Small unmanned aerial vehicle narrow-distance space safety patrol method

Abstract

The invention discloses a small unmanned aerial vehicle narrow-distance space safety patrol method, relates to the technical field of space monitoring, and is used for solving the problem that a patrol path of an unmanned aerial vehicle in a transformer substation is not clearly generated. According to the substation equipment layout, inspection points are divided into safety points, dangerous points and transition points, an inspection path is optimized through a shortest path algorithm, flight safety and task efficiency and energy consumption minimization are ensured, inspection tasks are allocated to unmanned aerial vehicles according to priorities, emergency tasks are ensured to be processed preferentially, and then environment changes are monitored in real time, so that the safety of the substation equipment is ensured. According to the method, the unmanned aerial vehicle can rapidly and accurately arrive at the new dangerous point through the real-time monitoring of the unmanned aerial vehicle, new dangerous points and obstacles are timely identified, the flight path is dynamically adjusted through transition point optimization, it is ensured that the unmanned aerial vehicle can rapidly and accurately arrive at the new dangerous points, finally, high-precision positioning of the unmanned aerial vehicle in a narrow-distance space is ensured through combination of RTK and a vision-assisted positioning system, the flight state is dynamically corrected through a PID control algorithm, and it is ensured that the path is accurate and stable.

Description

Technical Field

[0001] The present invention relates to the field of space monitoring technology, and more specifically, to a method for narrow-range space safety inspection using a small unmanned aerial vehicle. Background Art

[0002] Drone inspection technology is widely used in the monitoring and maintenance of various facilities, especially in the power, petroleum, communications and other industries. By carrying high-precision sensors and image processing systems, drones can achieve remote monitoring of equipment, real-time data collection and fault detection.

[0003] Deficiencies in existing technologies: When dynamic environmental changes and emergencies occur during drone inspections, traditional path planning methods mostly rely on static inspection route designs, and lack the ability to generate new danger points in real time and adjust inspection routes for emergencies (such as power accidents or equipment failures). Once new danger points appear, existing systems often find it difficult to adjust inspection routes in a timely and accurate manner, especially when sudden inspection tasks require a quick response. It is impossible to efficiently select appropriate points to optimize the drone's flight altitude, speed and attitude, and it lacks real-time adaptability to dynamic environmental changes, making it difficult to ensure flight safety and efficiency in complex power facilities. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, there is a solution as follows to solve the problem of unclear generation of patrol paths of UAVs in substations in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A small UAV narrow-range space safety inspection method includes the following steps: analyzing the substation equipment layout, dividing the equipment nodes into safe points, dangerous points and transition points, and determining the UAV inspection path through the shortest path algorithm, and assigning the inspection task to the UAV according to the importance of the equipment, the danger level of the inspection point and the timeliness of the task; Monitor substation environment changes in real time, detect new danger points and obstacles, and optimize and adjust the inspection path through transition points. The drone will inspect new danger points along the newly generated inspection path. The RTK and vision-assisted positioning systems are combined to determine the position of the UAV, and the UAV's path flight is monitored based on the PID control algorithm to dynamically correct the UAV's flight status.

[0006] In a preferred embodiment, the substation equipment layout is analyzed, the equipment nodes are divided into safe points, dangerous points and transition points, and the drone inspection path is determined by the shortest path algorithm. The specific steps are as follows: The equipment nodes in the substation are divided into three types of inspection points, including safe points, dangerous points, and transition points; The safety point is the area where the drone can fly safely and adjust its position. The danger point is the location where the equipment node inspection is required and the area where the drone arrives and performs the inspection task. The transition point is the node where the drone adjusts its flight status. It is located above the safety point. The drone adjusts its flight altitude, speed, and attitude at the transition point. The equipment node layout of the substation is converted into a spatial graph model. Each equipment or inspection point corresponds to a node of the spatial graph model, and the lines between the nodes represent the inspection path as edges. The shortest path is determined based on the equipment location of the substation, the actual distance of the inspection path, and the type of inspection task of the equipment. When calculating the path, the optimal path is determined based on the task execution cost of the dangerous point and the transition point and the shortest path of the inspection task. The formula is: ,in, is the flight distance between two consecutive inspection points from inspection point i to i+1 in the path, is the inspection cost of the jth dangerous point, which represents the time, energy or resources required to perform the task at the inspection point. is the cost weighting factor, which is used to balance the path length and task execution cost; The final optimal path is determined based on the total path length and task execution cost.

[0007] In a preferred embodiment, the inspection task is assigned to the drone according to the importance of the equipment, the danger level of the inspection point, and the timeliness of the task. The specific steps include: The importance of equipment is assessed as key equipment and auxiliary equipment. Key equipment includes the main transformer and switchgear of the substation, and auxiliary equipment includes sensors and cooling system equipment. Inspection points are divided into high-risk inspection points and low-risk inspection points according to their risk level. High-risk inspection points include high-voltage power areas and faulty equipment, while low-risk inspection points include regular inspection points or equipment areas without faults. Task timeliness requirements include emergency tasks and routine tasks. Emergency tasks include equipment failures, fire hazards, and other tasks that need to be handled immediately. Routine tasks include regular inspections and non-emergency equipment inspections. In the inspection route planning, based on the evaluation results, the task is assigned to the most suitable UAV according to the urgency, danger and importance of the equipment at each inspection point, and constraints are set during the path planning process.

[0008] In a preferred embodiment, the substation environment changes are monitored in real time, the newly generated dangerous points and obstacles are detected, and the inspection path is optimized and adjusted through the transition points. The drone inspects the new dangerous points according to the newly generated inspection path. The specific steps are as follows: Through the analysis of sensor data and real-time feedback, newly generated dangerous points are identified and their locations are calibrated. The surrounding environment is also checked for obstacles to obtain their locations. Update the original inspection path according to the identified new dangerous point locations and obstacle positions, and calculate the safe transition points; Use the optimization algorithm to recalculate the path, and the calculation expression is: ,in, represents a safe transition point in the drone’s path, It is the flight distance between two consecutive inspection points from the current inspection point i to the target inspection point i+1. DTO represents the obstacle distance on the path. HA is the flight altitude that needs to be adjusted. Indicates the importance of obstacle distance, Indicates the importance of height adjustment; The position of transition points along the UAV flight path is optimized based on the safe transition points, and a shortest path to the dangerous point is planned.

[0009] In a preferred embodiment, planning a shortest path to a dangerous point includes the following steps: All current safe points , as the next transition point, by calculating the distance between the current position of the drone and each safety point, determine the nearest safety point, and use the nearest safety point as the starting point, where, is the current safe point coordinate, , , Respectively represent the spatial coordinate position of the current safety point in the horizontal direction, vertical direction and height direction perpendicular to the horizontal plane; According to the current position of the drone and the position of the newly generated dangerous point, determine the distance between each safe point and the new dangerous point; By comparing the distances between all safe points and the newly generated dangerous points, the closest safe point is selected as the next target for the drone to fly; After selecting the nearest safe point, the flight status of the drone is optimized, and the inspection path is corrected according to the target safe point, transition point and current flight status; The flight status includes altitude, pitch angle, roll angle, yaw angle and flight speed.

[0010] In a preferred embodiment, the position of the UAV is determined by combining RTK and vision-assisted positioning systems, and the path flight of the UAV is monitored according to the PID control algorithm to dynamically correct the flight state of the UAV, including the following steps: The geographic location coordinates are obtained through the RTK system, the visual positioning data are obtained through the visual assisted positioning system, and the geographic location coordinates and the visual positioning data are combined to obtain the real-time position of the drone; Calculate the error between the current position of the drone and the target position; The control command is generated through the PID control algorithm, the position error is converted into control instructions, and the flight status of the UAV is adjusted.

[0011] The technical effects and advantages of a small unmanned aerial vehicle narrow-space safety patrol method of the present invention are as follows: The invention realizes the safe inspection of the narrow space of the substation through precise path planning, dynamic adjustment and high-precision positioning. Firstly, according to the layout of the substation equipment, the inspection points are divided into safe points, dangerous points and transition points. The inspection path of the UAV is optimized by the shortest path algorithm to ensure flight safety and time efficiency of task execution. Meanwhile, the flight cost and energy consumption are comprehensively considered, and the inspection tasks are assigned to the UAV according to priority. Secondly, the internal environmental changes of the substation are monitored in real time, and the newly generated dangerous points and obstacles are detected in time. The flight path is optimized and adjusted by the transition point to ensure that the UAV can quickly and accurately reach the new dangerous point and complete the inspection task. Finally, the RTK and visual assisted positioning system are combined to ensure that the UAV can accurately locate and stably fly in the narrow space. The flight state of the UAV is dynamically corrected by the PID control algorithm to ensure that the flight path is accurate and stable, thereby improving the adaptability and inspection efficiency of the UAV in the complex substation environment, being able to respond to environmental changes in time and adjust the inspection path, minimizing the time and energy consumption in task execution, ensuring that the UAV can accurately reach the inspection point in a narrow space, and improving the safety and reliability of the inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flowchart of a method for safely patrolling a narrow space using a small unmanned aerial vehicle. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] In order to achieve the above objectives, Figure 1 A structural schematic diagram of a small UAV narrow-space safety patrol method of the present invention is given, which specifically includes the following steps: Analyze the substation equipment layout, divide the equipment nodes into safe points, dangerous points and transition points, and determine the drone inspection path through the shortest path algorithm. Assign inspection tasks to drones based on the importance of the equipment, the danger level of the inspection points and the timeliness of the tasks. Monitor substation environment changes in real time, detect new danger points and obstacles, and optimize and adjust the inspection path through transition points. The drone will inspect new danger points along the newly generated inspection path. The RTK and vision-assisted positioning systems are combined to determine the position of the UAV, and the UAV's path flight is monitored based on the PID control algorithm to dynamically correct the UAV's flight status.

[0015] Step 1: Perform initial inspection route planning and task allocation. In the process of initial route planning for narrow-space inspection of small UAV substations, we must first determine the inspection task objectives and the spatial layout of equipment in the substation. For each inspection route, based on the equipment layout, the nature of the inspection points (safe points, dangerous points, transition points) and the flight characteristics of the UAV, a mathematical model that comprehensively considers path optimization, flight safety, and time efficiency is constructed. Through this model, reasonable task allocation and inspection route planning are achieved. The specific steps are as follows: To carry out mission objectives and spatial modeling, the drone starts from the starting point (usually a reference point or safety zone of the substation) and divides the equipment nodes in the substation into three types of inspection points, namely, safe points, dangerous points and transition points. Safe points are areas where drones can fly safely and adjust their positions (such as areas with no obvious obstacles around the equipment and good air circulation); dangerous points are points where equipment inspections are required, usually high-voltage areas of the equipment or locations where faulty equipment is located, requiring drones to accurately reach and perform inspection tasks; transition points are nodes where drones adjust their flight status, usually located above safe points, allowing drones to adjust their flight altitude, speed, attitude, etc., to quickly respond to emergencies or path changes; The equipment node layout of the substation is converted into a spatial graph model, in which each device or inspection point corresponds to a node, and the lines between the nodes represent feasible inspection paths. The transformation of the equipment layout is modeled through a spatial coordinate system. For example, a three-dimensional coordinate system is used to map the specific location and height information of the equipment to the actual environment, and combined with geographic information system (GIS) data, a three-dimensional model that can reflect the real environment is generated; The initial path planning will be carried out using the shortest path algorithm in graph theory. Considering the space limitations and flight constraints of the substation, the algorithm needs to be selected based on the equipment location of the substation, the actual distance of the inspection path, and the type of inspection task of the equipment. When calculating the path, the task execution cost of the dangerous point and the transition point needs to be taken into account to ensure the shortest path and the lowest energy consumption for each inspection task. Each inspection point must be connected to the next inspection point by a legal path. The shortest path algorithm in graph theory can be used to model the path selection, defining each inspection point as a node in the graph and the path as an edge. The path planning result is a series of sequential inspection points. The drone needs to fly in sequence and reach each equipment inspection point to perform the task. Under the given substation layout and task requirements, find an optimal path in the spatial graph model to minimize the time, energy consumption and risk of the drone inspection process. The task target path can be expressed by the following optimization problem, the formula is: ,in, is the flight distance between two consecutive inspection points from inspection point i to i+1 in the path, is the inspection cost of the jth dangerous point, that is, the time, energy or other resources required to perform the task at this point, N is the number of inspection points in the inspection path, M is the number of dangerous points, is the cost weighting factor, which is used to balance the path length and task execution cost. , it can adjust the importance of different tasks and avoid excessive bias towards one aspect due to simple path optimization (such as ignoring the accuracy of equipment inspection due to too fast flight speed). By weighing the total length of the path and the cost of task execution, the optimal final path can be determined; After the path planning is completed, the task allocation of drone inspection is carried out. In the planning of multiple inspection routes, the task allocation of drones in different routes needs to be determined according to the complexity and priority of the tasks. The task allocation will be carried out according to the importance of the equipment, the danger level of the inspection point and the timeliness of the task. For some urgent equipment problems (such as faulty equipment or dangerous points), drones will be assigned first and dispatched to perform relevant inspection tasks; Equipment importance is assessed as key equipment and auxiliary equipment. Key equipment includes the main transformer and switchgear of the substation. Failure of these equipment may lead to large-scale power outages. Therefore, these equipment have a higher inspection priority and need to be assigned to drones first. Auxiliary equipment such as sensors and cooling systems are important, but their failure will not directly lead to the shutdown of the entire station, so their inspection priority is relatively low.

[0016] Inspection points are divided into high-risk inspection points and low-risk inspection points. High-risk inspection points include high-voltage power areas, faulty equipment, etc. Inspections at these points require special attention to safety when the drone is flying, so they need to be given priority when assigning tasks and the most suitable drone should be assigned to perform them. Low-risk inspection points, such as regular inspection points or equipment areas without faults, have relatively low task priorities and can be assigned ordinary inspection tasks. Task timeliness requirements include emergency tasks and routine tasks. Emergency tasks, such as equipment failures and fire hazards, are tasks that need to be handled immediately and are given a higher priority. They are usually assigned to the nearest drone and started quickly. Routine tasks, such as regular inspections and non-emergency equipment inspections, can be carried out in a lower priority time period according to the drone's task schedule. In the planning of multiple inspection routes, based on the above evaluation results, the task allocation module will automatically allocate tasks to the most suitable drones according to the urgency, danger and importance of equipment at each inspection point. For example, for high-priority tasks, the scheduling system gives priority to the drone that is currently closest and in good flight status to ensure the fastest response. If there are multiple drones that can perform the same task, the task allocation system can further optimize the scheduling of drones based on factors such as flight path, energy status and task timeliness; It should be noted that in addition to the optimization goal, constraints also need to be considered in the path planning process, such as the altitude limit of the drone flight, the safety constraints of the flight status, the position constraints of the transition point, and the requirements for avoiding obstacles. The specific constraints are as follows: the drone inspection path needs to avoid obstacles in the substation, especially in narrow spaces, where the flight space is very limited. Therefore, the path planning algorithm needs to ensure that the drone will not collide with obstacles or other equipment during flight, which is verified by collision detection algorithms (such as path planning based on the grid method).

[0017] Energy consumption and mission constraints, that is, considering the energy limitations of the drone, the flight time must be kept to a minimum during path planning to avoid running out of power during flight. This constraint can be met by calculating the flight time and required energy for each segment in the path. For example, the flight space constraint, when flying a drone, it is not allowed to exceed the specified altitude range or fly into inaccessible areas (such as the high-voltage area in the substation), which can be expressed as the following constraint: ,in, is the flight height of the ith inspection point, and are the minimum and maximum altitudes of the flight respectively; The core goal of path planning and task allocation is to find the optimal path that can ensure flight safety and minimize the task cost. In this case, the A* algorithm is used for path selection. By combining the actual cost g(n) of the known path and the estimated cost h(n) from the current node to the target node, the optimal path is quickly calculated. The cost function of the A* algorithm is as follows: ,in, is the actual flight cost from the starting point to node n, is the estimated cost from node n to the target point, which can take into account the execution complexity and inspection cost of the task; By iterating the A* algorithm, the shortest path is ensured, and the path selection is adjusted in real time to respond to new environmental data and task requirements; The above steps realize the initial path planning and task allocation of the narrow-space inspection task of the substation based on small drones. The key to path planning is to comprehensively consider flight safety, task execution cost and shortest path. At the same time, the heuristic algorithm (A* algorithm) is used for calculation. By dynamically updating the path and task cost, it can respond to emergencies in the substation environment and ensure that the drone is always in a safe state during the inspection process and completes the task efficiently.

[0018] After completing the initial inspection route planning and task allocation, the dynamic path adjustment and transition point optimization stage is entered. The goal of this stage is to adjust the inspection path of the drone in real time according to the new situation when the internal environment of the substation changes (such as newly generated dangerous points, equipment failures, etc.), so as to ensure that the drone can quickly and safely reach the new dangerous points and complete the inspection tasks. This process involves dynamic path optimization, transition point adjustment and real-time flight status management. The following are the specific steps: Step 2: Dynamically adjust the path and optimize the transition points. When the drone performs inspection tasks, it may encounter new dangerous points or equipment failures, causing the original inspection route to no longer be applicable. Therefore, at this stage, it is necessary to first use the real-time monitoring system and the drone's sensors (such as visual cameras, lidar, etc.) to scan the environment and identify new dangerous points. Specifically, the detection process of new dangerous points includes real-time status monitoring of power equipment in the substation. For example, the switchgear in the substation may fail or short-circuit, resulting in the generation of new dangerous points. At this time, the newly generated dangerous points are identified and their locations are calibrated through sensor data and real-time feedback analysis. , where x, y, z are the coordinates of the new dangerous point in three-dimensional space; At the same time, in order to maintain flexible adjustment of the drone inspection path, the sensor system also needs to detect obstacles in the surrounding environment ( ), the types of obstacles include fixed obstacles (such as power equipment) and dynamic obstacles (such as flying birds or high-voltage wires, equipment brackets, etc.). The location and size of obstacles will affect the inspection path planning, so it is necessary to obtain the location of obstacles through real-time sensor data and impact sphere information.

[0019] Once a new danger point is identified , and possible obstacle locations , update the original inspection path and calculate the appropriate transition point, transition point It is a key node in the UAV flight process. It adjusts the inspection path of the UAV and reselects the appropriate transition point. The transition point is the point in the path where the flight altitude, speed and attitude can be adjusted to help the UAV safely transition from one inspection point to another. Especially when the environment changes suddenly, the recalculation of the transition point is completed according to the new danger points, obstacles and environmental changes. The purpose of path update is to start from the current inspection point To a new danger point The inspection path is optimized. When calculating the updated inspection path, new obstacles and flight safety requirements are taken into account, and the path is recalculated using an optimization algorithm (such as the heuristic A* algorithm or the Dijkstra algorithm) to ensure that the drone can safely reach the new dangerous point in the shortest time. In the calculation of the updated path, not only the flight distance is considered, but also the flight difficulty and cost of the path are evaluated.

[0020] For example, possible obstacles and equipment layout changes on the path will increase the difficulty of flying, so adjustments need to be made using the following formula: ,in, represents a safe transition point in the drone’s path, It is the flight distance between two consecutive inspection points from the current inspection point i to the target inspection point i+1. DTO represents the obstacle distance on the path. HA is the flight altitude that needs to be adjusted. Indicates the importance of obstacle distance, Indicates the importance of height adjustment, adjust the weight and The choice will balance path selection and flight altitude optimization according to the complexity of the environment and the priority of the mission; By recalculating and optimizing the position of the transition points in the drone's flight path, it can be ensured that the drone can bypass obstacles and successfully complete the flight process from the current point to the new dangerous point.

[0021] Further, new danger points After being identified, a shortest path to the dangerous point can be planned to ensure that the drone can reach the dangerous point for inspection as quickly as possible, and all current safety points ( ) as a possible transition point, by calculating the distance between the current position of the drone and each safety point, determine the nearest safety point, and use the safety point as the starting point to quickly go to the dangerous point, where, is the coordinate of the current safe point, , , Respectively represent the spatial coordinate position of the current safety point in the horizontal direction, vertical direction and height direction perpendicular to the horizontal plane, that is, the position representation in three-dimensional coordinates. The representation of similar positions that appear later is the same; Assume that the current position of the drone is , the newly generated danger point is , each safety point There is a distance from the new danger point: ; By comparing the distance between all safe points and the newly generated dangerous points, the closest safe point is selected. , as the next target of the drone: ; After selecting the optimal safety point, the drone will adjust the inspection path (based on the setting of the transition point) to quickly reach the safety point and prepare for the next path planning.

[0022] After selecting the optimal safety point, the flight control system needs to optimize the flight status of the drone by dynamically adjusting the current path, and make corresponding path corrections based on the target safety point, transition point, and current flight status (such as speed and attitude); Based on the selected nearest safe point, the flight control system needs to plan a path from the current position of the drone to the target safe point, and set important positions in the path (such as turning points, flight altitude change points, etc.) as transition points ; The inspection path adjustment formula can be dynamically planned based on the path optimization algorithm (such as A* algorithm, Dijkstra algorithm), taking into account obstacles and environmental factors, and calculating the shortest and safest path. In this path, the selection of each transition point is calculated based on the current flight status, target path and flight capability to ensure that the flight altitude, speed, attitude and other parameters after the path adjustment meet the mission requirements; When the drone flies along the planned path, the flight control system needs to provide real-time status feedback and adjustments for each transition point. When the drone flies to a certain transition point, the flight control system will adjust the flight status (such as altitude, pitch angle, roll angle, yaw angle) and flight speed based on real-time sensor data (such as airflow, obstacles, flight altitude changes, etc.) to ensure a smooth transition to the next flight state.

[0023] In the process of path adjustment, the optimization of transition points is not only to calculate the optimal position of transition points, but also to adjust the flight status. The optimization of transition points and flight status management mainly include the following aspects: Altitude adjustment: In a complex substation environment, obstacles, equipment, and electromagnetic interference may require the drone to adjust its altitude to bypass obstacles or avoid electromagnetic interference sources. The selection of transition points must take these altitude adjustments into account. For example, in the presence of obstacles, the flight altitude may need to be increased, so an appropriate transition point is selected for altitude adjustment. The altitude adjustment can be calculated in a way that minimizes the energy consumption of the flight: ,in, and are the target flight altitude and the current flight altitude respectively. is the weight coefficient of flight energy consumption. This formula can help determine the minimum energy consumption and shortest path of the UAV during flight. Flight speed adjustment: There may be requirements for flight speed in the path, especially when it involves complex terrain and sudden obstacles. The flight speed of the drone needs to be adjusted according to the complexity of the path, the number of obstacles, and the priority of the dangerous points. The transition point selection also needs to be adjusted according to the flight speed of the drone. If the new path bypasses obstacles or requires a large range of altitude adjustments, the flight speed may need to be appropriately reduced to ensure flight stability. The optimization of flight speed can be determined by calculating the relationship between the maximum flight speed and the energy consumption of the inspection path: ,in, represents the energy consumption on path j. The goal of flight speed optimization is to reduce energy consumption while ensuring flight safety and mission execution efficiency; Flight state adjustment: Flight state adjustment is another key content of transition point optimization. By optimizing the flight angle and direction, the drone can be ensured to transition smoothly and avoid sudden acceleration or loss of control. The optimization of flight state can be achieved by adjusting the pitch angle and roll angle of the drone. The optimization of flight state can be calculated by the following formula: ,in, is the flight state adjustment amount on path k, and the optimization goal is to reduce the energy and time required for flight state adjustment.

[0024] After completing the path adjustment and transition point optimization, the updated path is verified to ensure that all flight conditions (such as altitude, speed, attitude, etc.) still meet safety requirements in the new path and that the new path can quickly and effectively complete the inspection task. The details are as follows: Flight safety verification, that is, simulating new inspection routes through a virtual simulation environment or flight simulation system to detect whether the route will collide or violate flight restrictions, for example, to ensure that the drone will not enter dangerous high-voltage electrical areas or under equipment. During the safety verification process, factors such as wind speed and air resistance during flight must also be considered; Mission execution verification, that is, after the path verification is passed, it is also necessary to confirm whether the new inspection path can efficiently complete the task, especially the inspection of new dangerous points. During the verification process, check whether the drone can accurately reach the target dangerous point and complete the required inspection tasks (such as taking pictures, data collection, etc.); After verification, the path adjustment results will be passed to the flight control system to start the actual inspection task. The results of dynamic path adjustment and transition point optimization will directly affect the actual flight performance of the drone, ensuring the continuity and accuracy of task execution. During the dynamic path adjustment and transition point optimization stage, through real-time environmental monitoring and danger point detection, the inspection path is recalculated and the selection of transition points is optimized, thereby ensuring that the UAV can quickly and safely adjust the path when encountering emergencies. The optimization of flight status (including altitude, speed, attitude, etc.) helps to improve inspection efficiency and flight stability.

[0025] Step 3: Perform high-precision positioning and flight control. When the UAV performs narrow-space inspection tasks in substations, ensure that the UAV can accurately reach the inspection point. The coordination of high-precision positioning technology and flight control system can ensure that the inspection path of the UAV is accurate and the tasks are performed stably during the inspection process. Use high-precision positioning technology (such as RTK, visual assisted positioning, etc.) for precise navigation and ensure accurate control of the UAV's flight status. The specific steps are as follows: RTK positioning technology is the main positioning technology used by drones during flight. The RTK system provides more accurate geographic location coordinates ( ) Help the flight control system ensure that the drone flies along the planned path; The visual assisted positioning system can be combined with the RTK system to further improve the reliability of positioning, especially in environments where the GPS signal is weak or blocked. Visual positioning obtains environmental information through image recognition, feature point matching and other methods to provide auxiliary positioning to ensure the stable flight of the drone in a narrow space. The visual positioning data is ; Comprehensively consider RTK and visual positioning data and fuse them to get the final real-time position of the drone ; The precise location data output by the high-precision positioning system will be passed to the flight control system for dynamic tracking of the inspection path. At this stage, the flight control system is based on the real-time position of the drone. The drone can perform path tracking control at the target location (inspection point or transition point) to ensure that the drone accurately executes the path.

[0026] The core task of the flight control system is to dynamically correct the state of the drone through a high-precision PID control algorithm to ensure that the drone tracks the predetermined inspection path. The specific control steps are: Position error calculation, that is, calculating the error between the current position of the drone and the target position (the expected position): ,in, is the target location (such as a checkpoint or transition point), is the current real-time position of the drone. Through error calculation, the flight control system can evaluate the deviation between the drone and the target point; Control command generation, that is, through the PID controller, the position error is converted into control instructions (such as speed, acceleration, attitude angle, etc.), so that the drone can adjust the flight state to eliminate the deviation. Control instructions of PID controller for: ,in, They are proportional, integral and differential gain coefficients, and proper adjustment of these coefficients can achieve smooth and accurate path tracking.

[0027] The flight control system estimates the state based on the data from the RTK positioning system and the auxiliary positioning system (such as the current speed, acceleration, attitude angle, etc. of the drone) to determine whether the current flight state meets the inspection requirements. If a flight error occurs (such as inaccurate positioning accuracy), the system will correct the inspection path based on the feedback information. Based on the feedback information, the PID controller continuously updates the control command u(t) and sends it to the flight execution system to adjust the flight state of the drone in real time and monitor the flight data, including battery power, flight time, flight stability, etc., to ensure that the drone can complete the mission without failure or loss of connection during the flight. The flight monitoring system will continuously monitor the flight state of the drone and make adjustments or terminate the flight when necessary. The high-precision positioning and flight control steps are designed to provide accurate flight positions through RTK systems and auxiliary positioning technology, and to ensure that the drone can safely and accurately perform tasks along the predetermined path through dynamic flight control (such as PID control algorithm, flight state adjustment, etc.) and obstacle avoidance systems. Through real-time status monitoring and feedback correction, the flight control system can make timely adjustments based on the flight conditions of the drone.

[0028] It should be noted that the threshold information in this embodiment is pre-set by professionals and will not be explained in detail here. Some parameter English letters in the embodiments have the same situation, but different meanings are explained when used, which will not be explained one by one here.

[0029] The invention realizes the safe inspection of the narrow space of the substation through precise path planning, dynamic adjustment and high-precision positioning. Firstly, according to the layout of the substation equipment, the inspection points are divided into safe points, dangerous points and transition points. The inspection path of the UAV is optimized by the shortest path algorithm to ensure flight safety and time efficiency of task execution. Meanwhile, the flight cost and energy consumption are comprehensively considered, and the inspection tasks are assigned to the UAV according to priority. Secondly, the internal environmental changes of the substation are monitored in real time, and the newly generated dangerous points and obstacles are detected in time. The flight path is optimized and adjusted by the transition point to ensure that the UAV can quickly and accurately reach the new dangerous point and complete the inspection task. Finally, the RTK and visual assisted positioning system are combined to ensure that the UAV can accurately locate and stably fly in the narrow space. The flight state of the UAV is dynamically corrected by the PID control algorithm to ensure that the flight path is accurate and stable, thereby improving the adaptability and inspection efficiency of the UAV in the complex substation environment, being able to respond to environmental changes in time and adjust the inspection path, minimizing the time and energy consumption in task execution, ensuring that the UAV can accurately reach the inspection point in a narrow space, and improving the safety and reliability of the inspection.

[0030] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0031] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0032] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0033] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0034] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0035] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A small UAV narrow-space safety inspection method, characterized by: The steps include: Analyze the substation equipment layout, divide the equipment nodes into safe points, dangerous points and transition points, and determine the drone inspection path through the shortest path algorithm. Assign inspection tasks to drones based on the importance of the equipment, the danger level of the inspection points and the timeliness of the tasks. Monitor substation environment changes in real time, detect new danger points and obstacles, and optimize and adjust the inspection path through transition points. The drone will inspect new danger points along the newly generated inspection path. The RTK and vision-assisted positioning systems are combined to determine the position of the UAV, and the UAV's path flight is monitored based on the PID control algorithm to dynamically correct the UAV's flight status.

2. A small UAV narrow space safety inspection method according to claim 1, characterized in that: Analyze the substation equipment layout, divide the equipment nodes into safe points, dangerous points and transition points, and determine the drone inspection path through the shortest path algorithm. The specific steps are as follows: The equipment nodes in the substation are divided into three types of inspection points, including safe points, dangerous points, and transition points; The safety point is the area where the drone can fly safely and adjust its position. The danger point is the point where the equipment node needs to be checked and the area where the drone arrives and performs the inspection task. The transition point is the node where the drone adjusts its flight status. It is located above the safety point. The drone adjusts its flight altitude, speed, and attitude at the transition point. The equipment node layout of the substation is converted into a spatial graph model. Each equipment or inspection point corresponds to a node of the spatial graph model, and the lines between the nodes represent the inspection path as edges. The shortest path is determined based on the equipment location of the substation, the actual distance of the inspection path, and the type of inspection task of the equipment. When calculating the path, the optimal path is determined based on the task execution cost of the dangerous point and the transition point and the shortest path of the inspection task. The formula is: ,in, is the flight distance between two consecutive inspection points from inspection point i to i+1 in the path, is the inspection cost of the jth dangerous point, which indicates the time, energy or resources required to perform the task at the inspection point, N indicates the number of inspection points in the inspection path, M indicates the number of dangerous points, is the cost weighting factor, which is used to balance the path length and task execution cost; The final optimal path is determined based on the total path length and task execution cost.

3. A small UAV narrow-space safety inspection method according to claim 2, characterized in that: Assign inspection tasks to drones based on the importance of the equipment, the danger level of the inspection point, and the timeliness of the task. The specific steps include: The importance of equipment is assessed as key equipment and auxiliary equipment. Key equipment includes the main transformer and switchgear of the substation, and auxiliary equipment includes sensors and cooling system equipment. Inspection points are divided into high-risk inspection points and low-risk inspection points according to their risk level. High-risk inspection points include high-voltage power areas and faulty equipment, while low-risk inspection points include regular inspection points or equipment areas without faults. Task timeliness requirements include emergency tasks and routine tasks. Emergency tasks include equipment failures, fire hazards, and tasks that need to be handled immediately. Routine tasks include regular inspections and non-emergency equipment inspections. In the inspection route planning, based on the evaluation results, the task is assigned to the most suitable UAV according to the urgency, danger and importance of the equipment at each inspection point, and constraints are set during the path planning process.

4. A small UAV narrow space safety inspection method according to claim 3, characterized in that: Monitor the substation environment changes in real time, detect new dangerous points and obstacles, and optimize and adjust the inspection path through transition points. The drone will inspect new dangerous points according to the newly generated inspection path. The specific steps are as follows: Through the analysis of sensor data and real-time feedback, newly generated dangerous points are identified and their locations are calibrated. The surrounding environment is also checked for obstacles to obtain their locations. Update the original inspection path according to the identified new dangerous point locations and obstacle positions, and calculate the safe transition points; Use the optimization algorithm to recalculate the path, and the calculation expression is: ,in, represents a safe transition point in the drone’s path, It is the flight distance between two consecutive inspection points from the current inspection point i to the target inspection point i+1. DTO represents the obstacle distance on the path. HA is the flight altitude that needs to be adjusted. Indicates the importance of obstacle distance, Indicates the importance of height adjustment; The position of transition points along the UAV flight path is optimized based on the safe transition points, and a shortest path to the dangerous point is planned.

5. A small UAV narrow space safety inspection method according to claim 4, characterized in that: And plan a shortest path to the danger point, including the following steps: All current safe points , as the next transition point, by calculating the distance between the current position of the drone and each safety point, determine the nearest safety point, and use the nearest safety point as the starting point, where, is the current safe point coordinate, , , Respectively represent the spatial coordinate position of the current safety point in the horizontal direction, vertical direction and height direction perpendicular to the horizontal plane; According to the current position of the drone and the position of the newly generated dangerous point, determine the distance between each safe point and the new dangerous point; By comparing the distances between all safe points and the newly generated dangerous points, the closest safe point is selected as the next target for the drone to fly; After selecting the nearest safe point, the flight status of the drone is optimized, and the inspection path is corrected according to the target safe point, transition point and current flight status; The flight status includes altitude, pitch angle, roll angle, yaw angle and flight speed.

6. A small UAV narrow space safety inspection method according to claim 5, characterized in that: The RTK and vision-assisted positioning systems are combined to determine the position of the drone, and the flight path of the drone is monitored according to the PID control algorithm to dynamically correct the flight status of the drone, including the following steps: The geographic location coordinates are obtained through the RTK system, the visual positioning data are obtained through the visual assisted positioning system, and the geographic location coordinates and the visual positioning data are combined to obtain the real-time position of the drone; Calculate the error between the current position of the drone and the target position; The control command is generated through the PID control algorithm, the position error is converted into control instructions, and the flight status of the UAV is adjusted.

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