Unmanned aerial vehicle inspection method considering transformer substation space point location optimization and safety risk map

Abstract

The invention discloses an unmanned aerial vehicle inspection method considering transformer substation space point location optimization and a safety risk map, and aims to solve the problems of transformer substation unmanned aerial vehicle inspection task load, equipment importance difference and weak signals. Calculating a priority value by integrating equipment importance, historical faults and signal intensity, and ensuring that key equipment is polled preferentially; designing a short-time multi-inspection scheme to deal with electric quantity limitation; meanwhile, a low-altitude safety risk map combining environment shielding and an equipment barrier layer is generated, risks are quantified, routing inspection path planning is optimized, actual experiment verification shows that the method and system effectively improve the safety and efficiency of substation routing inspection, powerful support is provided for application of an unmanned aerial vehicle in a complex substation environment, and the method and system are suitable for popularization and application. The method has an important practical application value and a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of substations, and in particular relates to an unmanned aerial vehicle inspection method that takes into account substation spatial point optimization and safety risk maps. Background Art

[0002] Substation drone inspection tasks are large, and the importance of different equipment inspections varies. At the same time, there are risks such as drone loss of control due to weak signals. In view of these situations, it is urgent to propose a drone inspection method that can fully consider substation inspection tasks and inspection risks.

[0003] There have been many relevant research results in the field of drone safety management. These studies mainly focus on several key areas such as geographic fences, risk prediction, and safety assessment. For example, some studies have proposed setting up geographic fences at low altitudes in cities and towns to ensure the safety of related flights; others have conducted cluster analysis on the actual flight trajectory data in the terminal area, and then used data-driven methods to set up geographic fences based on the analysis results, thereby reducing the collision risk of small drones in the terminal area; in addition, there are also studies on drone trajectory segmentation technology, which assesses the risk level by predicting its flight trajectory; and when planning drone routes in complex low-altitude environments, it is necessary to comprehensively consider regional risk assessment and obtain routes with lower risks at a lower path cost; there is also a drone flight conflict network model based on a complex network, which dynamically identifies conflicting edges and adopts a priority strategy to achieve the optimal resolution of drone flight conflicts in different scenarios.

[0004] However, the above-mentioned research methods in drone safety management mainly rely on subjective experience to manually set the safety buffer distance; but so far, there has not been a most optimized safety buffer distance calibration method proposed, nor a corresponding performance evaluation index, which to a certain extent limits the improvement of the safety and effectiveness of drone inspections in substation applications.

[0005] Therefore, it is necessary to design a UAV inspection method that considers the spatial point optimization and safety risk map of substations to solve the above problems. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a drone inspection method that takes into account the spatial point optimization and safety risk map of the substation. It aims to solve the problems of insufficient optimization of the safety buffer distance for the current substation drone inspection, lack of a corresponding performance indicator evaluation system, and excessive reliance on the pilot's experience, resulting in high error rate and training costs. It can further improve the application effect and safety of drone inspections in substations.

[0007] In order to achieve the above technical effects, the technical solution adopted by the present invention is: A drone inspection method considering substation spatial point optimization and safety risk map includes the following steps: S1, uses drones equipped with laser radar for navigation positioning and obstacle detection, digitizes the actual map through the grid map generation method and calibrates the inspection point coordinates, and synchronizes them to the background in real time; so that the staff can grasp the location of the drone and the inspection equipment, formulate differentiated inspection plans according to the importance of the equipment, and update the parameters and grid map data; S2, adopts a plan of multiple short-time inspections, splits the tasks according to the distance between the inspection points, and automatically updates the inspection order according to the equipment priority value; the equipment priority value is divided based on the importance of the equipment, historical faults, inspection frequency and signal strength; S3, using the security risk map grid division and calculation generation method, by integrating the environmental shielding and equipment obstacle risk layers, comprehensively evaluates and generates the final substation inspection probability risk map to quantify the potential risks of drones to ground equipment; S4, conducts drone inspections based on the generated inspection points, inspection plans, and risk maps, including constructing a three-dimensional coordinate system, designing multiple inspection plans, calculating inspection priority values, selecting inspection points, generating two-dimensional grid maps and risk layers, comprehensively evaluating risk values, and forming a grid map with superimposed optimized points and safety risk values. Finally, the optimal inspection path is generated through a jump point search algorithm and the inspection task is executed.

[0008] Preferably, the specific process of step S1 includes: Use drones equipped with laser radar equipment to conduct navigation positioning and obstacle detection in the station inspection environment; After collecting data, the grid map generation method is used to digitize the actual map to construct a spatial grid map of the outdoor substation; In the spatial grid diagram, the positional relationship between the spatial grid and the inspection points is clarified, a three-dimensional coordinate system is established to determine the coordinates and distances of the inspection points, and these are synchronized to the backend monitoring room in real time; When the relevant information of the inspection equipment, including location and status, changes, the grid map and data are updated in real time.

[0009] Furthermore, since the inspection time of the entire station is long and the battery power of the drone is limited, a plan for multiple inspections in a short period of time is designed.

[0010] Preferably, the specific process of step S2 includes: S201, the inspection task is disassembled according to the distance of the inspection point. After completing an inspection task, the drone automatically returns to the machine nest to charge, and inspects again according to the decomposed task after being fully charged; after receiving the inspection signal, the inspection priority value is calculated and updated to the inspection equipment point of the grid map, and whether the equipment needs to be inspected is determined according to the priority value; S202, the substation files the inspection equipment and records each inspection, builds a detailed equipment historical fault database, counts the number of failures of each device in different seven-day periods within a month, and divides the level according to the corresponding relationship between the number of failures and the equipment historical fault index, and calculates the equipment historical fault index of each device Q ; S203, calculate the inspection frequency: ; In the formula n The number of times the device is inspected in a week, and the statistical data is mapped to the device inspection frequency index F To reduce statistical complexity; S204, divide the importance of equipment in outdoor substation into 6 levels, using the importance index of the equipment itself W express; S205, set signal value T Determine the drone inspection mission, when the signal reaches the threshold standard T The value is 1, when the signal does not reach the threshold standard T The value is 0; S206, set inspection priority value: Priority value U The importance index of the device itself W , Equipment historical failure index Q , Equipment inspection frequency index F and signal value T The weighted product U : ; in, , , They are , , The weight coefficient of the index is set according to the inspection task requirements; U A value of 0 indicates that the device will not be inspected by drones; S207, update the inspection priority value to the inspection point of the spatial grid map, display the urgency through the color and size of the dots, and select the inspection point according to the priority value; consider the battery life of the UAV, set the maximum time for each inspection, accumulate the inspection time of the selected inspection point, and cancel the point selection when the inspection time of the next point exceeds the maximum value, leaving a margin for the UAV's flight time when selecting the point.

[0011] Furthermore, considering that the ultimate risk caused by a drone collision incident in a substation is a collision with equipment or the ground, there is also a risk of falling to the ground or colliding with equipment after an aerial accident. In order to achieve the safe operation of drones in the low-altitude and complex airspace of substations, the safety target of drone inspections cannot be lower than the safety level of urban flights. The safety risk map is a two-dimensional map based on coordinate position, which is used to quantify the risk of each divided unit to ground equipment.

[0012] Preferably, the specific process of step S3 includes: S301, projecting the spatial grid map onto the ground, gridding the entire substation low-altitude inspection airspace, and obtaining a two-dimensional grid map of the entire two-dimensional inspection ground layer environment, divided into The matrix : ; In the formula, each matrix element corresponds to the risk value of the cell location , discrete coordinates Geographical location The conversion relationship between them is: ; In the formula, is the geographic length represented by the unit coordinate, , ; S302, considering the complex low-altitude flight environment of the substation, its safety risk map is generated by combining the environmental shielding layer and the equipment obstacle layer. Through risk assessment, the shielding layer and the obstacle layer are quantitatively superimposed on the location-based map to generate the final safety risk map: The environmental shielding layer calculates the shielding protection effect of buildings in the substation on the equipment. The shielding coefficient is defined as a real number [0,10], where 0 means no obstacle shielding, 3 means sparse trees, 6 means the ground floor building close to the equipment, and 10 means the tallest building in the station; the environmental shielding layer is expressed by the matrix Indicates that each element Indicates the shielding coefficient of the corresponding position, through the geographical location With discrete coordinates The conversion relationship between them (5) is used to calculate the risk value of the cell corresponding to each matrix element. : ; The substation laser radar scanning data is used to extract the 3D model of the inspection area in the station and generate the equipment obstacle layer. The height of obstacles in the substation inspection environment is defined using the matrix Indicates that each element Indicates the maximum height of buildings in the relevant location; by geographic location With discrete coordinates The risk value of each matrix element corresponding to the cell position is calculated by the conversion relationship between ; S303, by integrating the two layers in the environmental shielding and equipment obstacle risk maps, based on the comprehensive assessment model of different risk values, a final substation inspection probability risk map is generated; the risk map element value is defined as: ; In the formula, , The weight coefficient of the potential risk of the geographical location of environmental shielding and equipment obstacles; The elements in the matrix representing the ambient occlusion layer, Represents the elements in the obstacle height matrix in the substation inspection environment; if the drone is in a no-fly zone or the flight altitude is lower than the geographical location The obstacle height at the location where the risk map element value is -0.001; otherwise, it is the sum of the risk probabilities of environmental shielding and equipment obstacles; thus quantifying the geographical location of the drone flying over The generated risk map quantifies the risks that drones pose to ground equipment.

[0013] Preferably, the specific process of step S4 includes: S401, constructing a three-dimensional coordinate system of the substation inspection space, including data of all inspection points, buildings and obstacles, forming a visual spatial grid map corresponding to the inspection point coordinates, and the distance between the inspection points can be converted according to the coordinates; S402, design a plan for multiple inspections in a short period of time, and divide the inspection tasks into tasks according to the distances between the inspection points; S403, select the importance index of the device itself W , Equipment historical failure index Q , Equipment inspection frequency index F and signal value T Calculate inspection priority value U ; S404, considering the limited battery life of the drone, selecting equipment inspection points in order of inspection priority values; S405, when step S401 is completed, the spatial grid map is projected onto the ground and gridded to obtain a two-dimensional grid map; S406, generating an environment shielding layer and a device obstacle layer respectively, and calculating the risk values ​​of the shielding layer and the obstacle layer; S407, generating a final substation inspection probability risk map by integrating the different risk values ​​of the two layers for comprehensive evaluation, and calculating the layer grid risk value; S408, after completing step S404 and step S407, a substation inspection grid map with superimposed optimization points and safety risk values ​​is formed; S409, generating an optimal path for the drone inspection based on a jump point search algorithm, and obtaining coordinate points and a traversal order; S410, generating an execution plan for the UAV inspection task and executing the inspection work.

[0014] The beneficial effects of the present invention are as follows: The present invention realizes dynamic optimization of inspection points by constructing a spatial grid map of the substation and calculating inspection priority values, thereby improving the efficiency and pertinence of the inspection work. And by generating a low-altitude safety risk map and planning the drone inspection path in combination with the risk map, the collision risk during drone inspections is effectively reduced, and the safety and reliability of inspection tasks are improved. Compared with the prior art, this method can give priority to areas and equipment with higher safety risks, reducing safety risks during the inspection process. Intelligent inspections of drones are achieved through algorithms and data analysis technology, thereby reducing dependence on manual labor. The drone inspection method and system provided by the present invention provide new ideas and technical support for the application of drones in complex substation environments, and have broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of the steps of the drone inspection method of the present invention; Figure 2 It is a schematic diagram of a space grid diagram of the present invention; Figure 3 This is a schematic diagram of a substation inspection risk map experiment in an embodiment of the present invention; Figure 4 Schematic diagram of a substation inspection probability risk map in an embodiment of the present invention; Figure 5 Schematic diagram of the inspection path in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] Embodiment 1: like Figure 1 As shown, a drone inspection method considering substation spatial point optimization and safety risk map includes the following steps: S1, uses drones equipped with laser radar for navigation positioning and obstacle detection, digitizes the actual map through the grid map generation method and calibrates the inspection point coordinates, and synchronizes them to the background in real time; so that the staff can grasp the location of the drone and the inspection equipment, formulate differentiated inspection plans according to the importance of the equipment, and update the parameters and grid map data; S2, adopts a plan of multiple short-time inspections, splits the tasks according to the distance between the inspection points, and automatically updates the inspection order according to the equipment priority value; the equipment priority value is divided based on the importance of the equipment, historical faults, inspection frequency and signal strength; S3, using the security risk map grid division and calculation generation method, by integrating the environmental shielding and equipment obstacle risk layers, comprehensively evaluates and generates the final substation inspection probability risk map to quantify the potential risks of drones to ground equipment; S4, conducts drone inspections based on the generated inspection points, inspection plans, and risk maps, including constructing a three-dimensional coordinate system, designing multiple inspection plans, calculating inspection priority values, selecting inspection points, generating two-dimensional grid maps and risk layers, comprehensively evaluating risk values, and forming a grid map with superimposed optimized points and safety risk values. Finally, the optimal inspection path is generated through a jump point search algorithm and the inspection task is executed.

[0017] Preferably, the specific process of step S1 includes: Use drones equipped with laser radar equipment to conduct navigation positioning and obstacle detection in the station inspection environment; After collecting data, the grid map generation method is used to digitize the actual map to construct a spatial grid map of the outdoor substation; In the spatial grid diagram, the positional relationship between the spatial grid and the inspection points is clarified, a three-dimensional coordinate system is established to determine the coordinates and distances of the inspection points, and these are synchronized to the backend monitoring room in real time; When the relevant information of the inspection equipment, including location and status, changes, the grid map and data are updated in real time.

[0018] Furthermore, since the inspection time of the entire station is long and the battery power of the drone is limited, a plan for multiple inspections in a short period of time is designed.

[0019] Preferably, the specific process of step S2 includes: S201, the inspection task is disassembled according to the distance of the inspection point. After completing an inspection task, the drone automatically returns to the machine nest to charge, and inspects again according to the decomposed task after being fully charged; after receiving the inspection signal, the inspection priority value is calculated and updated to the inspection equipment point of the grid map, and whether the equipment needs to be inspected is determined according to the priority value; S202, the substation files the inspection equipment and records each inspection, builds a detailed equipment historical fault database, counts the number of failures of each device in different seven-day periods within a month, and divides the level according to the corresponding relationship between the number of failures and the equipment historical fault index, and calculates the equipment historical fault index of each device Q ; S203, calculate the inspection frequency: ; In the formulan The number of times the device is inspected in a week, and the statistical data is mapped to the device inspection frequency index F To reduce statistical complexity; S204, divide the importance of equipment in outdoor substation into 6 levels, using the importance index of the equipment itself W express; S205, set signal value T Determine the drone inspection mission, when the signal reaches the threshold standard T The value is 1, when the signal does not reach the threshold standard T The value is 0; S206, set inspection priority value: Priority value U The importance index of the device itself W , Equipment historical failure index Q , Equipment inspection frequency index F and signal value T The weighted product U : ; in, , , They are , , The weight coefficient of the index is set according to the inspection task requirements; U A value of 0 indicates that the device will not be inspected by drones; S207, update the inspection priority value to the inspection point of the spatial grid map, display the urgency through the color and size of the dots, and select the inspection point according to the priority value; consider the battery life of the UAV, set the maximum time for each inspection, accumulate the inspection time of the selected inspection point, and cancel the point selection when the inspection time of the next point exceeds the maximum value, leaving a margin for the UAV's flight time when selecting the point.

[0020] Furthermore, considering that the ultimate risk caused by a drone collision incident in a substation is a collision with equipment or the ground, there is also a risk of falling to the ground or colliding with equipment after an aerial accident. In order to achieve the safe operation of drones in the low-altitude and complex airspace of substations, the safety target of drone inspections cannot be lower than the safety level of urban flights. The safety risk map is a two-dimensional map based on coordinate position, which is used to quantify the risk of each divided unit to ground equipment.

[0021] Preferably, the specific process of step S3 includes: S301, projecting the spatial grid map onto the ground, gridding the entire substation low-altitude inspection airspace, and obtaining a two-dimensional grid map of the entire two-dimensional inspection ground layer environment, divided into The matrix : ; In the formula, each matrix element corresponds to the risk value of the cell location , discrete coordinates Geographical location The conversion relationship between them is: ; In the formula, is the geographic length represented by the unit coordinate, , ; S302, considering the complex low-altitude flight environment of the substation, its safety risk map is generated by combining the environmental shielding layer and the equipment obstacle layer. Through risk assessment, the shielding layer and the obstacle layer are quantitatively superimposed on the location-based map to generate the final safety risk map: The environmental shielding layer calculates the shielding protection effect of buildings in the substation on the equipment. The shielding coefficient is defined as a real number [0,10], where 0 means no obstacle shielding, 3 means sparse trees, 6 means the ground floor building close to the equipment, and 10 means the tallest building in the station; the environmental shielding layer is expressed by the matrix Indicates that each element Indicates the shielding coefficient of the corresponding position, through the geographical location With discrete coordinates The conversion relationship between them (5) is used to calculate the risk value of the cell corresponding to each matrix element. : ; The substation laser radar scanning data is used to extract the 3D model of the inspection area in the station and generate the equipment obstacle layer. The height of obstacles in the substation inspection environment is defined using the matrix Indicates that each element Indicates the maximum height of buildings in the relevant location; by geographic location With discrete coordinates The risk value of each matrix element corresponding to the cell position is calculated by the conversion relationship between ; S303, by integrating the two layers in the environmental shielding and equipment obstacle risk maps, based on the comprehensive assessment model of different risk values, a final substation inspection probability risk map is generated; the risk map element value is defined as: ; In the formula, , The weight coefficient of the potential risk of the geographical location of environmental shielding and equipment obstacles; The elements in the matrix representing the ambient occlusion layer, Represents the elements in the obstacle height matrix in the substation inspection environment; if the drone is in a no-fly zone or the flight altitude is lower than the geographical location The obstacle height at the location where the risk map element value is -0.001; otherwise, it is the sum of the risk probabilities of environmental shielding and equipment obstacles; thus quantifying the geographical location of the drone flying over The generated risk map quantifies the risks that drones pose to ground equipment.

[0022] Preferably, the specific process of step S4 includes: S401, constructing a three-dimensional coordinate system of the substation inspection space, including data of all inspection points, buildings and obstacles, forming a visual spatial grid map corresponding to the inspection point coordinates, and the distance between the inspection points can be converted according to the coordinates; S402, design a plan for multiple inspections in a short period of time, and divide the inspection tasks into tasks according to the distances between the inspection points; S403, select the importance index of the device itself W , Equipment historical failure index Q , Equipment inspection frequency index F and signal value T Calculate inspection priority value U ; S404, considering the limited battery life of the drone, selecting equipment inspection points in order of inspection priority values; S405, when step S401 is completed, the spatial grid map is projected onto the ground and gridded to obtain a two-dimensional grid map; S406, generating an environment shielding layer and a device obstacle layer respectively, and calculating the risk values ​​of the shielding layer and the obstacle layer; S407, generating a final substation inspection probability risk map by integrating the different risk values ​​of the two layers for comprehensive evaluation, and calculating the layer grid risk value; S408, after completing step S404 and step S407, a substation inspection grid map with superimposed optimization points and safety risk values ​​is formed; S409, generating an optimal path for the drone inspection based on a jump point search algorithm, and obtaining coordinate points and a traversal order; S410, generating an execution plan for the UAV inspection task and executing the inspection work.

[0023] Embodiment 2: This embodiment provides a drone inspection method that takes into account the optimization of substation spatial points and the safety risk map, wherein the specific process of decomposing the inspection task is as follows: Since a standard full-station inspection process takes a long time, and the power limit of the drone makes it difficult to complete the inspection task in one go, it often leads to the inability to conduct immediate inspections of key equipment, thereby increasing possible safety risks. In the drone inspection of outdoor substations, the time required for the drone to fly between two inspection points is much less than the time required to inspect each device. Therefore, in the actual inspection process, a plan of multiple inspections in a short period of time is proposed. The inspection task is broken down according to the distance between the inspection points. When the drone traverses all the inspection points in the task and completes the specified inspection path, it automatically returns to the machine nest to charge. After being fully charged, a signal is sent out and the automatic output of the inspection signal is triggered, and the next inspection is carried out according to the decomposed task.

[0024] When the inspection signal is received, the inspection priority value of the equipment points to be inspected in the station is calculated according to the requirements of the triggered inspection task, and automatically updated to the inspection equipment points on the grid map. The inspection priority value is used to determine whether the equipment currently needs to be inspected by a drone. That is, the higher the inspection priority value of the equipment, the higher its inspection ranking. The inspection priority value is related to the importance index of the equipment itself, the equipment historical failure index, and the equipment inspection frequency index. The drone substation inspection should also consider the signal strength conditions in real time to ensure the safety of the inspection work.

[0025] The equipment included in the inspection in the substation is recorded in the archives according to each inspection. The equipment historical fault database built in the station records the operating status and inspection records of each equipment in detail, covering the exact time when the fault occurred and the specific details of the fault; data statistics show that the equipment historical fault index is strongly correlated with the number of failures of different equipment in a month; a month is divided into 4 time periods (No. 1 to No. 7, No. 8 to No. 14, No. 15 to No. 21, No. 22 to No. 30 / 31) to count the number of failures of each equipment in different time periods and analyze the historical fault records in these time periods. According to the number of failures and equipment historical failure index shown in Table 1 Q Corresponding relationship, the number of equipment failures in a month is divided into levels, and then the equipment historical failure index corresponding to each level is calculated, as shown in Table 1 below: Table 1: Correspondence between the number of failures and the equipment historical failure index;

[0026] Generally, the situation of drone inspections of equipment is obtained by manually sorting and counting the data of drone inspection tasks. Manual recording is prone to errors and is too cumbersome. In order to simplify the workload of multiple inspection statistics, the number of times the equipment is inspected within a period of time is corresponded to the equipment inspection frequency index.

[0027] The importance of equipment in outdoor substations varies, and their impact on inspection tasks is also different; the number of inspections and the weight of priority inspections for equipment of different importance are also different, so it is necessary to classify the importance of equipment in outdoor substations; use the equipment importance index W It is generally more appropriate to divide it into 6 levels, and each level corresponds to the importance index of the equipment itself. The following Table 2 shows the equipment importance classification and index.

[0028] Table 2: Equipment importance classification and index;

[0029] In actual engineering applications, outdoor substations are widely distributed and located in suburban areas or areas far from cities, where there are often fewer communication base stations. During the inspection flight, drones rely on GPS signals for positioning. When the signal is weak, it is very likely that the drone will lose connection or control, leading to a crash, thus threatening the safety of the substation. Therefore, it is necessary to set the signal value T To determine whether to perform the drone inspection mission. T The value parameter is set to 1; otherwise, when the signal is weak (cannot meet the flight requirements), the value T The parameter is set to 0.

[0030] Furthermore, in this embodiment, a drone equipped with a laser radar is used for navigation positioning and obstacle detection, and the actual map is digitized and the inspection point coordinates are calibrated by a grid map generation method. The specific process is as follows: The laser radar equipment equipped with drones is used to carry out navigation positioning and obstacle detection in the inspection environment of the station. The collected data is analyzed and processed, and the actual map is digitized into a series of discrete grids using the grid map generation method, and the coordinates of all points covered in the inspection task are calibrated in space. like Figure 2 As shown in the figure, A is a spatial grid, and the black dot B is the inspection point projected onto the spatial grid by any inspection equipment; a three-dimensional coordinate system is established in the spatial grid map, and all inspection points have corresponding coordinates. The distance between all inspection points can be obtained based on the coordinate conversion. This spatial grid map is synchronized to the background monitoring room in real time through the network, and the staff can understand the location of the drone and the equipment it inspects in real time.

[0031] In the spatial grid map, the inspection point is the action point where the drone monitors the inspection equipment in the substation. When a certain equipment needs to be inspected during the inspection task, the drone flies to the inspection point according to the set route to take pictures at different angles. The position coordinates of the inspection point in the spatial grid map and the relevant parameters pre-set when the drone inspects the equipment at the inspection point are pre-set through data information; due to the different importance of different equipment in the inspection task, the formulated inspection plans are also different, which may lead to different inspection cycles for the equipment. When the relevant information of the outdoor substation inspection work changes, it is necessary to modify the relevant parameters and update the spatial grid map and its data.

[0032] In order to verify the effectiveness of this embodiment, the actual inspection area of ​​the substation (1.5km×1.5km) is used as the experimental scene, and a grid environment is constructed at a scale of 1:20, where the black area represents the obstacle area and the no-fly zone, and the white area is the allowed passage area, such as Figure 3 shown.

[0033] Substation inspection probability risk map generated based on comprehensive assessment models with different risk values, such as Figure 4 In the experiment, the gridding method was used to divide the low-altitude inspection environment of the substation, and the equipment obstacle layer and the environmental shielding layer were combined to generate the final low-altitude safety risk map by superimposing the risk values ​​at different locations. The experimental results show that as the drone approaches the obstacle, the risk probability increases significantly, which verifies the effectiveness of the risk map in quantifying the flight risk of drones.

[0034] The inspection path of the drone was optimized by combining the risk map and the inspection priority value. In the experiment, according to the risk distribution information provided by the risk map, the inspection points in the areas with lower risks were selected first, and the paths were planned for these preferred points; the coordinates and values ​​of the preferred points are shown in Table 3: Table 3: Inspection coordinate data; Serial number Horizontal axis Vertical axis Preferred value 1 0 0 0.005446 2 7 4 0.10676 3 10 5 0.210289 4 17 15 0.012831 5 11 12 0.04093 6 10 15 0.082085 7 9 19 0.024365 8 3 17 0.053322 9 7 9 0.070616 10 2 9 0.157312 The UAV path planning results are as follows: Figure 5 As shown; according to Table 3, the content and Figure 5 The path planning experimental results show that the path planning method combined with the risk map can effectively reduce the collision risk of drones during the inspection process and improve the safety and reliability of the inspection mission.

Claims

1. A drone inspection method considering substation spatial point optimization and safety risk map, characterized in that: The following steps are involved: S1, uses drones equipped with laser radar for navigation positioning and obstacle detection, digitizes actual maps and calibrates inspection point coordinates through grid map generation method, and synchronizes to the background in real time; formulates differentiated inspection plans based on the importance of equipment and updates parameters and grid map data; S2, adopts a plan of multiple short-time inspections, splits the tasks according to the distance between the inspection points, and automatically updates the inspection order according to the equipment priority value; the equipment priority value is divided based on the importance of the equipment, historical faults, inspection frequency and signal strength; S3, using the security risk map grid division and calculation generation method, by integrating the environmental shielding and equipment obstacle risk layers, comprehensively evaluates and generates the final substation inspection probability risk map to quantify the potential risks of drones to ground equipment; S4, conducts drone inspections based on the generated inspection points, inspection plans, and risk maps, including constructing a three-dimensional coordinate system, designing multiple inspection plans, calculating inspection priority values, selecting inspection points, generating two-dimensional grid maps and risk layers, comprehensively evaluating risk values, and forming a grid map with superimposed optimized points and safety risk values. Finally, the optimal inspection path is generated through a jump point search algorithm and the inspection task is executed.

2. The drone inspection method considering substation spatial point optimization and safety risk map according to claim 1 is characterized in that: The specific process of step S1 includes: Use drones equipped with laser radar equipment to conduct navigation positioning and obstacle detection in the station inspection environment; After collecting data, the grid map generation method is used to digitize the actual map to construct a spatial grid map of the outdoor substation; In the spatial grid diagram, the positional relationship between the spatial grid and the inspection points is clarified, a three-dimensional coordinate system is established to determine the coordinates and distances of the inspection points, and these are synchronized to the backend monitoring room in real time; When the relevant information of the inspection equipment, including location and status, changes, the grid map and data are updated in real time.

3. The method for unmanned aerial vehicle inspection considering substation spatial point optimization and safety risk map according to claim 2 is characterized in that: The specific process of step S2 includes: S201, the inspection task is disassembled according to the distance of the inspection point. After completing an inspection task, the drone automatically returns to the machine nest to charge, and inspects again according to the decomposed task after being fully charged; after receiving the inspection signal, the inspection priority value is calculated and updated to the inspection equipment point of the grid map, and whether the equipment needs to be inspected is determined according to the priority value; S202, the substation files the inspection equipment and records each inspection, builds a database of equipment historical faults, counts the number of faults of each device in different seven-day periods within a month, and divides the level according to the corresponding relationship between the number of faults and the equipment historical fault index, and calculates the equipment historical fault index of each device Q ; S203, calculate the inspection frequency: ； In the formula n The number of times the device is inspected in a week, and the statistical data is mapped to the device inspection frequency index F To reduce statistical complexity; S204, divide the importance of equipment in outdoor substation into 6 levels, using the importance index of the equipment itself W express; S205, set signal value T Determine the drone inspection mission, when the signal reaches the threshold standard T The value is 1, when the signal does not reach the threshold standard T The value is 0; S206, set inspection priority value: Priority value U The importance index of the device itself W , Equipment historical failure index Q , Equipment inspection frequency index F and signal value T The weighted product U : ； in, , , They are , , The weight coefficient of the index is set according to the inspection task requirements; U A value of 0 indicates that the device will not be inspected by drones; S207, update the inspection priority value to the inspection point of the spatial grid map, display the urgency through the color and size of the dots, and select the inspection point according to the priority value; consider the battery life of the UAV, set the maximum time for each inspection, accumulate the inspection time of the selected inspection point, and cancel the point selection when the inspection time of the next point exceeds the maximum value, leaving a margin for the UAV's flight time when selecting the point.

4. The drone inspection method considering substation spatial point optimization and safety risk map according to claim 3 is characterized in that: The specific process of step S3 includes: S301, projecting the spatial grid map onto the ground, gridding the entire substation low-altitude inspection airspace, and obtaining a two-dimensional grid map of the entire two-dimensional inspection ground layer environment, divided into The matrix : ； In the formula, each matrix element corresponds to the risk value of the cell location , discrete coordinates Geographical location The conversion relationship between them is: ； In the formula, is the geographic length represented by the unit coordinate, , ; S302, the security risk map is generated by combining the environmental shielding layer and the equipment obstacle layer. Through risk assessment, the shielding layer and the obstacle layer are quantitatively superimposed on the location-based map to generate the final security risk map: The environmental shielding layer calculates the shielding protection effect of buildings in the substation on the equipment. The shielding coefficient is defined as a real number [0,10], expressed as a matrix, and the risk value is calculated by the conversion relationship: ； The equipment obstacle layer is generated using the laser radar scanning data, the obstacle height is represented by a matrix, and the risk value is calculated using the same conversion relationship; S303, by integrating the two layers in the environmental shielding and equipment obstacle risk maps, based on the comprehensive assessment model of different risk values, a final substation inspection probability risk map is generated; the risk map element value is defined as: ； In the formula, , The weight coefficient of the potential risk of the geographical location of environmental shielding and equipment obstacles; The elements in the matrix representing the ambient occlusion layer, Represents the elements in the obstacle height matrix in the substation inspection environment.

5. The method for drone inspection considering substation spatial point optimization and safety risk map according to claim 4 is characterized in that: The specific process of step S4 includes: S401, constructing a three-dimensional coordinate system of the substation inspection space, including data of all inspection points, buildings and obstacles, forming a visual spatial grid map corresponding to the inspection point coordinates, and the distance between the inspection points can be converted according to the coordinates; S402, design a plan for multiple inspections in a short period of time, and divide the inspection tasks into tasks according to the distances between the inspection points; S403, select the importance index of the device itself W , Equipment historical failure index Q , Equipment inspection frequency index F and signal value T Calculate inspection priority value U ; S404, considering the limited battery life of the drone, selecting equipment inspection points in order of inspection priority values; S405, when step S401 is completed, the spatial grid map is projected onto the ground and gridded to obtain a two-dimensional grid map; S406, generating an environment shielding layer and a device obstacle layer respectively, and calculating the risk values ​​of the shielding layer and the obstacle layer; S407, generating a final substation inspection probability risk map by integrating the different risk values ​​of the two layers for comprehensive evaluation, and calculating the layer grid risk value; S408, after completing step S404 and step S407, a substation inspection grid map with superimposed optimization points and safety risk values ​​is formed; S409, generating an optimal path for the drone inspection based on a jump point search algorithm, and obtaining coordinate points and a traversal order; S410, generating an execution plan for the UAV inspection task and executing the inspection work.

Images