth of the communication link by deploying dedicated 5G base stations and core network devices. The intelligent obstacle avoidance system can adopt multi-sensor fusion technology, including lidar, vision sensors, and meteorological sensors, to generate a real-time three-dimensional environmental model and conduct flight risk assessment in combination with meteorological data. Blockchain evidence storage can adopt public chain or consortium chain technology to store the hash values of flight parameters and image metadata on the blockchain and automatically verify the integrity of the data through smart contracts.

[0040] Through the combination of 5G private network communication, intelligent obstacle avoidance system, and blockchain evidence storage technology, this application effectively solves the problems of data transmission delay, flight safety, and data authenticity during the aerial survey of vertical takeoff and landing fixed-wing UAVs. Compared with the prior art, the technical solution of this application has significant advantages in terms of data transmission speed, flight safety, and data authenticity, and can significantly improve the aerial survey ability of vertical takeoff and landing fixed-wing UAVs in complex environments.

[0041] Furthermore, this application also proposes that in the primary aerial survey step, dynamic obstacle avoidance includes: real-time updating of the three-dimensional point cloud map to detect newly added obstacles such as construction machinery and temporary buildings; generating the shortest avoidance path based on the Dijkstra algorithm, with the deviation between the adjusted flight path and the original flight path ≤ 1.5 meters.

[0042] By real-time updating the three-dimensional point cloud map, the system can detect newly added obstacles such as construction machinery and temporary buildings. Generating the shortest avoidance path based on the Dijkstra algorithm ensures that the deviation between the adjusted flight path and the original flight path does not exceed 1.5 meters. These technical features work together to ensure that the UAV can timely avoid newly added obstacles during the aerial survey and ensure the smooth completion of the aerial survey task.

[0043] Specifically, the technology of real-time updating the three-dimensional point cloud map enables the UAV to continuously obtain the latest information of the surrounding environment during flight and timely detect and identify newly added obstacles. The Dijkstra algorithm is a classic shortest path algorithm, through which the optimal path for the UAV to bypass obstacles can be quickly calculated to ensure that the UAV can safely and effectively complete the aerial survey task. Real-time updating of the three-dimensional point cloud map can be achieved through multi-modal sensor fusion technology, including the combination of LiDAR and visual SLAM sensors. These sensors can provide high-precision environmental data to provide a basis for the construction of the three-dimensional point cloud map. The application of the Dijkstra algorithm depends on a high-performance computing platform, which can process a large amount of path planning data in a short time to ensure that the UAV can quickly find the optimal avoidance path in complex environments.

[0044] The technical solution of this application combines the real-time update of the 3D point cloud map and the Dijkstra algorithm to achieve the function of the UAV avoiding newly added obstacles in real time during aerial surveying. Compared with the prior art, the technical solution of this application can provide higher safety and reliability, ensuring that the UAV can successfully complete the aerial survey task in a complex environment. Therefore, the technical solution of this application has significant technical advantages and application value.

[0045] Furthermore, this application also proposes that the UAV achieves synchronization in the following ways: periodically broadcasting its own position information (frequency ≥ 10Hz); using the Kalman filtering algorithm to eliminate communication delay errors, and the formation position error ≤ 0.5 meters.

[0046] During the multi-UAV cooperative formation surveying process, the UAVs achieve synchronization by periodically broadcasting their own position information and using the Kalman filtering algorithm to eliminate communication delay errors. The method of periodically broadcasting its own position information ensures that each UAV can obtain the positions of other UAVs in real time, so as to perform position synchronization. The Kalman filtering algorithm is used to eliminate the delay errors generated during communication, ensuring that the formation position error remains within 0.5 meters. These technical features cooperate with each other to solve the technical problem of UAV synchronization in multi-UAV cooperative formation surveying and achieve high-precision UAV formation surveying.

[0047] The periodic broadcasting of its own position information can be achieved in various ways. For example, using a high-precision GPS module to ensure that each UAV can accurately obtain its own position and broadcast it. The broadcast frequency is set to 10Hz or higher to ensure the real-time nature of the position information. The Kalman filtering algorithm can be implemented through an embedded computing platform to process the received position information in real time and eliminate the errors caused by communication delays. Furthermore, the parameters of the Kalman filtering algorithm can be dynamically adjusted according to the flight environment and communication conditions to adapt to different task requirements. As a preferred implementation method, a high-performance computing unit can be integrated on the UAV to improve the computing efficiency and accuracy of the filtering algorithm.

[0048] Therefore, this application solves the technical problem of UAV synchronization in multi-UAV cooperative formation surveying through the combination of periodically broadcasting its own position information and the Kalman filtering algorithm. Compared with the prior art, the technical solution of this application can significantly improve the synchronization accuracy of the UAV formation, ensure that the formation position error remains within 0.5 meters, and thus improve the overall efficiency and accuracy of multi-UAV cooperative formation surveying.

[0049] Furthermore, this application also proposes that the blockchain evidence storage module includes uploading flight parameters (latitude and longitude, attitude angle, battery status) to the blockchain, storing the hash values of image metadata (exposure time, ISO, focal length), automatically verifying data integrity by smart contracts, and triggering an alarm for abnormal data.

[0050] With these technical features, the technical solution of the present application can solve the problems of the integrity and immutability of flight parameters and image metadata. Specifically, the flight parameters and image metadata are stored through blockchain technology. The use of hash value storage ensures the immutability of the data, and the integrity of the data is automatically verified through smart contracts. Once abnormal data is detected, an alarm is immediately triggered, thereby ensuring the security and reliability of the data.

[0051] The flight parameters being uploaded to the blockchain can be achieved by the drone collecting data such as longitude, latitude, attitude angle, and battery status in real time during flight and uploading these data to be stored in the blockchain. The hash value storage of image metadata is to perform a hash operation on the image metadata (such as exposure time, ISO, focal length, etc.) captured by the drone and store the hash value in the blockchain to ensure that these data are not tampered with during transmission and storage. The automatic verification of data integrity by smart contracts means that smart contracts are pre-set in the blockchain. When new data is uploaded to the blockchain, the smart contracts will automatically verify the integrity of the data. If abnormal data is found, the alarm system will be triggered.

[0052] Thus, the present application effectively solves the problems of the integrity and immutability of flight parameters and image metadata through the blockchain evidence storage module. Compared with the prior art, the advantages of the present application lie in using the decentralized and immutable characteristics of blockchain technology to ensure the security and reliability of flight data and image data, and improving the data management ability and overall security of the drone system.

[0053] Furthermore, the present application also proposes that the flight parameters are uploaded to the blockchain, the hash value of the image metadata is stored, the smart contract automatically verifies the data integrity, and the abnormal data triggers an alarm.

[0054] The storage of flight parameters and image metadata on the blockchain can ensure the security and immutability of the data. The flight parameters include longitude, latitude, attitude angle, and battery status. After these data are uploaded to the blockchain, they can provide a detailed record during the flight. Information such as exposure time, ISO, and focal length of the image metadata is stored through hash values, ensuring the integrity and authenticity of the image data. The automatic verification function of the smart contract can monitor the integrity of the data in real time. Once abnormal data is detected, an alarm will be immediately triggered to remind the operator to check and process it. These technical features cooperate with each other to effectively solve the problems of tampering and loss that may occur to drone flight data and image data during storage and transmission, ensuring the reliability and accuracy of aerial survey data.

[0055] The implementation method of flight parameter uploading can be achieved by uploading the flight data of the UAV to the blockchain network in real time, and using the decentralization and immutability of the blockchain to ensure data security. The storage of image metadata hash values can be realized by performing a hash operation on the image metadata and storing the hash value on the blockchain, thus ensuring data integrity. The automatic verification function of the smart contract can perform data verification operations automatically in the blockchain network through pre-written contract code. Once data anomalies are detected, the contract will automatically trigger an alarm mechanism.

[0056] In this application, by uploading flight parameters and image metadata to the blockchain for storage and combining with smart contract technology, the secure storage and immutability of data are achieved, effectively solving the problems that may be encountered in the storage and transmission of UAV flight data and image data. Compared with the prior art, the technical solution of this application combines blockchain technology and smart contracts to provide a more secure and reliable data storage and verification method, ensuring the reliability and accuracy of aerial survey data.

[0057] Furthermore, this application also proposes that the UAV achieves synchronization in the following ways: periodically broadcasting its own position information (frequency ≥ 10Hz); using the Kalman filter algorithm to eliminate communication delay errors, and the formation position error ≤ 0.5 meters.

[0058] To achieve synchronization between UAVs in multi-UAV cooperative formation surveying, the technical solution eliminates communication delay errors by periodically broadcasting its own position information and using the Kalman filter algorithm. Periodically broadcasting its own position information ensures that each UAV can timely obtain the positions of other UAVs for coordination; the Kalman filter algorithm is used to eliminate the delay errors generated during communication, ensuring that the formation position error is within 0.5 meters. Through these means, the synchronization problem of UAVs in cooperative formation surveying can be effectively solved.

[0059] The implementation method of periodically broadcasting its own position information can be achieved by installing a high-frequency GPS module on each UAV and connecting it to the communication module, enabling the UAV to broadcast its position information at a frequency of at least 10Hz. The implementation method of the Kalman filter algorithm can be achieved by integrating a Kalman filter module into the control system of the UAV. This module can process the received position information in real time and eliminate the errors caused by communication delays. Furthermore, a redundancy mechanism can be designed in the control system of the UAV to ensure that a certain degree of synchronization can still be maintained in case of communication interruption or weak GPS signal.

[0060] Through the combination of periodic broadcast of position information and the Kalman filtering algorithm, this application effectively solves the problem of UAV synchronization in multi-aircraft collaborative formation surveying and mapping. Compared with the prior art, this application can significantly improve the accuracy and efficiency of UAV collaborative work, especially in complex environments, ensuring that the formation position error does not exceed 0.5 meters, thereby improving the accuracy and reliability of the surveying and mapping results.

[0061] The above are only the embodiments of this application and are not intended to limit the protection scope of this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. A vertical fixed-wing UAV, comprising a vertical fixed-wing UAV body, characterized in that: The vertical fixed-wing UAV body is equipped with a battery, which is installed in the fuselage of the vertical fixed-wing UAV body. An assembly box is installed on the bottom surface of the vertical fixed-wing UAV body, and an autopilot is installed in the assembly box. The vertical fixed-wing UAV body has a fuselage with a charging port electrically connected to the battery embedded in it, and an interface is embedded on the bottom surface of the fuselage. The autopilot is connected to a power cord, and a socket for inserting the interface is provided at the end of the power cord. Connecting pipes are provided at the four end corners of the top surface of the assembly box, and screw-mounted buttons are installed on the connecting pipes. The bottom surface of the vertical fixed-wing UAV body is provided with a matching connecting pipe with a press opening, which are mutually buckled and screw-connected through the screw-mounted buttons; The autopilot includes a mounting frame, which includes an autopilot mounting base plate, a cross-shaped guide rod and a fixed frame. The autopilot mounting base plate is provided with a No. 1 cross-shaped through hole, and the fixed frame is provided with a No. 2 cross-shaped through hole. The cross-shaped guide rod includes a positioning section and a fixing section. The fixing section is located inside the positioning section and is slidable. The positioning section passes through the No. 1 cross-shaped through hole and the No. 2 cross-shaped through hole in sequence, and the fixing section enters the interior of the fixing frame through the No. 2 cross-shaped through hole. The autopilot mounting base plate is provided with a No. 1 through hole, and the fixed frame is provided with a No. 2 through hole. The positioning section is provided with a No. 3 through hole. The screw rod passes through the No. 1 through hole, the No. 2 through hole, and the No. 3 through hole in sequence and cooperates with the internal threads of the through holes to fix the autopilot mounting base plate to the fixed frame.

2. A vertical take-off fixed-wing UAV according to claim 1, characterized in that: A partition is arranged inside the connecting pipe, and internally threaded pipes are embedded between the two sides of the partition and the inner wall of the connecting pipe.

3. A vertical take-off fixed-wing UAV according to claim 2, characterized in that: The threaded buttons are arranged relatively to each other, and each includes a button and a stud connected to the button. The matching connecting pipe and the connecting pipe are respectively provided with corresponding through openings, and the stud passes through the through openings and is threadedly connected to the internal threaded pipe.

4. A method for aerial surveying using a vertical fixed-wing UAV according to any one of claims 1 to 3, characterized in that: The aerial survey method includes primary aerial survey and advanced aerial survey. The primary aerial survey includes the following steps: The first step is dynamic aerial survey planning based on multimodal sensor fusion: integrating LiDAR and visual SLAM to build a real-time 3D point cloud map, automatically identifying obstacles in the survey area and dynamically adjusting the route; using a neural network model to intelligently calculate the optimal flight altitude and resolution combination based on the complexity of the terrain; the second step is AR-enhanced interactive mission planning: superimposing virtual tracks and real-life images through ground station AR glasses, and supporting gesture adjustment of polygon vertices; Automatically generate a pre-flight health report, including the results of the intelligent frequency calibration between the drone and the camera; Step 3: Vertical take-off and landing control in complex environments: Use multi-modal sensor fusion to achieve ±10° slope take-off and landing, and enhance the adaptability of mountain operations; Real-time adjustment of the camera exposure interval (0.3-3 seconds dynamic range) during flight to optimize image quality; Step 4: Intelligent verification of mission parameters: Automatically verify the rationality of the resolution-altitude combination, and output the error compensation plan; Generate a feasibility report for aerial survey missions including 3D terrain analysis; The advanced aerial survey comprises the following steps: Step 1: Dynamic task scheduling and power optimization: Dynamically adjust flight speed and altitude based on the battery health prediction model to maximize the coverage area of ​​a single flight; support multi-flight task priority allocation, and automatically sort based on real-time weather data; The second step is multi-drone collaborative formation mapping: 3-5 drones achieve position synchronization through V2X communication, improving operation efficiency by 40%; The cloud-based task management platform supports collaborative editing and conflict detection by multiple work groups. The third step is AI-driven data quality assurance: the real-time image quality assessment module automatically marks the areas that need to be re-photographed; multi-spectral data fusion generates a three-dimensional land classification model (automatically distinguishes vegetation / buildings / water bodies). The fourth step is modular payload rapid switching: a standardized interface is used to enable the sensor cabin to be replaced within 10 minutes, supporting the interchange of orthophoto cameras, multi-spectrometers, and lidars.

5. The aerial survey method of a vertical take-off fixed-wing UAV according to claim 4, characterized in that: The aerial survey method further includes the following: 5G private network communication: Build a low-latency control link to achieve real-time return of 1080p images, with remote operation delay of <50ms; Intelligent obstacle avoidance system: Combines 3D models with meteorological data to generate flight risk heat maps and automatically avoid high-risk areas; Blockchain evidence storage: flight parameters and image metadata are stored on the chain to ensure that the data cannot be tampered with.

6. The aerial survey method of a vertical fixed-wing UAV according to claim 4, characterized in that: In the primary aerial survey step, the dynamic obstacle avoidance includes: Update 3D point cloud maps in real time to detect new obstacles (such as construction machinery and temporary buildings); The shortest avoidance path is generated based on the Dijkstra algorithm, and the deviation between the adjusted route and the original route is ≤1.5 meters.

7. The aerial survey method of a vertical take-off fixed-wing UAV according to claim 4, characterized in that: In the multi-machine collaborative formation surveying, the UAVs are synchronized in the following ways: Periodically broadcast its own location information (frequency ≥ 10Hz); The Kalman filter algorithm is used to eliminate communication delay errors, and the formation position error is ≤0.5 meters.

8. The aerial survey method of a vertical fixed-wing UAV according to claim 4, characterized in that: The blockchain evidence storage module includes: Flight parameters (latitude and longitude, attitude angle, battery status) are uploaded to the chain; Image metadata (exposure time, ISO, focal length) hash value storage; Smart contracts automatically verify data integrity, and abnormal data triggers alarms.

Images