A Method for Safe Inspection of a Small UAV in a Narrow Space

Abstract

The present invention discloses a method for safe inspection of a small unmanned aerial vehicle (UAV) in a narrow space, which relates to the technical field of space monitoring and is used to solve the problem of unclear inspection path generation of the UAV in a substation. According to the equipment layout of the substation, the inspection points are divided into safe points, dangerous points and transition points, and the inspection path is optimized by the shortest path algorithm to ensure the minimization of flight safety, task efficiency and energy consumption. The inspection tasks are assigned to the UAV according to the priority to ensure that urgent tasks are processed first. Secondly, the environmental changes are monitored in real time, new dangerous points and obstacles are identified in a timely manner, and the flight path is dynamically adjusted through the optimization of the transition points to ensure that the UAV can quickly and accurately reach the new dangerous points. Finally, combined with the RTK and visual-aided positioning systems, the high-precision positioning of the UAV in the narrow space is guaranteed, and the flight state is dynamically corrected through the PID control algorithm to ensure the accuracy and stability of the path.

Description

Technical Field

[0001] The present invention relates to the technical field of space monitoring. More specifically, the present invention relates to a method for safe inspection of a narrow space by a small unmanned aerial vehicle (UAV). Background Art

[0002] The UAV inspection technology is widely used in the monitoring and maintenance of various facilities, especially playing an important role in industries such as power, petroleum, and communication. By carrying high-precision sensors and image processing systems, UAVs can achieve remote monitoring of equipment, real-time data collection, and fault detection.

[0003] Deficiencies of the prior art: During the UAV inspection process, in the event of dynamic environmental changes and emergencies, most traditional path planning methods rely on static inspection route design, lacking the ability to generate new dangerous points in real time for emergencies (such as power accidents or equipment failures) and adjust the inspection path. Once new dangerous points appear, the existing systems often have difficulty adjusting the inspection path in a timely and accurate manner. Especially in the case of sudden inspection tasks that require rapid response, it is impossible to efficiently select appropriate points to optimize the flight altitude, speed, and attitude of the UAV, and lack 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] To overcome the above-mentioned defects of the prior art, the following solutions are provided to solve the problem of unclear generation of the inspection path of the UAV in the substation in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for safe inspection of a narrow space by a small UAV, including the following steps: Analyze the layout of substation equipment, divide the equipment nodes into safe points, dangerous points, and transition points, and determine the UAV inspection path through the shortest path algorithm. Assign the inspection tasks to the UAV according to the importance of the equipment, the danger level of the inspection points, and the timeliness of the tasks;

[0007] Real-time monitor the environmental changes in the substation, detect newly generated dangerous points and obstacles, and optimize and adjust the inspection path through the transition points. The UAV conducts inspections of the newly generated dangerous points according to the newly generated inspection path;

[0008] Combine RTK and visual-aided positioning systems to determine the position of the UAV, and monitor the path flight of the UAV according to the PID control algorithm to dynamically correct the flight state of the UAV.

[0009] In a preferred embodiment, analyze the layout of substation equipment, divide the equipment nodes into safe points, dangerous points, and transition points, and determine the UAV inspection path through the shortest path algorithm. The specific steps are as follows:

[0010] The equipment nodes in the substation are divided into three types of inspection points, including safety points, dangerous points, and transition points;

[0011] The safety point is the area where the UAV can fly safely and adjust its position. The dangerous point is the location where equipment nodes need to be inspected, and it is the area where the UAV arrives and executes the inspection task. The transition point is the node for adjusting the flight state of the UAV, located above the safety point. The UAV adjusts its flight altitude, speed, and attitude at the transition point;

[0012] The layout of the equipment nodes in the substation is transformed into a spatial graph model. Each equipment or inspection point corresponds to a node in the spatial graph model, and the connection lines between the nodes represent the inspection paths as edges;

[0013] Based on the equipment location in the substation, the actual distance of the inspection path, and the type of inspection task of the equipment, the shortest path is determined. When calculating the path, the optimal path is determined according to the task execution cost of the dangerous point and the transition point and the shortest path of the inspection task. The formula is: where, 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, representing the time, energy, or resources required when executing the task at the inspection point, is the cost weighting factor, used to balance the path length and the task execution cost;

[0014] According to the total path length and the task execution cost, the final optimal path is determined.

[0015] In a preferred embodiment, the inspection tasks are assigned to the UAV according to the importance of the equipment, the danger level of the inspection point, and the timeliness of the task. The specific steps include:

[0016] The equipment importance is evaluated as key equipment and auxiliary equipment. The key equipment includes the main transformer and switchgear of the substation, and the auxiliary equipment includes sensors and cooling system equipment;

[0017] The danger level of the inspection point is evaluated as high-danger inspection points and low-danger inspection points. The high-danger inspection points include high-voltage areas and faulty equipment, and the low-danger inspection points include regular inspection points or equipment areas without faults;

[0018] The requirements for task timeliness are urgent tasks and regular tasks. Urgent tasks include equipment failures, fire hazards, etc., which need to be dealt with immediately. Regular tasks include regular inspections and non-urgent equipment inspections;

[0019] In the inspection route planning, according to the evaluation results, tasks are assigned to the most suitable drones based on the urgency, danger level of each inspection point, and the importance of the equipment, and constraint conditions are set during the path planning process.

[0020] In a preferred embodiment, the environmental changes of the substation are monitored in real time, newly generated dangerous points and obstacles are detected, and the inspection path is optimized and adjusted through transition points. The drone conducts inspections of the newly generated dangerous points according to the newly generated inspection path. The specific steps are as follows:

[0021] Through sensor data and real-time feedback analysis, newly generated dangerous points are identified and their positions are calibrated, and obstacle detection is performed on the surrounding environment to obtain the positions of the obstacles;

[0022] Based on the positions of the newly identified dangerous points and the positions of the obstacles, the original inspection path is updated, and safe transition points are calculated;

[0023] The path is recalculated using an optimization algorithm, and the calculation formula is: , where represents the safe transition point in the drone path, 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 distance of the obstacle on the path, and HA is the flight altitude that needs to be adjusted, represents the importance of the obstacle distance, represents the importance of the altitude adjustment;

[0024] Based on the safe transition points, the positions of the transition points for the drone flight path are optimized, and the shortest path to reach the dangerous point is planned.

[0025] In a preferred embodiment, and planning the shortest path to reach the dangerous point includes the following steps:

[0026] All current safe points , are used as the next transition point. By calculating the distances between the current position of the drone and each safe point, the nearest safe point is determined, and the nearest safe point is used as the starting point, where is the coordinate of the current safe point, , , respectively represent the spatial coordinate positions of the current safe point in the horizontal direction, vertical direction, and height direction perpendicular to the horizontal plane;

[0027] Based on the current position of the drone and the positions of the newly generated dangerous points, the distances between each safe point and the newly generated dangerous points are determined;

[0028] By comparing the distances between all safe points and the newly generated dangerous points, the nearest safe point is selected as the next target for the drone flight.

[0029] Optimize the flight state of the UAV after selecting the nearest safe point, and correct the inspection path according to the target safe point, transition point, and current flight state;

[0030] The flight state includes altitude, pitch angle, roll angle, yaw angle, and flight speed.

[0031] In a preferred embodiment, the position of the UAV is determined by combining RTK and a vision-assisted positioning system, 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:

[0032] Obtain geographical location coordinates through the RTK system, obtain vision positioning data through the vision-assisted positioning system, and combine the geographical location coordinates and the vision positioning data to obtain the real-time position of the UAV;

[0033] Calculate the error between the current position and the target position of the UAV;

[0034] Generate control commands through the PID control algorithm, convert the position error into control instructions, and adjust the flight state of the UAV.

[0035] The technical effects and advantages of a method for safe inspection of a narrow space by a small UAV according to the present invention:

[0036] Through precise path planning, dynamic adjustment, and high-precision positioning, the present invention realizes the safe inspection of the narrow space of the substation. First, 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 the time efficiency of task execution. At the same time, considering the flight cost and energy consumption, the inspection tasks are assigned to the UAV according to the priority. Secondly, the internal environment changes of the substation are monitored in real time, and newly generated dangerous points and obstacles are detected in time. The flight path is optimized and adjusted through the transition points to ensure that the UAV can quickly and accurately reach the new dangerous points and complete the inspection tasks. Finally, by combining RTK and the vision-assisted positioning system, it is ensured that the UAV can be accurately positioned and stably fly in the narrow space. With the help of the PID control algorithm, the flight state of the UAV is dynamically corrected 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 points in the narrow space, and improving the safety and reliability of the inspection. Description of the Drawings

[0037] Figure 1 It is a schematic flow chart of a method for safe inspection of a narrow space by a small UAV according to the present invention. Detailed implementation manners

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] To achieve the above object, Figure 1 A structural schematic diagram of a method for safe inspection of a narrow space of a small unmanned aerial vehicle in the present invention is given, which specifically includes the following steps;

[0040] Analyze the layout of substation equipment, divide the equipment nodes into safety points, dangerous points and transition points, and determine the inspection path of the unmanned aerial vehicle through the shortest path algorithm. Assign the inspection tasks to the unmanned aerial vehicle according to the importance of the equipment, the danger level of the inspection points and the timeliness of the tasks;

[0041] Real-time monitor the environmental changes in the substation, detect newly generated dangerous points and obstacles, and optimize and adjust the inspection path through the transition points. The unmanned aerial vehicle performs inspections on the newly generated dangerous points according to the newly generated inspection path;

[0042] Combine RTK and visual-aided positioning systems to determine the position of the unmanned aerial vehicle, and monitor the path flight of the unmanned aerial vehicle according to the PID control algorithm to dynamically correct the flight state of the unmanned aerial vehicle.

[0043] Step 1, perform initial inspection route planning and task assignment. During the initial route planning process of the small unmanned aerial vehicle for substation narrow space inspection, first determine the goals of the inspection tasks and the spatial layout of the equipment in the substation. For each inspection path, based on the equipment layout, the nature of the inspection points (safety points, dangerous points, transition points) and the flight characteristics of the unmanned aerial vehicle, construct a mathematical model that comprehensively considers path optimization, flight safety and time efficiency. Through this model, realize reasonable task assignment and inspection path planning. The specific steps are as follows:

[0044] Perform task goal and spatial modeling. The unmanned aerial vehicle starts from the starting point (usually a reference point or a safe area in the substation), and the equipment nodes in the substation need to be divided into three types of inspection points, namely safety points, dangerous points and transition points. A safety point is an area where the unmanned aerial vehicle can fly safely and adjust its position (such as an area without obvious obstacles around the equipment and good air circulation); A dangerous point is a point where equipment inspection is required, usually the high-voltage area of the equipment or the location of the faulty equipment, and it is required that the unmanned aerial vehicle accurately reach and perform inspection tasks; A transition point is a node for adjusting the flight state of the unmanned aerial vehicle, usually located above the safety point, allowing the unmanned aerial vehicle to adjust its flight height, speed, attitude, etc. to quickly respond to emergencies or path changes;

[0045] The device node layout of the substation is transformed into a spatial graph model, where each device or inspection point corresponds to a node, and the connection lines between the nodes represent the feasible inspection paths. The transformation of the device layout is modeled through a spatial coordinate system. For example, a three-dimensional coordinate system is used to map the specific position and height information of the devices into the actual environment. Combining with the Geographic Information System (GIS) data, a three-dimensional model that can reflect the real environment is generated;

[0046] The preliminary path planning will use the shortest path algorithm in graph theory. Considering the spatial limitations and flight constraints of the substation, the algorithm needs to be selected based on the device positions in the substation, the actual distance of the inspection paths, and the types of inspection tasks of the devices. When calculating the path, the task execution costs of the dangerous points and transition points need to be taken into account to ensure the shortest path and the lowest energy consumption for each inspection task. And there must be a legal path connection between each inspection point and the next inspection point. The path selection can be modeled through the shortest path algorithm in graph theory, defining each inspection point as a node in the graph and the path as an edge;

[0047] The result of path planning is a series of sequential inspection points. The UAV needs to fly in sequence and reach each device inspection point to execute tasks. Given the substation layout and task requirements, find an optimal path in the spatial graph model to minimize the time, energy consumption, and risk during the UAV inspection process. The task target path can be represented by the following optimization problem, and the formula is: , where, 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 when executing tasks at this point. N represents the number of inspection points in the inspection path, and M represents the number of dangerous points, is the cost weighting factor used to balance the path length and task execution cost. By adjusting , the importance adjustment of different tasks can be realized, avoiding that simple path optimization leads to overemphasis on one aspect (such as too fast flight speed while ignoring the accuracy of device inspection). By weighing the total path length and task execution cost, the final path is determined to be optimal;

[0048] After the path planning is completed, the task assignment of the UAV inspection is carried out. In the planning of multiple inspection routes, it is necessary to decide the task assignment of the UAV in different routes according to the complexity and priority of the tasks. The task assignment will be based on the importance of the devices, the danger level of the inspection points, and the timeliness of the tasks. For some urgent equipment problems (such as faulty devices or dangerous points), they are preferentially assigned to the UAV and its relevant inspection tasks are scheduled;

[0049] The importance assessment of equipment classifies it into key equipment and auxiliary equipment. Key equipment includes, for example, the main transformers and switchgear in a substation. The failure of these devices may lead to large-scale power outages. Therefore, the inspection priority of these devices is relatively high, and drones should be allocated to them first. Auxiliary equipment such as sensors and cooling systems, although important, their failures will not directly cause the shutdown of the entire station, so their inspection priority is relatively low.

[0050] The risk assessment of inspection points classifies them into high-risk inspection points and low-risk inspection points. High-risk inspection points include, for example, high-voltage areas and faulty equipment. Special attention should be paid to safety during the inspection of these points when the drone is flying. Therefore, priority should be considered during task allocation, and the most suitable drone should be arranged to execute the task. For low-risk inspection points, such as regular inspection points or equipment areas without faults, the task priority is relatively low, and ordinary inspection tasks can be arranged.

[0051] The requirements for task timeliness classify tasks into urgent tasks and regular tasks. Urgent tasks such as equipment failures and fire hazards, which need to be dealt with immediately, will be given a higher priority and are usually allocated to the nearest drone and started quickly. Regular tasks such as regular inspections and non-urgent equipment inspections can be carried out during lower-priority time periods according to the drone's task arrangement.

[0052] 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 drone according to the urgency, danger, and importance of each inspection point. For example, for high-priority tasks, the scheduling system will first select the drone that is currently the closest and in good flight condition to ensure a quick response. If multiple drones can perform the same task, the task allocation system can further optimize the drone scheduling according to factors such as flight path, energy status, and task timeliness.

[0053] It should be noted that in addition to the optimization objectives, constraint conditions also need to be considered during the path planning process, such as the altitude limit of drone flight, safety constraints of flight status, position constraints of transition points, and requirements for avoiding obstacles. The specific constraints are as follows. The inspection path of the drone 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 through a collision detection algorithm (such as path planning based on the grid method).

[0054] Energy consumption and task constraints, that is, considering the energy limitation of the drone, the path planning also needs to ensure the shortest flight time to avoid power shortage during flight. This part of the constraint can be completed by calculating the flight time and required energy for each section of the path.

[0055] For example, for flight space constraints, when a drone is flying, it is not allowed to exceed a specified altitude range or fly into an inaccessible area (such as the high-voltage area inside a substation). This can be represented by the following constraints: , where is the flight altitude of the i-th inspection point, and are the minimum and maximum flight altitudes respectively;

[0056] The core objective of path planning and task allocation is to find the optimal path that can ensure flight safety and minimize task cost. In this example, 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 can be quickly calculated. The cost function of the A* algorithm is as follows: , where represents 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;

[0057] Through the iteration of the A* algorithm, the shortest path of the path is ensured, and the path selection is adjusted in real time to respond to new environmental data and task requirements;

[0058] Through the above steps, the initial path planning and task allocation of the substation narrow-distance space inspection task based on a small drone are realized. The key to path planning lies in comprehensively considering flight safety, task execution cost, and the shortest path. At the same time, a heuristic algorithm (A* algorithm) is used for calculation. By dynamically updating the path and task cost, sudden situations occurring in the substation environment can be dealt with, ensuring that the drone is always in a safe state and can efficiently complete the task during the inspection process.

[0059] After completing the initial inspection route planning and task allocation, it enters the dynamic path adjustment and transition point optimization stage. The goal of this stage is to be able to adjust the inspection path of the drone in real time according to new situations 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 task. This process involves dynamic path optimization, adjustment of transition points, and real-time flight state management. The following are the specific steps:

[0060] Step 2, perform dynamic path adjustment and transition point optimization. When the drone is performing the inspection task, it may encounter new dangerous points or equipment failures, resulting in the inapplicability of the original inspection route. Therefore, in this stage, first, it is necessary to scan the environment through a real-time monitoring system in combination with the sensors of the drone (such as visual cameras, lidar, etc.) to identify new dangerous points;

[0061] Specifically, the detection process of new dangerous points includes real-time status monitoring of power equipment in the substation. For example, faults or short circuits may occur in the switchgear in the substation, resulting in the generation of new dangerous points. At this time, through sensor data and real-time feedback analysis, the newly generated dangerous points are identified and their positions are calibrated. , where x, y, and z are the coordinates of the new dangerous point in three-dimensional space;

[0062] At the same time, in order to maintain flexible adjustment of the UAV inspection path, the sensor system also needs to detect obstacles in the surrounding environment ( ), and 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 position and size of the obstacles will affect the inspection path planning, so the position of the obstacles needs to be obtained through real-time sensor data and the influence range information.

[0063] Once a new dangerous point is identified , as well as the possible positions of obstacles , update the original inspection path and calculate appropriate transition points. The transition points are key nodes during the UAV flight. Adjust the UAV inspection path and reselect appropriate transition points. The transition points are points in the path where the flight height, speed, and attitude can be adjusted, helping the UAV to safely transition from one inspection point to another. Especially when the environment changes suddenly, the recalculation of the transition points is completed according to the new dangerous points, obstacles, and environmental changes;

[0064] The purpose of path update is to optimize the inspection path from the current inspection point to the new dangerous point . When calculating the updated inspection path, considering the requirements of new obstacles and flight safety, use optimization algorithms (such as heuristic A* algorithm or Dijkstra algorithm) to recalculate the path to ensure that the UAV 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.

[0065] For example, the possible obstacles and changes in equipment layout on the path will increase the flight difficulty, so it needs to be adjusted through the following formula: , where represents the safe transition point in the UAV path, 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 distance of the obstacle on the path, HA is the flight height that needs to be adjusted, represents the importance of the obstacle distance, Indicate the importance of altitude adjustment and adjust the weight and The selection will balance path selection and flight altitude optimization according to the complexity of the environment and the priority of the task;

[0066] By recalculating and optimizing the positions of the transition points of the UAV flight path, it can be ensured that the UAV can bypass obstacles and successfully complete the flight process from the current point to the new dangerous point.

[0067] Furthermore, after the new dangerous point is identified, a shortest path to reach this dangerous point can be planned to ensure that the UAV can reach the dangerous point for inspection at the fastest speed. Take all current safe points ( ) as possible transition points. By calculating the distances between the current position of the UAV and each safe point, the nearest safe point is determined and used as the starting point to quickly move towards the dangerous point. Among them, is the coordinate of the current safe point, , , respectively represent the spatial coordinate positions of the current safe 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 same applies to the subsequent similar position representation forms;

[0068] Assume that the current position of the UAV is , and the newly generated dangerous point is . Each safe point has a distance from the new dangerous point: ;

[0069] By comparing the distances between all safe points and the newly generated dangerous point, the nearest safe point is selected as the next target of the UAV: ;

[0070] After selecting the optimal safe point, the UAV will adjust the inspection path (based on the setting of transition points) to quickly reach this safe point and prepare for the next path planning.

[0071] After selecting the optimal safe point, the flight control system needs to optimize the flight state of the UAV through dynamic adjustment of the current path, and make corresponding path corrections according to the target safe point, transition points, and the current flight state (such as speed, attitude);

[0072] Based on the selected nearest safe point, the flight control system needs to perform path planning from the current position of the UAV to the target safe point, and set the important positions (such as turning points, altitude change points, etc.) in this path as transition points ;

[0073] The inspection path adjustment formula can perform dynamic programming based on path optimization algorithms (such as the A* algorithm and Dijkstra algorithm), consider obstacles and environmental factors, and calculate the shortest and safest path. In this path, the selection of each transition point is calculated according to the current flight state, target path, and flight capabilities to ensure that parameters such as flight altitude, speed, and attitude after path adjustment meet the mission requirements;

[0074] When the UAV flies along the planned path, the flight control system needs to perform real-time status feedback and adjustment on each transition point. When the UAV flies to a certain transition point, the flight control system will adjust the flight state (such as altitude, pitch angle, roll angle, yaw angle) and flight speed according to real-time sensor data (such as air flow, obstacles, flight altitude changes, etc.) to ensure a smooth transition to the next flight state.

[0075] During the path adjustment process, the optimization of transition points not only includes calculating the optimal position of transition points but also includes the adjustment of flight states. The optimization of transition points and flight state management mainly includes the following aspects:

[0076] Altitude adjustment: In a complex substation environment, factors such as obstacles, equipment, and electromagnetic interference may require the UAV 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, and thus an appropriate transition point is selected for altitude adjustment. Altitude adjustment can be calculated by minimizing the flight energy consumption: , where 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;

[0077] Flight speed adjustment: There may be requirements for flight speed in the path, especially when dealing with complex terrains and sudden obstacles. The flight speed of the UAV needs to be adjusted according to the complexity of the path, the number of obstacles, and the priority of dangerous points. The selection of transition points also needs to be adjusted according to the flight speed of the UAV. If the new path bypasses obstacles or requires a large range of altitude adjustments, then 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: , where 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;

[0078] Flight state adjustment: The adjustment of the flight state is a key aspect of another transition point optimization. By optimizing the flight angle and direction, it is ensured that the UAV can make a smooth transition and avoid sudden acceleration or out-of-control situations. The optimization of the flight state can be achieved by adjusting the pitch angle and roll angle of the UAV. The optimization of the flight state can be calculated using the following formula: , where 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.

[0079] After completing the path adjustment and transition point optimization, the updated path is verified. The purpose of the verification is to ensure that all flight states (such as altitude, speed, attitude, etc.) still meet the safety requirements in the new path, and the new path can complete the inspection task quickly and effectively, as follows:

[0080] Flight safety verification, that is, through a virtual simulation environment or a flight simulation system, the new inspection path is simulated to detect whether the path will collide or violate flight restrictions. For example, ensure that the UAV does not enter dangerous high-voltage areas or under equipment. During the safety verification process, factors such as wind speed and air resistance during flight should also be considered;

[0081] Task execution verification, that is, after the path verification passes, 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 UAV can accurately reach the target dangerous point and complete the required inspection tasks (such as taking pictures, data collection, etc.);

[0082] After the verification passes, the path adjustment result will be transmitted 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 UAV, ensuring the continuity and accuracy of task execution;

[0083] In the dynamic path adjustment and transition point optimization stage, through real-time environmental monitoring and dangerous point detection, the inspection path is recalculated and the selection of transition points is optimized, so as to ensure that the UAV can quickly and safely adjust the path in case of emergencies. The optimization of flight states (including altitude, speed, attitude, etc.) helps to improve the inspection efficiency and flight stability.

[0084] Step 3, perform high-precision positioning and flight control. When the UAV performs the inspection task in the narrow-distance space of the substation, ensure that the UAV can accurately reach the inspection point. The coordinated work of high-precision positioning technology and the flight control system can ensure that the inspection path of the UAV is accurate and error-free, and the task can be stably executed during the inspection process. Use high-precision positioning technology (such as RTK, vision-assisted positioning, etc.) for precise navigation and ensure the precise control of the UAV flight state. The specific steps are as follows:

[0085] The RTK positioning technology is the main positioning technology used by drones during flight. The RTK system helps the flight control system ensure that the drone flies along the predetermined path by providing more accurate geographical location coordinates ( );

[0086] The vision-assisted positioning system can be combined with the RTK system to further improve the reliability of positioning. Especially in an environment where GPS signals are weak or blocked, vision positioning obtains environmental information through methods such as image recognition and feature point matching, thereby providing auxiliary positioning to ensure the stable flight of the drone in a narrow space. The vision positioning data is ;

[0087] Taking into account the RTK and vision positioning data comprehensively and fusing them to obtain the final real-time position of the drone ;

[0088] The accurate position data output by the high-precision positioning system will be transmitted 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 and the target position (inspection point or transition point) to perform path tracking control to ensure that the drone accurately executes the path.

[0089] The core task of the flight control system is to dynamically correct the drone's state through a high-precision PID control algorithm to ensure that the drone tracks the predetermined inspection path. The specific control steps are as follows:

[0090] Position error calculation, that is, calculating the error between the current position of the drone and the target position (the expected position): , where is the target position (such as an inspection point or a 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;

[0091] Control command generation, that is, through the PID controller, converting the position error into control commands (such as speed, acceleration, attitude angle, etc.) to enable the drone to adjust its flight state to eliminate the deviation. The control commands of the PID controller are: , where are the proportional, integral, and differential gain coefficients respectively. Appropriate adjustment of these coefficients can achieve stable and accurate path tracking.

[0092] The flight control system performs state estimation based on data from the RTK positioning system and the auxiliary positioning system (such as the current speed, acceleration, attitude angle, etc. of the UAV), 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 according to 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 UAV in real time, and monitors the flight data, including battery power, flight time, flight stability, etc., to ensure that the UAV can complete the task during flight without failure or loss of connection. The flight monitoring system will continuously monitor the flight state of the UAV and make adjustments or terminate the flight when necessary;

[0093] The high-precision positioning and flight control steps are designed to provide accurate flight positions through the RTK system and auxiliary positioning technologies, and ensure that the UAV can safely and accurately execute tasks according to the predetermined path through dynamic flight control (such as PID control algorithm, flight state adjustment, etc.) and obstacle avoidance system. Through real-time status monitoring and feedback correction, the flight control system can make timely adjustments according to the flight situation of the UAV.

[0094] It should be noted that the threshold information related in this embodiment is pre-set by professionals and will not be explained in detail here. There are some cases where the English letters of some parameters are the same in this embodiment, but different meanings are explained when used, and they will not be explained one by one here.

[0095] The present invention realizes the safe inspection of the narrow space in the substation through precise path planning, dynamic adjustment and high-precision positioning. First, according to the layout of substation equipment, the inspection points are divided into safe points, dangerous points and transition points, and the inspection path of the UAV is optimized by the shortest path algorithm to ensure flight safety and the time benefit of task execution. At the same time, considering the flight cost and energy consumption comprehensively, the inspection tasks are assigned to the UAV according to the priority. Secondly, the internal environment change of the substation is monitored in real time, new generated dangerous points and obstacles are detected in time, and the flight path is optimized and adjusted through the transition points to ensure that the UAV can quickly and accurately reach the new dangerous points and complete the inspection tasks. Finally, combined with the RTK and vision-aided positioning systems, it is ensured that the UAV can be accurately positioned and stably fly in the narrow space. With the help of the PID control algorithm, the flight state of the UAV is dynamically corrected 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 to the greatest extent, ensuring that the UAV can accurately reach the inspection points in the narrow space, and enhancing the safety and reliability of the inspection.

[0096] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0097] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0098] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0099] In addition, in each embodiment of this application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0100] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0101] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall 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. Combine RTK and vision-assisted positioning systems to determine the position of the drone, monitor the flight path of the drone based on the PID control algorithm, and dynamically correct the flight status of the drone; 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.

2. A small UAV narrow space safety inspection method according to claim 1, 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.

3. A small UAV narrow-space safety inspection method according to claim 2, 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.

4. A small UAV narrow space safety inspection method according to claim 3, 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 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; 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.

5. A small UAV narrow space safety inspection method according to claim 4, 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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