A geographic information-based unmanned aerial vehicle urban management scene patrol and inspection system and method

Abstract

The application discloses a kind of unmanned plane city management scene tour checking and inspecting system and method based on geographic information, the system includes: event processing module, task management module, route planning module, unmanned plane executive module and three-dimensional modeling module, realize the intelligent planning of unmanned plane city management inspection task in combination with geographic information, by fusing city management inspection business process, task multi-mode issue, three-dimensional map route self-defined planning and task scene management, realize the high degree of fusion of unmanned plane operation and business management, form full-process closed loop, ensure that unmanned plane can complete task in best state under different scenes, greatly enhance the flexibility and adaptability of task issue. Through the space geographic information of three-dimensional map, the potential state perception ability of obstacle in route planning is enhanced, the flight risk is reduced, the safety risk in the process of unmanned plane flight is greatly reduced, and the tour checking and inspecting effect can be further optimized.

Description

Technical Field

[0001] This invention relates to a drone-based urban management scene patrol and inspection system and method, specifically a drone-based urban management scene patrol and inspection system and method based on geographic information; it belongs to the field of drone application related technology. Background Technology

[0002] As cities expand and urban management becomes increasingly complex, drones, with their high mobility, flexibility, and ability to acquire high-resolution images, have gradually become a vital force in urban management. However, there are still many limitations in integrating drones with business applications: (1) At the task management level, the issuance of management tasks relies excessively on the personal experience of operators and lacks a systematic and intelligent task scheduling mechanism. Traditional task issuance methods are mostly limited to simple immediate flight commands, making it difficult to flexibly adjust parameters such as task priority and execution time according to the actual situation. Furthermore, in the face of sudden emergencies, due to the cumbersome manual handling process and the delay in information transmission, it is impossible to ensure that the drone can respond quickly and go to the target area for disposal. In other words, the existing systems and methods lack a business loop and are mostly still at the stage of offline docking with drones for task processing, without direct online connection with the event. Moreover, after the drone takes off, it cannot share the captured videos or images immediately, resulting in the overall business process not forming a closed loop and requiring manual tracking and processing.

[0003] (2) Regarding the planning of inspection areas, most existing technologies operate based on two-dimensional maps. Although they can provide some geographical information, their ability to express the complex three-dimensional spatial environment of cities is limited. They cannot accurately reflect key geographical features such as terrain undulations and building heights, making it difficult to fully consider the impact of these factors on UAV flight safety and inspection effectiveness when planning inspection areas and routes. For example, there may be unreasonable division of inspection areas, with insufficient inspection frequency in some key or high-risk areas, while some non-critical areas are over-inspected. Differentiated inspection strategies cannot be formulated based on the geographical characteristics and functional uses of different areas, resulting in unsatisfactory inspection results and waste of resources.

[0004] (3) Regarding flight path planning, current UAV flight path planning mainly focuses on flight path planning algorithms, such as using the shortest path algorithm or greedy algorithm to determine the flight route from the starting point to the target point. This can improve the efficiency of UAV flight to a certain extent. However, in terms of inspection areas and flight path planning, although attempts have been made to introduce geographic information, most of them simply overlay geographic information data onto two-dimensional maps without truly exploring the three-dimensional spatial characteristics of geographic information. For mountainous areas or urban areas with high-rise buildings, planning flight paths based solely on coordinate information on two-dimensional maps cannot effectively avoid the three-dimensional spatial range of obstacles such as mountains and buildings, threatening flight safety. Therefore, in the actual UAV flight path planning process, it is necessary not only to consider the geometric parameters of the flight path, such as flight altitude, heading overlap rate, and lateral overlap, but also to pay attention to flight environment parameters, flight performance parameters, camera parameters, etc., all of which rely heavily on human experience.

[0005] It is evident that existing drone-based urban management patrol and inspection systems and methods have numerous problems, including: closed-loop overall business process, low task handling efficiency, poor situational awareness of the surrounding environment during flight path planning, and flight path planning's high dependence on human experience. These problems restrict the efficiency and feasibility of using drones for urban management. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a drone-based urban management scene patrol and inspection system and method. Based on the actual business processes of urban management, this system combines geographic information and drone flight path automatic planning algorithms to achieve intelligent task patrol and inspection of drones in urban management.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention first discloses a geographic information-based drone-based urban management scene patrol and inspection system, comprising: Event handling module: Used to receive reported events, perform preliminary processing and judgment on the events, determine whether drones are needed to handle them, and transmit the processing results to the task management module; Task Management Module: Responsible for managing UAV missions, planning missions according to instructions from the Event Processing Module, and interacting with the Flight Path Planning Module to determine the specific parameters of the UAV flight mission; Flight route planning module: Calculates safe and efficient drone flight routes using camera parameters and geographic information and sends them to the drone flight execution module; UAV Flight Module: Executes UAV flight missions, conducts flight operations according to the routes determined by the route planning module, collects relevant results information and associates it with tasks or events, and notifies the system of task completion status; 3D Modeling Module: Performs 3D modeling of the target area, constructs a 3D map, and provides geographic information data support for the system.

[0008] Preferably, in the aforementioned task management module, the specific parameters of the flight mission include: the UAV's flight altitude, speed, camera parameters, and payload equipment information.

[0009] More preferably, the results collected by the aforementioned UAV flight module are photos or videos.

[0010] Preferably, the aforementioned task management system supports at least three task delivery modes: immediate delivery, scheduled delivery, and periodic delivery.

[0011] This invention also discloses an inspection method based on the aforementioned drone-based urban management scene patrol and inspection system, which specifically includes the following steps: (1) Event reporting: The system is started, and relevant personnel report the event to the event handling module; (2) Event handling: The system issues an event and determines whether a drone is needed; (3) Task planning: Select the task mode, determine whether to select an existing route, select the task scenario, preview and submit after confirming the task, complete the task approval, and enter the UAV flight operation. (4) Flight path planning: Automatically calculate or define the camera parameters in the mission scenario, such as the altitude, heading angle, and lateral overlap rate to be sampled, and automatically generate the UAV flight path; (5) Drone flight: After the task is approved, it is sent to the system to notify the drone to take off and automatically associate the results information such as photos / videos with the task or time, notify the system, and complete the patrol flight.

[0012] Preferably, during the aforementioned event handling, if it is determined that drone participation is required, the information is transmitted to the task management module to add a drone flight mission; if not, the handling result is fed back to the system to complete the event loop.

[0013] More preferably, when planning a mission, if an existing flight path is selected, the system will automatically recommend the drone's flight altitude and speed, camera parameters, and payload equipment information according to the selected scenario; if an existing flight path is not selected, the system will proceed with the flight path planning process.

[0014] More preferably, the aforementioned route planning is achieved based on a 3D map constructed using a 3D modeling module.

[0015] More preferably, the specific implementation steps of the aforementioned route planning are as follows: a) Calculate or define the required sampling precision, altitude, flight path angle, and lateral overlap rate based on camera parameters; b) Add polygonal boundary points to the 3D map. These points are 3D curved surfaces on Earth and need to be converted to a 2D coordinate system using a tool, becoming a plane. c) Based on b), a planar polygon is obtained. Then, the take-off point of the unmanned aerial vehicle is determined by the location of the hangar. The point with the shortest distance from the take-off point to the polygon is defined as the starting point of the flight path, which is also the point with the minimum distance from the aircraft to the flight path. d) Based on the lateral overlap rate and sampling accuracy in a), calculate the longest distance S between the two waypoints, and based on the planar polygon obtained in b), calculate the slope of the boundary line; e) A ray is formed from the waypoint at the starting point along the slope direction. Then, this ray is translated by a vertical distance S. Based on the longest distance S between the two waypoints obtained in d) and the slope, the intersection point is obtained and recorded, which becomes a waypoint H1 on the route. f) If the ray is parallel to the line segment or has no intersection within a perpendicular distance S, then the flight of that side is considered complete, and a new boundary needs to be obtained as the line segment for the next cycle. g) Then, continue moving this ray perpendicular to the distance S and the line segment of the polygon, continuously obtaining intersection points, and finally judging the entire polygon until the line segment of the polygon becomes the first line segment, then stop the loop; h) After obtaining all 2N intersection points on the polygon in the previous step, find the first corresponding point from the initial point in the opposite direction according to the slope and record it as the second waypoint (the vertical distance of movement corresponding to the waypoint is S). Continue to cycle forward and backward, connecting the two in pairs to form a route. i) Finally, all points are converted into latitude and longitude coordinates and displayed on a 3D map to form parallel flight routes.

[0016] The advantages of this invention are: (1) The UAV urban management scene patrol and inspection system of the present invention combines geographic information to realize intelligent planning of UAV urban management patrol and inspection tasks. By integrating urban management patrol and inspection business processes, multi-mode task delivery, custom route planning of three-dimensional map and task scene management, it realizes a high degree of integration of UAV operation and business management, forming a closed loop of the whole process, ensuring timely, efficient and accurate handling of urban management events, complete information transmission and feedback, and improving overall management efficiency.

[0017] (2) In this invention, a variety of task modes are also adopted to support adaptation to various scenarios. Automatic parameter recommendation reduces reliance on human experience, improves the scientific nature and flexibility of task execution, and collects various scenarios of actual task handling. The system supports different delivery modes such as immediate delivery, timed delivery, and periodic delivery, which can cover all business scenarios in the process of urban management. At the same time, in order to reduce reliance on human experience, task scenarios can be selected in the task planning stage. The system can automatically recommend the altitude and speed of the UAV, the camera parameters of the UAV, and the payload equipment information according to the selected scenario content. This can minimize the reliance on human experience, not only reducing the complexity and uncertainty of human operation, but also improving the scientific nature and standardization of task execution, ensuring that the UAV can complete the task in the best state in different scenarios, and greatly enhancing the flexibility and adaptability of task delivery.

[0018] (3) In this invention, a three-dimensional map is constructed by surveying and mapping modeling, and the cruise area can be directly defined on the three-dimensional map. The spatial geographic information of the three-dimensional map enhances the ability to perceive the situation of obstacles in the flight route planning, reduces flight risks, greatly reduces the safety risks during the flight of the UAV, and can further optimize the patrol and inspection effect.

[0019] (4) In traditional route planning, coordinates are manually selected one by one on a two-dimensional map and the order between coordinates is specified. However, this invention is based on a route generation algorithm of map polygon boundary points. It uses an automatic route generation mechanism to ensure area coverage, improves image acquisition quality, route planning efficiency and accuracy, reduces human error, and greatly enhances the intelligence level and practicality of UAV urban management inspection and patrol tasks. Attached Figure Description

[0020] Figure 1 This is a business architecture diagram of the UAV urban management scene patrol and inspection system based on geographic information of the present invention; Figure 2 This is a business process diagram of the UAV urban management scene patrol and inspection method based on geographic information of the present invention; Figure 3 This is a flowchart of the flight path generation process in the UAV urban management scene patrol and inspection method based on geographic information of the present invention; Figure 4 This is an example image of the flight path generated in the UAV urban management scene patrol and inspection method based on geographic information of the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0022] Example 1

[0023] This embodiment discloses a geographic information-based drone-based urban management scene patrol and inspection system. (See also...) Figure 1 It includes an event handling module, a task management module, a flight path planning module, a drone flight module, and a 3D modeling module. These modules are closely connected through information transmission and feedback, together forming an efficient and intelligent drone-based urban management scene patrol and inspection system.

[0024] (1) Event processing module: Receives event reports from field personnel, performs preliminary processing and judgment on the event, and determines whether the drone needs to be involved in the handling. If needed, the information is passed to the task management module to add a drone flight mission; if not needed, the processing result is directly fed back to the system to complete the event closure.

[0025] (2) Task Management Module: Responsible for managing UAV tasks, including task mode selection (such as immediate dispatch, scheduled dispatch, periodic dispatch), task scenario selection, task approval, and other functions. It performs task planning based on the needs of the event processing module or user instructions, and interacts with the flight path planning module to determine the specific parameters of the UAV flight task.

[0026] The specific process is as follows: Receive event information from the event processing module that requires drone intervention, and enter the task planning process; 2) Based on the user's selection of the task mode and task scenario, the system automatically recommends relevant parameters (such as UAV flight altitude, speed, camera parameters, payload equipment information, etc.). 3) After the task approval is completed, the task information is transmitted to the flight path planning module. At the same time, after the task is completed, the result information (photos / videos) transmitted by the UAV flight module is received and automatically associated with the task / event to achieve information closure.

[0027] (3) Flight route planning module: Based on the 3D map, the inspection area is customized and the flight route is automatically generated. Using camera parameters and geographic information, a safe and efficient UAV flight route is calculated to ensure effective coverage of the target area, enhance the perception of obstacles, and reduce flight risks.

[0028] The specific process is as follows: Receive task information sent by the task management module and automatically calculate or define key data such as sampling height, heading angle, and lateral overlap rate based on camera parameters in the task scenario; It supports operations on 3D maps, including adding polygon boundary points (converting them to planar polygons in a 2D coordinate system using tools), determining the starting point of a flight route (based on the hangar location and the principle of the shortest distance to the polygon), calculating the longest distance between waypoints and the slope of the parallel line of the flight route, and generating flight routes (through a series of complex calculations and intersection point acquisition processes). Based on Figure 3 The flight route information generated by the dimensional model is synchronized to the task management module so that users can confirm and make adjustments; Once the mission is confirmed, the flight path information is synchronized to the UAV flight module, enabling the UAV to fly along the preset flight path.

[0029] (4) Unmanned Aerial Vehicle (UAV) Flight Module To execute drone flight missions, the drones will fly along the routes determined by the route planning module, collect photos / videos and other results, and automatically associate the results with the mission or event, and notify the system of the mission completion status.

[0030] The specific process is as follows: Receive the approved task notification from the task management module, and take off to execute the task according to the route information provided by the route planning module; During flight, photos and videos are collected. Once the mission is completed, the results are automatically linked to the mission or event and the system is notified, completing the information feedback for the entire business process. This enables the system to record and process the mission execution status.

[0031] (5) 3D modeling module By directly calling drones to survey the target scene or receiving uploaded third-party images of the target scene, a 3D model of the target area is created, constructing a 3D map that accurately reflects key geographical features such as terrain undulations and building heights, providing precise geographic information data support for subsequent inspection area customization and route planning.

[0032] The specific process is as follows: It connects to the route planning module, providing it with 3D map data. When performing operations based on the 3D map (such as adding polygon boundary points, determining the starting point of the route, and calculating the relationship between waypoints), the route planning module relies on the geographic information contained in the 3D map constructed by the 3D modeling module to enhance its ability to perceive the potential of obstacles and achieve safer and more efficient route planning.

[0033] It can receive external commands or trigger signals. When detailed management or geographic information updates are needed for a specific area, it initiates a mapping task for the target scene, acquires the latest geographic data, and updates the 3D map model, ensuring that the geographic information used by the system remains accurate and timely. Simultaneously, it provides basic data support for other functions in the system involving geographic information display and analysis (such as more accurately marking event coordinates during event reporting).

[0034] Example 2

[0035] This embodiment discloses an inspection method based on the aforementioned inspection system. This method closely integrates urban management business processes with drone flight path planning, forming a closed-loop process. Figure 2 As shown in the flowchart, the entire process is a closed loop, from event reporting, task planning, route customization, to the synchronization of results information. Starting with "event reporting," the process goes through stages such as "event handling," "task planning," "automatic route planning," and "drone takeoff," ultimately generating results information (photos / videos) and automatically associating them with the task / event. This not only achieves seamless integration between event handling and drone operation but also forms a complete closed loop, ensuring that every event receives effective processing and feedback, thus improving the efficiency and accuracy of urban management.

[0036] This method first adds polygon boundary points to the map and uses specialized tools to convert these points on the 3D surface into planar polygons in a 2D coordinate system. Next, the point with the shortest distance from the UAV's takeoff point to the polygon is determined as the starting point of the flight path. Then, the longest distance between waypoints and the slope of the parallel line of the flight path angle are calculated based on the lateral overlap rate and sampling accuracy. Rays are formed along the slope direction from the starting waypoint and translated to obtain intersections with polygon line segments. After iterating through the entire polygon to obtain all intersections, they are connected pairwise to form the flight path. Finally, the coordinates of the flight path points are converted to latitude and longitude and displayed on a 3D map. The generated parallel flight path ensures comprehensive coverage of the polygon area, improving the completeness and accuracy of the inspection. This complex and intelligent flight path generation process automates the conversion from task requirements to actual flight paths, improving the efficiency and accuracy of flight path planning while reducing the introduction of human error.

[0037] The specific steps are as follows: (1) Event reporting: After the system is started, relevant personnel can report events to the event processing module through the system event reporting function; (2) Event handling: The system issues an event and determines whether a drone is needed; if a drone is needed, the drone flight operation is initiated; if a drone is not needed, the result is directly fed back to the system.

[0038] The system of this invention supports different distribution modes such as immediate distribution, scheduled distribution, and periodic distribution, which can cover all business scenarios in the process of urban management, while reducing the reliance on human experience.

[0039] (3) Task planning: Select the task mode, determine whether to select an existing route, select the task scenario, preview and submit after confirming the task, complete the task approval, and enter the UAV flight operation.

[0040] In this invention, various scenarios for actual task handling are collected. In this step, the collected task scenarios can be directly selected. The system automatically recommends the drone's flight altitude and speed, the drone's camera parameters, and payload equipment information according to the selected scenario, which can minimize the reliance on human experience.

[0041] If you do not select an existing route, you will proceed to the route planning process described below.

[0042] (4) Route planning: In traditional route planning, coordinates are manually selected one by one on a two-dimensional map and the order between coordinates is specified. However, this invention automatically calculates or defines the camera parameters in the task scene, such as the altitude, heading angle, and lateral overlap rate to be sampled, and then automatically generates the UAV route. This is also an important innovation of this invention.

[0043] A 3D map was constructed using a 3D modeling module, which supports the direct definition of the cruise area on the 3D map. The spatial geographic information of the 3D map enhances the ability to perceive the situation of obstacles in flight route planning, shifts flight risks to the left, and greatly reduces the safety risks during UAV flight.

[0044] like Figure 3 As shown, the specific implementation steps for route planning are as follows: a) Calculate or define the required sampling precision, altitude, flight path angle, and lateral overlap rate based on camera parameters; b) Add polygonal boundary points to the 3D map. These points are on a 3D curved surface on Earth and need to be converted to a 2D coordinate system, becoming a plane; c) Based on b), a planar polygon is obtained. Then, the take-off point of the unmanned aerial vehicle is determined by the location of the hangar. The point with the shortest distance from the take-off point to the polygon is defined as the starting point of the flight path, which is also the point with the minimum distance from the aircraft to the flight path. d) Based on the lateral overlap rate and sampling accuracy in a), calculate the longest distance S between the two waypoints, and based on the planar polygon obtained in b), calculate the slope of the boundary line. e) A ray is formed from the waypoint at the starting point along the slope direction. Then, this ray is translated by a vertical distance S. Based on the longest distance S between the two waypoints obtained in d) and the slope, the intersection point is obtained and recorded, which becomes a waypoint H1 on the route. f) If the ray is parallel to the line segment or has no intersection within a perpendicular distance S, then the flight of that side is considered complete, and a new boundary needs to be obtained as the line segment for the next cycle. g) Then, continue moving this ray perpendicular to the distance S and the line segment of the polygon, continuously obtaining intersection points, and finally checking the entire polygon. Stop the loop when the line segment of the polygon becomes the first line segment; h) After obtaining all the intersection points on the polygon in the previous step (the number of intersection points is 2N), find the first corresponding point from the initial point in the opposite direction according to the slope and record it as the second waypoint (the vertical distance of movement corresponding to the waypoint is S). Continue to cycle forward and backward, connecting the two in pairs to form a flight path. i) Finally, convert all points to latitude and longitude using coordinates, which will appear as parallel flight routes on the 3D map. An example of a flight route is shown below. Figure 4 As shown.

[0045] (5) Drone flight: After the task is approved, it is sent to the system to notify the drone to take off and automatically associate the results information such as photos / videos with the task or time, notify the system, and complete the patrol flight.

[0046] In summary, this invention enables intelligent planning of drone-based urban management patrol and inspection tasks based on geographic information. By integrating urban management patrol and inspection business processes, multi-mode task deployment, customized route planning using 3D maps, and task scenario management, it achieves a high degree of integration between drone operations and business management, diversified and timely task handling, enhanced situational awareness capabilities in route planning, and knowledge management of route planning. This reduces the business threshold for route planning and improves patrol and inspection efficiency and safety.

[0047] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by equivalent substitution or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A geographic information-based drone-based urban management scene patrol and inspection system, characterized in that, include: Event handling module: Used to receive reported events, perform preliminary processing and judgment on the events, determine whether drones are needed for handling, and transmit the processing results to the task management module; Task Management Module: Responsible for managing UAV missions, planning missions according to instructions from the Event Processing Module, and interacting with the Flight Path Planning Module to determine the specific parameters of the UAV flight mission; Flight route planning module: Calculates safe and efficient drone flight routes using camera parameters and geographic information and sends them to the drone flight execution module; UAV Flight Module: Executes UAV flight missions, conducts flight operations according to the routes determined by the route planning module, collects relevant results information and associates it with tasks or events, and notifies the system of task completion status; 3D modeling module: Performs 3D modeling of the target area, constructs 3D maps, and provides geographic information data support for the system; The specific implementation steps of the route planning module are as follows: a) Calculate or define the required sampling precision, altitude, flight path angle, and lateral overlap rate based on camera parameters; b) Add polygon boundary points to the 3D map, and use a tool to convert it into a 2D coordinate system, turning it into a plane; c) Based on b), a planar polygon is obtained. Then, the take-off point of the unmanned aerial vehicle is determined by the location of the hangar. The point with the shortest distance from the take-off point to the polygon is defined as the starting point of the flight path, which is also the point with the minimum distance from the aircraft to the flight path. d) Based on the lateral overlap rate and sampling accuracy in a), calculate the longest distance S between the two waypoints, and based on the planar polygon obtained in b), calculate the slope of the boundary line; e) A ray is formed from the waypoint at the starting point along the slope direction. Then, this ray is translated by a vertical distance S. Based on the longest distance S between the two waypoints obtained in d) and the slope, the intersection point is obtained and recorded, which becomes a waypoint H1 on the route. f) If the ray is parallel to the line segment or has no intersection within a perpendicular distance S, then the flight of the polygon's edge is considered complete, and a new boundary needs to be obtained as the line segment for the next cycle. g) Then, continue moving this ray perpendicular to the distance S and the line segment of the polygon, continuously obtaining intersection points, and finally judging the entire polygon until the line segment of the polygon becomes the first line segment, then stop the loop; h) After obtaining all 2N intersection points on the polygon in the previous step, find the first corresponding point from the initial point in the opposite direction according to the slope and record it as the second waypoint. Continue to cycle forward and backward, connecting the two in pairs to form a route. i) Finally, all points are converted from coordinates to latitude and longitude and displayed on a 3D map to form parallel flight paths.

2. The UAV-based urban management scene patrol and inspection system according to claim 1, characterized in that, The specific parameters of the flight mission in the mission management module include: the UAV's flight altitude, speed, camera parameters, and payload equipment information.

3. The UAV-based urban management scene patrol and inspection system according to claim 1, characterized in that, The results collected by the drone flight module are photos or videos.

4. The UAV-based urban management scene patrol and inspection system according to claim 1, characterized in that, The task management module supports at least three task delivery modes: immediate delivery, scheduled delivery, and periodic delivery.

5. The inspection method based on the drone-based urban management scene patrol and inspection system according to any one of claims 1 to 4, characterized in that, Includes the following steps: (1) Event reporting: The system is started, and relevant personnel report the event to the event handling module; (2) Event handling: The system issues an event and determines whether a drone is needed; (3) Task planning: Select the task mode, determine whether to select an existing route, select the task scenario, preview and submit after confirming the task, complete the task approval, and enter the UAV flight operation. (4) Flight path planning: Automatically calculate or define the camera parameters in the mission scenario, such as the altitude, heading angle, and lateral overlap rate to be sampled, and automatically generate the UAV flight path; (5) Drone flight: After the task is approved, it is sent to the system to notify the drone to take off and automatically associate the results information such as photos / videos with the task or time, notify the system, and complete the patrol flight.

6. The inspection and patrol method according to claim 5, characterized in that, If drones are needed for the response, the information is transmitted to the task management module to add a drone flight mission; if not, the processing results are fed back to the system to complete the event loop.

7. The inspection and patrol method according to claim 5, characterized in that, When planning a mission, if an existing flight path is selected, the system will automatically recommend the drone's flight altitude and speed, camera parameters, and payload equipment information according to the selected scenario. If no existing route is selected, the route planning process will begin.

8. The inspection and patrol method according to claim 5, characterized in that, The route planning is achieved based on a 3D map constructed using a 3D modeling module.

9. The inspection and patrol method according to claim 5, characterized in that, The specific steps for implementing the route planning are as follows: a) Calculate or define the required sampling precision, altitude, flight path angle, and lateral overlap rate based on camera parameters; b) Add polygon boundary points to the 3D map, and use a tool to convert it into a 2D coordinate system, turning it into a plane; c) Based on b), a planar polygon is obtained. Then, the take-off point of the unmanned aerial vehicle is determined by the location of the hangar. The point with the shortest distance from the take-off point to the polygon is defined as the starting point of the flight path, which is also the point with the minimum distance from the aircraft to the flight path. d) Based on the lateral overlap rate and sampling accuracy in a), calculate the longest distance S between the two waypoints, and based on the planar polygon obtained in b), calculate the slope of the boundary line; e) A ray is formed from the waypoint at the starting point along the slope direction. Then, this ray is translated by a vertical distance S. Based on the longest distance S between the two waypoints obtained in d) and the slope, the intersection point is obtained and recorded, which becomes a waypoint H1 on the route. f) If the ray is parallel to the line segment or has no intersection within a perpendicular distance S, then the flight of the polygon's edge is considered complete, and a new boundary needs to be obtained as the line segment for the next cycle. g) Then, continue moving this ray perpendicular to the distance S and the line segment of the polygon, continuously obtaining intersection points, and finally judging the entire polygon until the line segment of the polygon becomes the first line segment, then stop the loop; h) After obtaining all 2N intersection points on the polygon in the previous step, find the first corresponding point from the initial point in the opposite direction according to the slope and record it as the second waypoint. Continue to cycle forward and backward, connecting the two in pairs to form a route. i) Finally, all points are converted from coordinates to latitude and longitude and displayed on a 3D map to form parallel flight paths.

Images